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Sabanés Zariquiey F, Galvelis R, Gallicchio E, Chodera JD, Markland TE, De Fabritiis G. Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. J Chem Inf Model 2024; 64:1481-1485. [PMID: 38376463 DOI: 10.1021/acs.jcim.3c02031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.
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Affiliation(s)
- Francesc Sabanés Zariquiey
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Raimondas Galvelis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Emilio Gallicchio
- Department of Chemistry, Graduate Center, Brooklyn College, City University of New York, New York, New York 11210, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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2
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Zariquiey FS, Galvelis R, Gallicchio E, Chodera JD, Markland TE, de Fabritiis G. Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials. ArXiv 2024:arXiv:2401.16062v2. [PMID: 38351937 PMCID: PMC10862936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the Alchemical Transfer Method (ATM) and validate its performance against established benchmarks and find significant enhancements compared to conventional MM force fields like GAFF2.
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3
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Correction to Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2024; 20:990-991. [PMID: 38164914 DOI: 10.1021/acs.jctc.3c01298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
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Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MM, Hu F, Huang J, Krämer A, Michel J, Mitchell JA, Pande VS, Rodrigues JPGLM, Rodriguez-Guerra J, Simmonett AC, Singh S, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, De Fabritiis G, Markland TE. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. J Phys Chem B 2024; 128:109-116. [PMID: 38154096 PMCID: PMC10846090 DOI: 10.1021/acs.jpcb.3c06662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Raimondas Galvelis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Raúl P. Peláez
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Charlles R. A. Abreu
- Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil
- Redesign Science Inc., 180 Varick St., New York, NY 10014, USA
| | - Stephen E. Farr
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, NY, USA
- Ph.D. Program in Chemistry and Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY, USA
| | - Anton Gorenko
- Stream HPC, Koningin Wilhelminaplein 1 - 40601, 1062 HG Amsterdam, Netherlands
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Frank Hu
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Andreas Krämer
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Joshua A. Mitchell
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, CA 95616, USA
| | - Vijay S. Pande
- Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, CA 94025, USA
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - João PGLM Rodrigues
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - Jaime Rodriguez-Guerra
- Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany
| | - Andrew C. Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Jason Swails
- Entos Inc., 9310 Athena Circle, La Jolla, CA 92037, USA
| | - Philip Turner
- College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Yuanqing Wang
- Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, NY 10004, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Gianni De Fabritiis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
- ICREA, Passeig Lluis Companys 23, 08010, Barcelona, Spain
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5
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Outhwaite IR, Singh S, Berger BT, Knapp S, Chodera JD, Seeliger MA. Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting. eLife 2023; 12:e86189. [PMID: 38047771 PMCID: PMC10769483 DOI: 10.7554/elife.86189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 12/03/2023] [Indexed: 12/05/2023] Open
Abstract
Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.
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Affiliation(s)
- Ian R Outhwaite
- Department of Pharmacological Sciences, Stony Brook UniversityStony BrookUnited States
| | - Sukrit Singh
- Department of Pharmacological Sciences, Stony Brook UniversityStony BrookUnited States
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew YorkUnited States
| | - Benedict-Tilman Berger
- Institute of Pharmaceutical Chemistry, Goethe University FrankfurtFrankfurt am MainGermany
- Structural Genomics Consortium, Buchmann Institute for Life Sciences, Goethe University FrankfurtFrankfurt am MainGermany
| | - Stefan Knapp
- Institute of Pharmaceutical Chemistry, Goethe University FrankfurtFrankfurt am MainGermany
- Structural Genomics Consortium, Buchmann Institute for Life Sciences, Goethe University FrankfurtFrankfurt am MainGermany
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew YorkUnited States
| | - Markus A Seeliger
- Department of Pharmacological Sciences, Stony Brook UniversityStony BrookUnited States
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6
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Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MM, Hu F, Huang J, Krämer A, Michel J, Mitchell JA, Pande VS, Rodrigues JP, Rodriguez-Guerra J, Simmonett AC, Singh S, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, Fabritiis GD, Markland TE. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. ArXiv 2023:arXiv:2310.03121v2. [PMID: 37986730 PMCID: PMC10659447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.
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7
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Boby ML, Fearon D, Ferla M, Filep M, Koekemoer L, Robinson MC, Chodera JD, Lee AA, London N, von Delft A, von Delft F. Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors. Science 2023; 382:eabo7201. [PMID: 37943932 PMCID: PMC7615835 DOI: 10.1126/science.abo7201] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 10/09/2023] [Indexed: 11/12/2023]
Abstract
We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.
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Affiliation(s)
- Melissa L. Boby
- Pharmacology Graduate Program, Weill Cornell Graduate School of Medical Sciences, New York, NY 10065, USA
- Program in Chemical Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Program in Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, OX11 0QX, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, United Kingdom
| | - Matteo Ferla
- Oxford Biomedical Research Centre, National Institute for Health Research, University of Oxford, Oxford, UK
| | - Mihajlo Filep
- Department of Chemical and Structural Biology, The Weizmann Institute of Science, Rehovot, Israel, 7610001
| | - Lizbé Koekemoer
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - The COVID Moonshot Consortium
- Pharmacology Graduate Program, Weill Cornell Graduate School of Medical Sciences, New York, NY 10065, USA
- Program in Chemical Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Program in Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, OX11 0QX, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, United Kingdom
- Oxford Biomedical Research Centre, National Institute for Health Research, University of Oxford, Oxford, UK
- Department of Chemical and Structural Biology, The Weizmann Institute of Science, Rehovot, Israel, 7610001
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- PostEra Inc., 1 Broadway, 14th Floor,Cambridge, MA 02142, USA
- Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - John D. Chodera
- Program in Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alpha A Lee
- PostEra Inc., 1 Broadway, 14th Floor,Cambridge, MA 02142, USA
| | - Nir London
- Department of Chemical and Structural Biology, The Weizmann Institute of Science, Rehovot, Israel, 7610001
| | - Annette von Delft
- Oxford Biomedical Research Centre, National Institute for Health Research, University of Oxford, Oxford, UK
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Frank von Delft
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, OX11 0QX, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, United Kingdom
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
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8
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Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland TE, Chodera JD, De Fabritiis G. NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. J Chem Inf Model 2023; 63:5701-5708. [PMID: 37694852 PMCID: PMC10577237 DOI: 10.1021/acs.jcim.3c00773] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines a neural network potential (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by ∼5 times and achieve a combined sampling of 1 μs for each complex, marking the longest simulations ever reported for this class of simulations.
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Affiliation(s)
- Raimondas Galvelis
- Acellera Labs, C/Doctor Trueta 183, Barcelona 08005, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Doctor Aiguader 88, Barcelona 08003, Spain
| | - Alejandro Varela-Rial
- Acellera Ltd, Devonshire House 582 Honeypot Lane, Stanmore Middlesex, HA7 1JS, United Kingdom
| | - Stefan Doerr
- Acellera Ltd, Devonshire House 582 Honeypot Lane, Stanmore Middlesex, HA7 1JS, United Kingdom
| | - Roberto Fino
- Acellera Labs, C/Doctor Trueta 183, Barcelona 08005, Spain
| | - Peter Eastman
- Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Doctor Aiguader 88, Barcelona 08003, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain
- Acellera Ltd, Devonshire House 582 Honeypot Lane, Stanmore Middlesex, HA7 1JS, United Kingdom
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9
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Schaller D, Christ CD, Chodera JD, Volkamer A. Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. bioRxiv 2023:2023.09.11.557138. [PMID: 37745489 PMCID: PMC10515787 DOI: 10.1101/2023.09.11.557138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
In recent years machine learning has transformed many aspects of the drug discovery process including small molecule design for which the prediction of the bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches, but is fundamentally limited by the accuracy with which protein:ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase:inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario. The assembled benchmark data set focuses on the well-studied protein kinase family and comprises a subset of 589 protein structures co-crystallized with 423 ATP-competitive ligands. We find that the docking methods biased by the co-crystallized ligand-utilizing shape overlap with or without maximum common substructure matching-are more successful in recovering binding poses than standard physics-based docking alone. Also, docking into multiple structures significantly increases the chance to generate a low RMSD docking pose. Docking utilizing an approach that combines all three methods (Posit) into structures with the most similar co-crystallized ligands according to shape and electrostatics proofed to be the most efficient way to reproduce binding poses achieving a success rate of 66.9 % across all included systems. The studied docking and pose selection strategies-which utilize the OpenEye Toolkit-were implemented into pipelines of the KinoML framework allowing automated and reliable protein:ligand complex generation for future downstream machine learning tasks. Although focused on protein kinases, we believe the general findings can also be transferred to other protein families.
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Affiliation(s)
- David Schaller
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Clara D. Christ
- Molecular Design, Research and Development, Pharmaceuticals, Bayer AG, 13342 Berlin, Germany
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
- Data Driven Drug Design, Faculty of Mathematics and Computer Sciences, Saarland University, Saarbrücken, Germany
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10
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2023; 19:4863-4882. [PMID: 37450482 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States
| | - Dominic A Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Michael M Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Laura E Rosen
- Vir Biotechnology, San Francisco, California 94158, United States
| | - Kevin Hauser
- Vir Biotechnology, San Francisco, California 94158, United States
| | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
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11
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Boothroyd S, Behara PK, Madin OC, Hahn DF, Jang H, Gapsys V, Wagner JR, Horton JT, Dotson DL, Thompson MW, Maat J, Gokey T, Wang LP, Cole DJ, Gilson MK, Chodera JD, Bayly CI, Shirts MR, Mobley DL. Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field. J Chem Theory Comput 2023; 19:3251-3275. [PMID: 37167319 PMCID: PMC10269353 DOI: 10.1021/acs.jctc.3c00039] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Indexed: 05/13/2023]
Abstract
We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.
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Affiliation(s)
| | - Pavan Kumar Behara
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California 92697, United States
| | - Owen C. Madin
- Chemical
& Biological Engineering Department, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - David F. Hahn
- Computational
Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Hyesu Jang
- Chemistry
Department, The University of California
at Davis, Davis, California 95616, United States
- OpenEye
Scientific Software, Santa
Fe, New Mexico 87508, United States
| | - Vytautas Gapsys
- Computational
Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
- Computational
Biomolecular Dynamics Group, Department of Theoretical and Computational
Biophysics, Max Planck Institute for Multidisciplinary
Sciences, Am Fassberg 11, D-37077, Göttingen, Germany
| | - Jeffrey R. Wagner
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California 92697, United States
- The Open
Force Field Initiative, Open Molecular Software
Foundation, Davis, California 95616, United States
| | - Joshua T. Horton
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, U.K.
| | - David L. Dotson
- The Open
Force Field Initiative, Open Molecular Software
Foundation, Davis, California 95616, United States
- Datryllic LLC, Phoenix, Arizona 85003, United
States
| | - Matthew W. Thompson
- Chemical
& Biological Engineering Department, University of Colorado Boulder, Boulder, Colorado 80309, United States
- The Open
Force Field Initiative, Open Molecular Software
Foundation, Davis, California 95616, United States
| | - Jessica Maat
- Department
of Chemistry, University of California, Irvine, California 92697, United States
| | - Trevor Gokey
- Department
of Chemistry, University of California, Irvine, California 92697, United States
| | - Lee-Ping Wang
- Chemistry
Department, The University of California
at Davis, Davis, California 95616, United States
| | - Daniel J. Cole
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, U.K.
| | - Michael K. Gilson
- Skaggs
School of Pharmacy and Pharmaceutical Sciences, The University of California at San Diego, La Jolla, California 92093, United States
| | - John D. Chodera
- Computational
& Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | | | - Michael R. Shirts
- Chemical
& Biological Engineering Department, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - David L. Mobley
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California 92697, United States
- Department
of Chemistry, University of California, Irvine, California 92697, United States
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12
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex. bioRxiv 2023:2023.03.07.530278. [PMID: 36945557 PMCID: PMC10028896 DOI: 10.1101/2023.03.07.530278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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13
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Perner F, Stein EM, Wenge DV, Singh S, Kim J, Apazidis A, Rahnamoun H, Anand D, Marinaccio C, Hatton C, Wen Y, Stone RM, Schaller D, Mowla S, Xiao W, Gamlen HA, Stonestrom AJ, Persaud S, Ener E, Cutler JA, Doench JG, McGeehan GM, Volkamer A, Chodera JD, Nowak RP, Fischer ES, Levine RL, Armstrong SA, Cai SF. MEN1 mutations mediate clinical resistance to menin inhibition. Nature 2023; 615:913-919. [PMID: 36922589 PMCID: PMC10157896 DOI: 10.1038/s41586-023-05755-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 01/24/2023] [Indexed: 03/17/2023]
Abstract
Chromatin-binding proteins are critical regulators of cell state in haematopoiesis1,2. Acute leukaemias driven by rearrangement of the mixed lineage leukaemia 1 gene (KMT2Ar) or mutation of the nucleophosmin gene (NPM1) require the chromatin adapter protein menin, encoded by the MEN1 gene, to sustain aberrant leukaemogenic gene expression programs3-5. In a phase 1 first-in-human clinical trial, the menin inhibitor revumenib, which is designed to disrupt the menin-MLL1 interaction, induced clinical responses in patients with leukaemia with KMT2Ar or mutated NPM1 (ref. 6). Here we identified somatic mutations in MEN1 at the revumenib-menin interface in patients with acquired resistance to menin inhibition. Consistent with the genetic data in patients, inhibitor-menin interface mutations represent a conserved mechanism of therapeutic resistance in xenograft models and in an unbiased base-editor screen. These mutants attenuate drug-target binding by generating structural perturbations that impact small-molecule binding but not the interaction with the natural ligand MLL1, and prevent inhibitor-induced eviction of menin and MLL1 from chromatin. To our knowledge, this study is the first to demonstrate that a chromatin-targeting therapeutic drug exerts sufficient selection pressure in patients to drive the evolution of escape mutants that lead to sustained chromatin occupancy, suggesting a common mechanism of therapeutic resistance.
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Affiliation(s)
- Florian Perner
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Internal Medicine C, University Medicine Greifswald, Greifswald, Germany
| | - Eytan M Stein
- Leukemia Service, Department of Medicine, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniela V Wenge
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sukrit Singh
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeonghyeon Kim
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Athina Apazidis
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Homa Rahnamoun
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Disha Anand
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Internal Medicine C, University Medicine Greifswald, Greifswald, Germany
| | - Christian Marinaccio
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Charlie Hatton
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yanhe Wen
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard M Stone
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - David Schaller
- In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Shoron Mowla
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wenbin Xiao
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Holly A Gamlen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Aaron J Stonestrom
- Leukemia Service, Department of Medicine, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sonali Persaud
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elizabeth Ener
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jevon A Cutler
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - John G Doench
- Genetic Perturbation Platform, Broad Institute, Cambridge, MA, USA
| | | | - Andrea Volkamer
- In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - John D Chodera
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Radosław P Nowak
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Eric S Fischer
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Ross L Levine
- Leukemia Service, Department of Medicine, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Scott A Armstrong
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Sheng F Cai
- Leukemia Service, Department of Medicine, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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14
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Cavender CE, Behara PK, Boothroyd S, Dotson DL, Horton JT, Mitchell JA, Pulido I, Thompson MW, Wagner J, Wang L, Chodera JD, Cole DJ, Mobley DL, Shirts MR, Gilson MK. Development and benchmarking of an open, self-consistent force field for proteins and small molecules from the open force field initiative. Biophys J 2023; 122:421a. [PMID: 36784152 DOI: 10.1016/j.bpj.2022.11.2280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Affiliation(s)
- Chapin E Cavender
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Pavan K Behara
- Department of Pharmaceutical Sciences, University of California Irvine, Irvine, CA, USA
| | - Simon Boothroyd
- Open Force Field Consortium, Open Molecular Software Foundation, Davis, CA, USA; Boothroyd Scientific Consulting Ltd, London, United Kingdom
| | - David L Dotson
- Open Force Field Consortium, Open Molecular Software Foundation, Davis, CA, USA; Datryllic LLC, Phoenix, AZ, USA
| | - Joshua T Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Joshua A Mitchell
- Open Force Field Consortium, Open Molecular Software Foundation, Davis, CA, USA
| | - Ivan Pulido
- Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew W Thompson
- Open Force Field Consortium, Open Molecular Software Foundation, Davis, CA, USA
| | - Jeffrey Wagner
- Open Force Field Consortium, Open Molecular Software Foundation, Davis, CA, USA
| | - Lily Wang
- Open Force Field Consortium, Open Molecular Software Foundation, Davis, CA, USA
| | - John D Chodera
- Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California Irvine, Irvine, CA, USA
| | - Michael R Shirts
- Chemical & Biological Engineering, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
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15
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Outhwaite IR, Singh S, Berger BT, Knapp S, Chodera JD, Seeliger MA. Death by a Thousand Cuts â€" Combining Kinase Inhibitors for Selective Target Inhibition and Rational Polypharmacology. bioRxiv 2023:2023.01.13.523972. [PMID: 36711619 PMCID: PMC9882273 DOI: 10.1101/2023.01.13.523972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.
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16
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Eastman P, Behara PK, Dotson DL, Galvelis R, Herr JE, Horton JT, Mao Y, Chodera JD, Pritchard BP, Wang Y, De Fabritiis G, Markland TE. SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Sci Data 2023; 10:11. [PMID: 36599873 PMCID: PMC9813265 DOI: 10.1038/s41597-022-01882-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023] Open
Abstract
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA.
| | - Pavan Kumar Behara
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - David L Dotson
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, CA, 95616, USA
| | | | - John E Herr
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Josh T Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
| | - Yuezhi Mao
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Benjamin P Pritchard
- Molecular Sciences Software Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Yuanqing Wang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Graduate School of Medical Sciences, New York, NY, 10065, USA
| | - Gianni De Fabritiis
- Acellera Labs, Doctor Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003, Barcelona, Spain and ICREA, Passeig Lluis Companys 23, 08010, Barcelona, Spain
| | - Thomas E Markland
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA
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17
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Horton J, Boothroyd S, Wagner J, Mitchell JA, Gokey T, Dotson DL, Behara PK, Ramaswamy VK, Mackey M, Chodera JD, Anwar J, Mobley DL, Cole DJ. Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale. J Chem Inf Model 2022; 62:5622-5633. [PMID: 36351167 PMCID: PMC9709916 DOI: 10.1021/acs.jcim.2c01153] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The development of accurate transferable force fields is key to realizing the full potential of atomistic modeling in the study of biological processes such as protein-ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to yield accuracy improvements. However, force field torsion parameters, which must account for many stereoelectronic and steric effects, are considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for druglike fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root-mean-square error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.560.390.77 to 0.420.280.59 kcal/mol and R2 correlation improved from 0.720.350.87 to 0.930.840.97).
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Affiliation(s)
- Joshua
T. Horton
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon TyneNE1 7RU, United
Kingdom
| | - Simon Boothroyd
- Boothroyd
Scientific Consulting Ltd., 71-75 Shelton Street, LondonWC2H 9JQ, Greater London, United Kingdom
| | - Jeffrey Wagner
- The
Open Force Field Initiative, Open Molecular
Software Foundation, Davis, California95616, United States
| | - Joshua A. Mitchell
- The
Open Force Field Initiative, Open Molecular
Software Foundation, Davis, California95616, United States
| | - Trevor Gokey
- Department
of Chemistry, University of California, Irvine, California92697, United States
| | - David L. Dotson
- The
Open Force Field Initiative, Open Molecular
Software Foundation, Davis, California95616, United States
| | - Pavan Kumar Behara
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California92697, United States
| | | | - Mark Mackey
- Cresset, New Cambridge House, Bassingbourn
Road, LitlingtonSG8 0SS, Cambridgeshire, United Kingdom
| | - John D. Chodera
- Computational
& Systems Biology Program, Sloan Kettering
Institute, Memorial Sloan Kettering Cancer Center, New
York, New York10065, United States
| | - Jamshed Anwar
- Department
of Chemistry, Lancaster University, LancasterLA1 4YW, United Kingdom
| | - David L. Mobley
- Department
of Chemistry, University of California, Irvine, California92697, United States,Department
of Pharmaceutical Sciences, University of
California, Irvine, California92697, United States
| | - Daniel J. Cole
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon TyneNE1 7RU, United
Kingdom,
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18
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Wang Y, Fass J, Kaminow B, Herr JE, Rufa D, Zhang I, Pulido I, Henry M, Bruce Macdonald HE, Takaba K, Chodera JD. End-to-end differentiable construction of molecular mechanics force fields. Chem Sci 2022; 13:12016-12033. [PMID: 36349096 PMCID: PMC9600499 DOI: 10.1039/d2sc02739a] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/05/2022] [Indexed: 01/07/2023] Open
Abstract
Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma.
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Affiliation(s)
- Yuanqing Wang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA,Physiology, Biophysics and System Biology PhD Program, Weill Cornell Medical College, Cornell UniversityNew York 10065NYUSA,MFA Program in Creative Writing, Division of Humanities and Arts, City College of New York, City University of New YorkNew York 10031NYUSA
| | - Josh Fass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA,Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell UniversityNew York 10065NYUSA
| | - Benjamin Kaminow
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA,Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell UniversityNew York 10065NYUSA
| | - John E. Herr
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA
| | - Dominic Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA,Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell UniversityNew York 10065NYUSA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA,Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell UniversityNew York 10065NYUSA
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA
| | - Mike Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA
| | - Kenichiro Takaba
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA,Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma CorporationShizuoka 410-2321Japan
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterNew York 10065NYUSA
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19
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Hahn DF, Bayly CI, Boby ML, Macdonald HEB, Chodera JD, Gapsys V, Mey ASJS, Mobley DL, Benito LP, Schindler CEM, Tresadern G, Warren GL. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]. Living J Comput Mol Sci 2022; 4:1497. [PMID: 36382113 PMCID: PMC9662604 DOI: 10.33011/livecoms.4.1.1497] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.
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Affiliation(s)
- David F. Hahn
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Melissa L. Boby
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- MSD R&D Innovation Centre, 120 Moorgate, London EC2M 6UR, United Kingdom
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, CA USA
| | - Laura Perez Benito
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Gary Tresadern
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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20
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Boothroyd S, Madin OC, Mobley DL, Wang LP, Chodera JD, Shirts MR. Improving Force Field Accuracy by Training against Condensed-Phase Mixture Properties. J Chem Theory Comput 2022; 18:3577-3592. [PMID: 35533269 DOI: 10.1021/acs.jctc.1c01268] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulations as a route to gaining a fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we retrain a select number of Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems and assess the performance of each of these combinations. We show that retraining against the mixture data improves the force field's ability to reproduce mixture properties, including solvation free energies, correcting some systematic errors that exist when training vdW interactions against properties of pure systems only.
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Affiliation(s)
- Simon Boothroyd
- Boothroyd Scientific Consulting Ltd., 71-75 Shelton Street, London WC2H 9JQ, Greater London, U.K
| | - Owen C Madin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - David L Mobley
- Department of Chemistry, University of California, Irvine, California 92617, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
| | - Lee-Ping Wang
- Department of Chemistry, University of California, Davis, California 95616, United States
| | - John D Chodera
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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21
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Boothroyd S, Wang LP, Mobley DL, Chodera JD, Shirts MR. Open Force Field Evaluator: An Automated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation. J Chem Theory Comput 2022; 18:3566-3576. [PMID: 35507313 DOI: 10.1021/acs.jctc.1c01111] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Developing accurate classical force field representations of molecules is key to realizing the full potential of molecular simulations, both as a powerful route to gaining fundamental insights into a broad spectrum of chemical and biological phenomena and for predicting physicochemical and mechanical properties of substances. The Open Force Field Consortium is an industry-funded open science effort to this end, developing open-source tools for rapidly generating new high-quality small-molecule force fields. An integral aspect of this is the parameterization and assessment of force fields against high-quality, condensed-phase physical property data, curated from open data sources such as the NIST ThermoML Archive, alongside quantum chemical data. The quantity of such experimental data in open data archives alone would require an onerous amount of human and computational resources to both curate and estimate manually, especially when estimations must be obtained for numerous sets of force field parameters. Here, we present an entirely automated, highly scalable framework for evaluating physical properties and their gradients in terms of force field parameters. It is written as a modular and extensible Python framework, which employs an intelligent multiscale estimation approach that allows for the automated estimation of properties from simulation and cached simulation data, and a pluggable API for estimation of new properties. In this study, we demonstrate the utility of the framework by benchmarking the OpenFF 1.0.0 small-molecule force field and GAFF 1.8 and GAFF 2.1 force fields against a test set of binary density and enthalpy of mixing measurements curated using the framework utilities. Further, we demonstrate the framework's utility as part of force field optimization by using it alongside ForceBalance, a framework for systematic force field optimization, to retrain a set of nonbonded van der Waals parameters against a training set of density and enthalpy of vaporization measurements.
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Affiliation(s)
- Simon Boothroyd
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.,Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Lee-Ping Wang
- Department of Chemistry, The University of California at Davis, Davis, California 95616, United States
| | - David L Mobley
- Departments of Pharmaceutical Sciences and Chemistry, The University of California at Irvine, Irvine, California 92617, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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22
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Madin OC, Boothroyd S, Messerly RA, Fass J, Chodera JD, Shirts MR. Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models. J Chem Inf Model 2022; 62:874-889. [PMID: 35129974 PMCID: PMC9217127 DOI: 10.1021/acs.jcim.1c00829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A high level of physical detail in a molecular model improves its ability to perform high accuracy simulations but can also significantly affect its complexity and computational cost. In some situations, it is worthwhile to add complexity to a model to capture properties of interest; in others, additional complexity is unnecessary and can make simulations computationally infeasible. In this work, we demonstrate the use of Bayesian inference for molecular model selection, using Monte Carlo sampling techniques accelerated with surrogate modeling to evaluate the Bayes factor evidence for different levels of complexity in the two-centered Lennard-Jones + quadrupole (2CLJQ) fluid model. Examining three nested levels of model complexity, we demonstrate that the use of variable quadrupole and bond length parameters in this model framework is justified only for some chemistries. Through this process, we also get detailed information about the distributions and correlation of parameter values, enabling improved parametrization and parameter analysis. We also show how the choice of parameter priors, which encode previous model knowledge, can have substantial effects on the selection of models, penalizing careless introduction of additional complexity. We detail the computational techniques used in this analysis, providing a roadmap for future applications of molecular model selection via Bayesian inference and surrogate modeling.
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Affiliation(s)
- Owen C. Madin
- Department of Chemical & Biological Engineering, University of Colorado Boulder, Boulder, CO 80309
| | - Simon Boothroyd
- Boothroyd Scientific Consulting Ltd., 71-75 Shelton Street, London, Greater London, United Kingdom, WC2H 9JQ
| | | | - Josh Fass
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Department of Chemical & Biological Engineering, University of Colorado Boulder, Boulder, CO 80309
| | - Michael R. Shirts
- Department of Chemical & Biological Engineering, University of Colorado Boulder, Boulder, CO 80309
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23
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Cavender CE, Behara PK, Boothroyd S, Dotson DL, Pulido IJ, Thompson MW, Wagner J, Wang L, Chodera JD, Mobley DL, Shirts MR, Gilson MK. Building an open, self-consistent force field for biopolymers and small molecules with the open force field initiative infrastructure. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.1367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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24
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Li Q, Jiang B, Guo J, Shao H, Del Priore IS, Chang Q, Kudo R, Li Z, Razavi P, Liu B, Boghossian AS, Rees MG, Ronan MM, Roth JA, Donovan KA, Palafox M, Reis-Filho JS, de Stanchina E, Fischer ES, Rosen N, Serra V, Koff A, Chodera JD, Gray NS, Chandarlapaty S. INK4 Tumor Suppressor Proteins Mediate Resistance to CDK4/6 Kinase Inhibitors. Cancer Discov 2022; 12:356-371. [PMID: 34544752 PMCID: PMC8831444 DOI: 10.1158/2159-8290.cd-20-1726] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 07/13/2021] [Accepted: 09/15/2021] [Indexed: 01/22/2023]
Abstract
Cyclin-dependent kinases 4 and 6 (CDK4/6) represent a major therapeutic vulnerability for breast cancer. The kinases are clinically targeted via ATP competitive inhibitors (CDK4/6i); however, drug resistance commonly emerges over time. To understand CDK4/6i resistance, we surveyed over 1,300 breast cancers and identified several genetic alterations (e.g., FAT1, PTEN, or ARID1A loss) converging on upregulation of CDK6. Mechanistically, we demonstrate CDK6 causes resistance by inducing and binding CDK inhibitor INK4 proteins (e.g., p18INK4C). In vitro binding and kinase assays together with physical modeling reveal that the p18INK4C-cyclin D-CDK6 complex occludes CDK4/6i binding while only weakly suppressing ATP binding. Suppression of INK4 expression or its binding to CDK6 restores CDK4/6i sensitivity. To overcome this constraint, we developed bifunctional degraders conjugating palbociclib with E3 ligands. Two resulting lead compounds potently degraded CDK4/6, leading to substantial antitumor effects in vivo, demonstrating the promising therapeutic potential for retargeting CDK4/6 despite CDK4/6i resistance. SIGNIFICANCE: CDK4/6 kinase activation represents a common mechanism by which oncogenic signaling induces proliferation and is potentially targetable by ATP competitive inhibitors. We identify a CDK6-INK4 complex that is resilient to current-generation inhibitors and develop a new strategy for more effective inhibition of CDK4/6 kinases.This article is highlighted in the In This Issue feature, p. 275.
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Affiliation(s)
- Qing Li
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Baishan Jiang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts
| | - Jiaye Guo
- Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Hong Shao
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Isabella S Del Priore
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Qing Chang
- Anti-Tumor Assessment, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Rei Kudo
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Zhiqiang Li
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pedram Razavi
- Breast Medicine Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell Medical College, New York, New York
| | - Bo Liu
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Matthew G Rees
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Melissa M Ronan
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Jennifer A Roth
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Katherine A Donovan
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts
| | - Marta Palafox
- Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - Jorge S Reis-Filho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Elisa de Stanchina
- Anti-Tumor Assessment, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Eric S Fischer
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts
| | - Neal Rosen
- Program in Molecular Pharmacology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Violeta Serra
- Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - Andrew Koff
- Program in Molecular Biology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - John D Chodera
- Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nathanael S Gray
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts
| | - Sarat Chandarlapaty
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.
- Breast Medicine Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell Medical College, New York, New York
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25
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Tang CP, Clark O, Ferrarone JR, Campos C, Lalani AS, Chodera JD, Intlekofer AM, Elemento O, Mellinghoff IK. GCN2 kinase activation by ATP-competitive kinase inhibitors. Nat Chem Biol 2022; 18:207-215. [PMID: 34949839 PMCID: PMC9549920 DOI: 10.1038/s41589-021-00947-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/28/2021] [Indexed: 12/17/2022]
Abstract
Small-molecule kinase inhibitors represent a major group of cancer therapeutics, but tumor responses are often incomplete. To identify pathways that modulate kinase inhibitor response, we conducted a genome-wide knockout (KO) screen in glioblastoma cells treated with the pan-ErbB inhibitor neratinib. Loss of general control nonderepressible 2 (GCN2) kinase rendered cells resistant to neratinib, whereas depletion of the GADD34 phosphatase increased neratinib sensitivity. Loss of GCN2 conferred neratinib resistance by preventing binding and activation of GCN2 by neratinib. Several other Food and Drug Administration (FDA)-approved inhibitors, such erlotinib and sunitinib, also bound and activated GCN2. Our results highlight the utility of genome-wide functional screens to uncover novel mechanisms of drug action and document the role of the integrated stress response (ISR) in modulating the response to inhibitors of oncogenic kinases.
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Affiliation(s)
- Colin P Tang
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Pharmacology Program, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Owen Clark
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Carl Campos
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - John D Chodera
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew M Intlekofer
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Physics and Biophysics Program, Weill Cornell Medicine, New York, NY, USA
| | - Ingo K Mellinghoff
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Pharmacology Program, Weill Cornell Medicine, New York, NY, USA.
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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26
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Smith DGA, Lolinco AT, Glick ZL, Lee J, Alenaizan A, Barnes TA, Borca CH, Di Remigio R, Dotson DL, Ehlert S, Heide AG, Herbst MF, Hermann J, Hicks CB, Horton JT, Hurtado AG, Kraus P, Kruse H, Lee SJR, Misiewicz JP, Naden LN, Ramezanghorbani F, Scheurer M, Schriber JB, Simmonett AC, Steinmetzer J, Wagner JR, Ward L, Welborn M, Altarawy D, Anwar J, Chodera JD, Dreuw A, Kulik HJ, Liu F, Martínez TJ, Matthews DA, Schaefer HF, Šponer J, Turney JM, Wang LP, De Silva N, King RA, Stanton JF, Gordon MS, Windus TL, Sherrill CD, Burns LA. Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs. J Chem Phys 2021; 155:204801. [PMID: 34852489 DOI: 10.1063/5.0059356] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.
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Affiliation(s)
- Daniel G A Smith
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | | | - Zachary L Glick
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Jiyoung Lee
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Asem Alenaizan
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Taylor A Barnes
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | - Carlos H Borca
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Roberto Di Remigio
- Department of Chemistry, Centre for Theoretical and Computational Chemistry, UiT, The Arctic University of Norway, N-9037 Tromsø, Norway
| | - David L Dotson
- Open Force Field Initiative, University of Colorado Boulder, Boulder, Colorado 80309, USA
| | - Sebastian Ehlert
- Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie, Universität Bonn, Beringstraße 4, D-53115 Bonn, Germany
| | - Alexander G Heide
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Michael F Herbst
- Applied and Computational Mathematics, RWTH Aachen University, Schinkelstr. 2, 52062 Aachen, Germany
| | - Jan Hermann
- FU Berlin, Department of Mathematics and Computer Science, 14195 Berlin, Germany
| | - Colton B Hicks
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Joshua T Horton
- Department of Chemistry, Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - Adrian G Hurtado
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York 11794-5250, USA
| | - Peter Kraus
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth 6845, WA, Australia
| | - Holger Kruse
- Institute of Biophysics of the Czech Academy of Sciences, Královopolská 135, 612 65 Brno, Czech Republic
| | | | - Jonathon P Misiewicz
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Levi N Naden
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | | | - Maximilian Scheurer
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Jeffrey B Schriber
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Institutes of Health-National Heart, Lung and Blood Institute, Bethesda, Maryland 20892, USA
| | - Johannes Steinmetzer
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Jena, Germany
| | - Jeffrey R Wagner
- Open Force Field Initiative, University of Colorado Boulder, Boulder, Colorado 80309, USA
| | - Logan Ward
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Matthew Welborn
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | - Doaa Altarawy
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | - Jamshed Anwar
- Department of Chemistry, Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Andreas Dreuw
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Fang Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Todd J Martínez
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Devin A Matthews
- The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Henry F Schaefer
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Jiří Šponer
- Institute of Biophysics of the Czech Academy of Sciences, Královopolská 135, 612 65 Brno, Czech Republic
| | - Justin M Turney
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Lee-Ping Wang
- Department of Chemistry, University of California Davis, Davis, California 95616, USA
| | - Nuwan De Silva
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Rollin A King
- Department of Chemistry, Bethel University, St. Paul, Minnesota 55112, USA
| | - John F Stanton
- Quantum Theory Project, The University of Florida, 2328 New Physics Building, Gainesville, Florida 32611-8435, USA
| | - Mark S Gordon
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Theresa L Windus
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - C David Sherrill
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Lori A Burns
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
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27
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Bahr MN, Nandkeolyar A, Kenna JK, Nevins N, Da Vià L, Işık M, Chodera JD, Mobley DL. Automated high throughput pK a and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge. J Comput Aided Mol Des 2021; 35:1141-1155. [PMID: 34714468 DOI: 10.1007/s10822-021-00427-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 10/13/2021] [Indexed: 11/28/2022]
Abstract
The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pKa and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules.
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Affiliation(s)
- Matthew N Bahr
- Pharmaceutical Research and Development, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA.
| | - Aakankschit Nandkeolyar
- Pharmaceutical Research and Development, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA.,Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.,Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - John K Kenna
- Pharmaceutical Research and Development, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA
| | - Neysa Nevins
- Pharmaceutical Research and Development, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA
| | - Luigi Da Vià
- Pharmaceutical Research and Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
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28
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Qiu Y, Smith DGA, Boothroyd S, Jang H, Hahn DF, Wagner J, Bannan CC, Gokey T, Lim VT, Stern CD, Rizzi A, Tjanaka B, Tresadern G, Lucas X, Shirts MR, Gilson MK, Chodera JD, Bayly CI, Mobley DL, Wang LP. Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field. J Chem Theory Comput 2021; 17:6262-6280. [PMID: 34551262 PMCID: PMC8511297 DOI: 10.1021/acs.jctc.1c00571] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.
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Affiliation(s)
- Yudong Qiu
- Chemistry Department, The University of California at Davis, Davis, California 95616, United States
| | - Daniel G A Smith
- The Molecular Sciences Software Institute (MolSSI), Blacksburg, Virginia 24060, United States
| | - Simon Boothroyd
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Hyesu Jang
- Chemistry Department, The University of California at Davis, Davis, California 95616, United States
| | - David F Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Jeffrey Wagner
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Caitlin C Bannan
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences, The University of California at San Diego, La Jolla, California 92093, United States
| | - Trevor Gokey
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Victoria T Lim
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Chaya D Stern
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Andrea Rizzi
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York 10065, United States
| | - Bryon Tjanaka
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Xavier Lucas
- F. Hoffmann-La Roche AG, Basel 4070, Switzerland
| | - Michael R Shirts
- Chemical & Biological Engineering Department, The University of Colorado at Boulder, Boulder, Colorado 80309, United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, The University of California at San Diego, La Jolla, California 92093, United States
| | - John D Chodera
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | | | - David L Mobley
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Lee-Ping Wang
- Chemistry Department, The University of California at Davis, Davis, California 95616, United States
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29
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Starr TN, Czudnochowski N, Liu Z, Zatta F, Park YJ, Addetia A, Pinto D, Beltramello M, Hernandez P, Greaney AJ, Marzi R, Glass WG, Zhang I, Dingens AS, Bowen JE, Tortorici MA, Walls AC, Wojcechowskyj JA, De Marco A, Rosen LE, Zhou J, Montiel-Ruiz M, Kaiser H, Dillen JR, Tucker H, Bassi J, Silacci-Fregni C, Housley MP, di Iulio J, Lombardo G, Agostini M, Sprugasci N, Culap K, Jaconi S, Meury M, Dellota E, Abdelnabi R, Foo SYC, Cameroni E, Stumpf S, Croll TI, Nix JC, Havenar-Daughton C, Piccoli L, Benigni F, Neyts J, Telenti A, Lempp FA, Pizzuto MS, Chodera JD, Hebner CM, Virgin HW, Whelan SPJ, Veesler D, Corti D, Bloom JD, Snell G. SARS-CoV-2 RBD antibodies that maximize breadth and resistance to escape. Nature 2021; 597:97-102. [PMID: 34261126 PMCID: PMC9282883 DOI: 10.1038/s41586-021-03807-6] [Citation(s) in RCA: 296] [Impact Index Per Article: 98.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/06/2021] [Indexed: 02/07/2023]
Abstract
An ideal therapeutic anti-SARS-CoV-2 antibody would resist viral escape1-3, have activity against diverse sarbecoviruses4-7, and be highly protective through viral neutralization8-11 and effector functions12,13. Understanding how these properties relate to each other and vary across epitopes would aid the development of therapeutic antibodies and guide vaccine design. Here we comprehensively characterize escape, breadth and potency across a panel of SARS-CoV-2 antibodies targeting the receptor-binding domain (RBD). Despite a trade-off between in vitro neutralization potency and breadth of sarbecovirus binding, we identify neutralizing antibodies with exceptional sarbecovirus breadth and a corresponding resistance to SARS-CoV-2 escape. One of these antibodies, S2H97, binds with high affinity across all sarbecovirus clades to a cryptic epitope and prophylactically protects hamsters from viral challenge. Antibodies that target the angiotensin-converting enzyme 2 (ACE2) receptor-binding motif (RBM) typically have poor breadth and are readily escaped by mutations despite high neutralization potency. Nevertheless, we also characterize a potent RBM antibody (S2E128) with breadth across sarbecoviruses related to SARS-CoV-2 and a high barrier to viral escape. These data highlight principles underlying variation in escape, breadth and potency among antibodies that target the RBD, and identify epitopes and features to prioritize for therapeutic development against the current and potential future pandemics.
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MESH Headings
- Adult
- Aged
- Animals
- Antibodies, Monoclonal/chemistry
- Antibodies, Monoclonal/immunology
- Antibodies, Viral/chemistry
- Antibodies, Viral/immunology
- Antibody Affinity
- Broadly Neutralizing Antibodies/chemistry
- Broadly Neutralizing Antibodies/immunology
- COVID-19/immunology
- COVID-19/virology
- COVID-19 Vaccines/chemistry
- COVID-19 Vaccines/immunology
- Cell Line
- Cricetinae
- Cross Reactions/immunology
- Epitopes, B-Lymphocyte/chemistry
- Epitopes, B-Lymphocyte/genetics
- Epitopes, B-Lymphocyte/immunology
- Female
- Humans
- Immune Evasion/genetics
- Immune Evasion/immunology
- Male
- Mesocricetus
- Middle Aged
- Models, Molecular
- SARS-CoV-2/chemistry
- SARS-CoV-2/classification
- SARS-CoV-2/genetics
- SARS-CoV-2/immunology
- Spike Glycoprotein, Coronavirus/chemistry
- Spike Glycoprotein, Coronavirus/genetics
- Spike Glycoprotein, Coronavirus/immunology
- Vaccinology
- COVID-19 Drug Treatment
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Affiliation(s)
- Tyler N Starr
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Zhuoming Liu
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO, USA
| | - Fabrizia Zatta
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Young-Jun Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Amin Addetia
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Dora Pinto
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Martina Beltramello
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | | | - Allison J Greaney
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Roberta Marzi
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - William G Glass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA
| | - Adam S Dingens
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - John E Bowen
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | | | - Alexandra C Walls
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | | | - Anna De Marco
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | | | - Jiayi Zhou
- Vir Biotechnology, San Francisco, CA, USA
| | | | | | | | | | - Jessica Bassi
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | | | | | | | - Gloria Lombardo
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | | | - Nicole Sprugasci
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Katja Culap
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Stefano Jaconi
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | | | | | - Rana Abdelnabi
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, KU Leuven, Leuven, Belgium
| | - Shi-Yan Caroline Foo
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, KU Leuven, Leuven, Belgium
| | - Elisabetta Cameroni
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Spencer Stumpf
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO, USA
| | - Tristan I Croll
- Cambridge Institute for Medical Research, Department of Haematology, University of Cambridge, Cambridge, UK
| | - Jay C Nix
- Molecular Biology Consortium, Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Luca Piccoli
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Fabio Benigni
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Johan Neyts
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, KU Leuven, Leuven, Belgium
| | | | | | - Matteo S Pizzuto
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Herbert W Virgin
- Vir Biotechnology, San Francisco, CA, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sean P J Whelan
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO, USA
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Davide Corti
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland.
| | - Jesse D Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
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30
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Wieder M, Fass J, Chodera JD. Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution. Chem Sci 2021; 12:11364-11381. [PMID: 34567495 PMCID: PMC8409483 DOI: 10.1039/d1sc01185e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 07/05/2021] [Indexed: 11/21/2022] Open
Abstract
The computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios—the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery—is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions. We show how alchemical free energies can be calculated with QML potentials to identify deficiencies in RRHO approximations for computing tautomeric free energies, and how these potentials can be learned from experiment to improve prediction accuracy.![]()
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Affiliation(s)
- Marcus Wieder
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
| | - Josh Fass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA .,Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences New York NY 10065 USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
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31
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Zimmerman MI, Porter JR, Ward MD, Singh S, Vithani N, Meller A, Mallimadugula UL, Kuhn CE, Borowsky JH, Wiewiora RP, Hurley MFD, Harbison AM, Fogarty CA, Coffland JE, Fadda E, Voelz VA, Chodera JD, Bowman GR. SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nat Chem 2021; 13:651-659. [PMID: 34031561 PMCID: PMC8249329 DOI: 10.1038/s41557-021-00707-0] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/14/2021] [Indexed: 01/20/2023]
Abstract
SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression and replication that depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate 0.1 seconds of the viral proteome. Our adaptive sampling simulations predict dramatic opening of the apo spike complex, far beyond that seen experimentally, explaining and predicting the existence of 'cryptic' epitopes. Different spike variants modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also discover dramatic conformational changes across the proteome, which reveal over 50 'cryptic' pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas.
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Affiliation(s)
- Maxwell I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Justin R Porter
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Michael D Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Sukrit Singh
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Neha Vithani
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Upasana L Mallimadugula
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Catherine E Kuhn
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Jonathan H Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Rafal P Wiewiora
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, NY, New York, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, NY, New York, USA
| | | | - Aoife M Harbison
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - Carl A Fogarty
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | | | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, PA, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, NY, New York, USA
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA.
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA.
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32
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Morris A, McCorkindale W, Consortium TCM, Drayman N, Chodera JD, Tay S, London N, Lee AA. Discovery of SARS-CoV-2 main protease inhibitors using a synthesis-directed de novo design model. Chem Commun (Camb) 2021; 57:5909-5912. [PMID: 34008627 PMCID: PMC8204246 DOI: 10.1039/d1cc00050k] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The SARS-CoV-2 main viral protease (Mpro) is an attractive target for antivirals given its distinctiveness from host proteases, essentiality in the viral life cycle and conservation across coronaviridae. We launched the COVID Moonshot initiative to rapidly develop patent-free antivirals with open science and open data. Here we report the use of machine learning for de novo design, coupled with synthesis route prediction, in our campaign. We discover novel chemical scaffolds active in biochemical and live virus assays, synthesized with model generated routes. We discovered potent SARS-CoV-2 main protease inhibitors using synthesis-directed molecular design.![]()
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Affiliation(s)
- Aaron Morris
- PostEra Inc, 2 Embarcadero Centre, San Franciso, CA 94111, USA.
| | | | | | - Nir Drayman
- The Pritzker School for Molecular Engineering, The University of Chicago, Chicago, IL, USA
| | - John D Chodera
- Computational and Systems Biology Program Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Savaş Tay
- The Pritzker School for Molecular Engineering, The University of Chicago, Chicago, IL, USA
| | - Nir London
- Department of Organic Chemistry, The Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Alpha A Lee
- PostEra Inc, 2 Embarcadero Centre, San Franciso, CA 94111, USA.
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33
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Suárez E, Wiewiora RP, Wehmeyer C, Noé F, Chodera JD, Zuckerman DM. What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models. J Chem Theory Comput 2021; 17:3119-3133. [PMID: 33904312 PMCID: PMC8127341 DOI: 10.1021/acs.jctc.0c01154] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Markov state models (MSMs) have been widely applied to study the kinetics and pathways of protein conformational dynamics based on statistical analysis of molecular dynamics (MD) simulations. These MSMs coarse-grain both configuration space and time in ways that limit what kinds of observables they can reproduce with high fidelity over different spatial and temporal resolutions. Despite their popularity, there is still limited understanding of which biophysical observables can be computed from these MSMs in a robust and unbiased manner, and which suffer from the space-time coarse-graining intrinsic in the MSM model. Most theoretical arguments and practical validity tests for MSMs rely on long-time equilibrium kinetics, such as the slowest relaxation time scales and experimentally observable time-correlation functions. Here, we perform an extensive assessment of the ability of well-validated protein folding MSMs to accurately reproduce path-based observable such as mean first-passage times (MFPTs) and transition path mechanisms compared to a direct trajectory analysis. We also assess a recently proposed class of history-augmented MSMs (haMSMs) that exploit additional information not accounted for in standard MSMs. We conclude with some practical guidance on the use of MSMs to study various problems in conformational dynamics of biomolecules. In brief, MSMs can accurately reproduce correlation functions slower than the lag time, but path-based observables can only be reliably reproduced if the lifetimes of states exceed the lag time, which is a much stricter requirement. Even in the presence of short-lived states, we find that haMSMs reproduce path-based observables more reliably.
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Affiliation(s)
- Ernesto Suárez
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD 21702
| | - Rafal P. Wiewiora
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Daniel M. Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239
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34
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Starr TN, Czudnochowski N, Zatta F, Park YJ, Liu Z, Addetia A, Pinto D, Beltramello M, Hernandez P, Greaney AJ, Marzi R, Glass WG, Zhang I, Dingens AS, Bowen JE, Wojcechowskyj JA, De Marco A, Rosen LE, Zhou J, Montiel-Ruiz M, Kaiser H, Tucker H, Housley MP, di Iulio J, Lombardo G, Agostini M, Sprugasci N, Culap K, Jaconi S, Meury M, Dellota E, Cameroni E, Croll TI, Nix JC, Havenar-Daughton C, Telenti A, Lempp FA, Pizzuto MS, Chodera JD, Hebner CM, Whelan SP, Virgin HW, Veesler D, Corti D, Bloom JD, Snell G. Antibodies to the SARS-CoV-2 receptor-binding domain that maximize breadth and resistance to viral escape. bioRxiv 2021:2021.04.06.438709. [PMID: 33851154 PMCID: PMC8043444 DOI: 10.1101/2021.04.06.438709] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An ideal anti-SARS-CoV-2 antibody would resist viral escape 1-3 , have activity against diverse SARS-related coronaviruses 4-7 , and be highly protective through viral neutralization 8-11 and effector functions 12,13 . Understanding how these properties relate to each other and vary across epitopes would aid development of antibody therapeutics and guide vaccine design. Here, we comprehensively characterize escape, breadth, and potency across a panel of SARS-CoV-2 antibodies targeting the receptor-binding domain (RBD), including S309 4 , the parental antibody of the late-stage clinical antibody VIR-7831. We observe a tradeoff between SARS-CoV-2 in vitro neutralization potency and breadth of binding across SARS-related coronaviruses. Nevertheless, we identify several neutralizing antibodies with exceptional breadth and resistance to escape, including a new antibody (S2H97) that binds with high affinity to all SARS-related coronavirus clades via a unique RBD epitope centered on residue E516. S2H97 and other escape-resistant antibodies have high binding affinity and target functionally constrained RBD residues. We find that antibodies targeting the ACE2 receptor binding motif (RBM) typically have poor breadth and are readily escaped by mutations despite high neutralization potency, but we identify one potent RBM antibody (S2E12) with breadth across sarbecoviruses closely related to SARS-CoV-2 and with a high barrier to viral escape. These data highlight functional diversity among antibodies targeting the RBD and identify epitopes and features to prioritize for antibody and vaccine development against the current and potential future pandemics.
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Affiliation(s)
- Tyler N. Starr
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | | | - Fabrizia Zatta
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Young-Jun Park
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Zhuoming Liu
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Amin Addetia
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Dora Pinto
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Martina Beltramello
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | | | - Allison J. Greaney
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Roberta Marzi
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - William G. Glass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, New York, NY 10065, USA
| | - Adam S. Dingens
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - John E. Bowen
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | | | - Anna De Marco
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | | | - Jiayi Zhou
- Vir Biotechnology, San Francisco, CA 94158, USA
| | | | | | | | | | | | - Gloria Lombardo
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | | | - Nicole Sprugasci
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Katja Culap
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Stefano Jaconi
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | | | | | - Elisabetta Cameroni
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Tristan I. Croll
- Cambridge Institute for Medical Research, Department of Haematology, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Jay C. Nix
- Molecular Biology Consortium, Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | | | | | - Matteo S. Pizzuto
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Sean P.J. Whelan
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Herbert W. Virgin
- Vir Biotechnology, San Francisco, CA 94158, USA
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, USA
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Davide Corti
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Jesse D. Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, Seattle, WA 98109, USA
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35
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Thomson EC, Rosen LE, Shepherd JG, Spreafico R, da Silva Filipe A, Wojcechowskyj JA, Davis C, Piccoli L, Pascall DJ, Dillen J, Lytras S, Czudnochowski N, Shah R, Meury M, Jesudason N, De Marco A, Li K, Bassi J, O'Toole A, Pinto D, Colquhoun RM, Culap K, Jackson B, Zatta F, Rambaut A, Jaconi S, Sreenu VB, Nix J, Zhang I, Jarrett RF, Glass WG, Beltramello M, Nomikou K, Pizzuto M, Tong L, Cameroni E, Croll TI, Johnson N, Di Iulio J, Wickenhagen A, Ceschi A, Harbison AM, Mair D, Ferrari P, Smollett K, Sallusto F, Carmichael S, Garzoni C, Nichols J, Galli M, Hughes J, Riva A, Ho A, Schiuma M, Semple MG, Openshaw PJM, Fadda E, Baillie JK, Chodera JD, Rihn SJ, Lycett SJ, Virgin HW, Telenti A, Corti D, Robertson DL, Snell G. Circulating SARS-CoV-2 spike N439K variants maintain fitness while evading antibody-mediated immunity. Cell 2021. [PMID: 33621484 DOI: 10.1101/2020.11.04.355842] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
SARS-CoV-2 can mutate and evade immunity, with consequences for efficacy of emerging vaccines and antibody therapeutics. Here, we demonstrate that the immunodominant SARS-CoV-2 spike (S) receptor binding motif (RBM) is a highly variable region of S and provide epidemiological, clinical, and molecular characterization of a prevalent, sentinel RBM mutation, N439K. We demonstrate N439K S protein has enhanced binding affinity to the hACE2 receptor, and N439K viruses have similar in vitro replication fitness and cause infections with similar clinical outcomes as compared to wild type. We show the N439K mutation confers resistance against several neutralizing monoclonal antibodies, including one authorized for emergency use by the US Food and Drug Administration (FDA), and reduces the activity of some polyclonal sera from persons recovered from infection. Immune evasion mutations that maintain virulence and fitness such as N439K can emerge within SARS-CoV-2 S, highlighting the need for ongoing molecular surveillance to guide development and usage of vaccines and therapeutics.
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Affiliation(s)
- Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK; Department of Clinical Research, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - James G Shepherd
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Ana da Silva Filipe
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Chris Davis
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Luca Piccoli
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - David J Pascall
- Institute of Biodiversity, Animal Health and Comparative Medicine, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow G61 1QH, UK
| | - Josh Dillen
- Vir Biotechnology, San Francisco, CA 94158, USA
| | - Spyros Lytras
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Rajiv Shah
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Natasha Jesudason
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Anna De Marco
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Kathy Li
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Jessica Bassi
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Aine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Dora Pinto
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Rachel M Colquhoun
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Katja Culap
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Fabrizia Zatta
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Stefano Jaconi
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Vattipally B Sreenu
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Jay Nix
- Molecular Biology Consortium, Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, New York, NY 10065, USA
| | - Ruth F Jarrett
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - William G Glass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Martina Beltramello
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Kyriaki Nomikou
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Matteo Pizzuto
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Lily Tong
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Elisabetta Cameroni
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Tristan I Croll
- Cambridge Institute for Medical Research, Department of Haematology, University of Cambridge, Cambridge CB2 0XY, UK
| | - Natasha Johnson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Arthur Wickenhagen
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Alessandro Ceschi
- Faculty of Biomedical Sciences, Università della Svizzera italiana, 6900 Lugano, Switzerland; Division of Clinical Pharmacology and Toxicology, Institute of Pharmacological Sciences of Southern Switzerland, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland; Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Aoife M Harbison
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - Daniel Mair
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Paolo Ferrari
- Department of Nephrology, Ospedale Civico Lugano, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland; Prince of Wales Hospital Clinical School, University of New South Wales, Sydney, NSW 2052, Australia
| | - Katherine Smollett
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Federica Sallusto
- Institute for Research in Biomedicine, Università della Svizzera italiana, 6500 Bellinzona, Switzerland; ETH Institute of Microbiology, ETH Zurich, 8093 Zürich, Switzerland
| | - Stephen Carmichael
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Christian Garzoni
- Clinic of Internal Medicine and Infectious Diseases, Clinica Luganese Moncucco, 6900 Lugano, Switzerland
| | - Jenna Nichols
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Massimo Galli
- III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, 20157 Milan, Italy
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Agostino Riva
- III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, 20157 Milan, Italy
| | - Antonia Ho
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Marco Schiuma
- III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, 20157 Milan, Italy
| | - Malcolm G Semple
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 7BE, UK; Respiratory Medicine, Alder Hey Children's Hospital, Liverpool L12 2AP, UK
| | - Peter J M Openshaw
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - J Kenneth Baillie
- The Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK; Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh EH16 4SA, UK
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Suzannah J Rihn
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Samantha J Lycett
- The Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Herbert W Virgin
- Vir Biotechnology, San Francisco, CA 94158, USA; Washington University School of Medicine, Saint Louis, MO 63110, USA
| | | | - Davide Corti
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - David L Robertson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK.
| | - Gyorgy Snell
- Vir Biotechnology, San Francisco, CA 94158, USA.
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36
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Thomson EC, Rosen LE, Shepherd JG, Spreafico R, da Silva Filipe A, Wojcechowskyj JA, Davis C, Piccoli L, Pascall DJ, Dillen J, Lytras S, Czudnochowski N, Shah R, Meury M, Jesudason N, De Marco A, Li K, Bassi J, O'Toole A, Pinto D, Colquhoun RM, Culap K, Jackson B, Zatta F, Rambaut A, Jaconi S, Sreenu VB, Nix J, Zhang I, Jarrett RF, Glass WG, Beltramello M, Nomikou K, Pizzuto M, Tong L, Cameroni E, Croll TI, Johnson N, Di Iulio J, Wickenhagen A, Ceschi A, Harbison AM, Mair D, Ferrari P, Smollett K, Sallusto F, Carmichael S, Garzoni C, Nichols J, Galli M, Hughes J, Riva A, Ho A, Schiuma M, Semple MG, Openshaw PJM, Fadda E, Baillie JK, Chodera JD, Rihn SJ, Lycett SJ, Virgin HW, Telenti A, Corti D, Robertson DL, Snell G. Circulating SARS-CoV-2 spike N439K variants maintain fitness while evading antibody-mediated immunity. Cell 2021; 184:1171-1187.e20. [PMID: 33621484 PMCID: PMC7843029 DOI: 10.1016/j.cell.2021.01.037] [Citation(s) in RCA: 410] [Impact Index Per Article: 136.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/12/2020] [Accepted: 01/22/2021] [Indexed: 12/13/2022]
Abstract
SARS-CoV-2 can mutate and evade immunity, with consequences for efficacy of emerging vaccines and antibody therapeutics. Here, we demonstrate that the immunodominant SARS-CoV-2 spike (S) receptor binding motif (RBM) is a highly variable region of S and provide epidemiological, clinical, and molecular characterization of a prevalent, sentinel RBM mutation, N439K. We demonstrate N439K S protein has enhanced binding affinity to the hACE2 receptor, and N439K viruses have similar in vitro replication fitness and cause infections with similar clinical outcomes as compared to wild type. We show the N439K mutation confers resistance against several neutralizing monoclonal antibodies, including one authorized for emergency use by the US Food and Drug Administration (FDA), and reduces the activity of some polyclonal sera from persons recovered from infection. Immune evasion mutations that maintain virulence and fitness such as N439K can emerge within SARS-CoV-2 S, highlighting the need for ongoing molecular surveillance to guide development and usage of vaccines and therapeutics.
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Affiliation(s)
- Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK; Department of Clinical Research, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - James G Shepherd
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Ana da Silva Filipe
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Chris Davis
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Luca Piccoli
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - David J Pascall
- Institute of Biodiversity, Animal Health and Comparative Medicine, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow G61 1QH, UK
| | - Josh Dillen
- Vir Biotechnology, San Francisco, CA 94158, USA
| | - Spyros Lytras
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Rajiv Shah
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Natasha Jesudason
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Anna De Marco
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Kathy Li
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Jessica Bassi
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Aine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Dora Pinto
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Rachel M Colquhoun
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Katja Culap
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Fabrizia Zatta
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Stefano Jaconi
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Vattipally B Sreenu
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Jay Nix
- Molecular Biology Consortium, Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, New York, NY 10065, USA
| | - Ruth F Jarrett
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - William G Glass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Martina Beltramello
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Kyriaki Nomikou
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Matteo Pizzuto
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Lily Tong
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Elisabetta Cameroni
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - Tristan I Croll
- Cambridge Institute for Medical Research, Department of Haematology, University of Cambridge, Cambridge CB2 0XY, UK
| | - Natasha Johnson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | | | - Arthur Wickenhagen
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Alessandro Ceschi
- Faculty of Biomedical Sciences, Università della Svizzera italiana, 6900 Lugano, Switzerland; Division of Clinical Pharmacology and Toxicology, Institute of Pharmacological Sciences of Southern Switzerland, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland; Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Aoife M Harbison
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - Daniel Mair
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Paolo Ferrari
- Department of Nephrology, Ospedale Civico Lugano, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland; Prince of Wales Hospital Clinical School, University of New South Wales, Sydney, NSW 2052, Australia
| | - Katherine Smollett
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Federica Sallusto
- Institute for Research in Biomedicine, Università della Svizzera italiana, 6500 Bellinzona, Switzerland; ETH Institute of Microbiology, ETH Zurich, 8093 Zürich, Switzerland
| | - Stephen Carmichael
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Christian Garzoni
- Clinic of Internal Medicine and Infectious Diseases, Clinica Luganese Moncucco, 6900 Lugano, Switzerland
| | - Jenna Nichols
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Massimo Galli
- III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, 20157 Milan, Italy
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Agostino Riva
- III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, 20157 Milan, Italy
| | - Antonia Ho
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Marco Schiuma
- III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, 20157 Milan, Italy
| | - Malcolm G Semple
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 7BE, UK; Respiratory Medicine, Alder Hey Children's Hospital, Liverpool L12 2AP, UK
| | - Peter J M Openshaw
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - J Kenneth Baillie
- The Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK; Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh EH16 4SA, UK
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Suzannah J Rihn
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Samantha J Lycett
- The Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Herbert W Virgin
- Vir Biotechnology, San Francisco, CA 94158, USA; Washington University School of Medicine, Saint Louis, MO 63110, USA
| | | | - Davide Corti
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - David L Robertson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK.
| | - Gyorgy Snell
- Vir Biotechnology, San Francisco, CA 94158, USA.
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37
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Işık M, Rustenburg AS, Rizzi A, Gunner MR, Mobley DL, Chodera JD. Overview of the SAMPL6 pK a challenge: evaluating small molecule microscopic and macroscopic pK a predictions. J Comput Aided Mol Des 2021; 35:131-166. [PMID: 33394238 PMCID: PMC7904668 DOI: 10.1007/s10822-020-00362-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 11/17/2020] [Indexed: 01/01/2023]
Abstract
The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.
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Affiliation(s)
- Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA.
| | - Ariën S Rustenburg
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY, 10065, USA
| | - Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA
| | - M R Gunner
- Department of Physics, City College of New York, New York, NY, 10031, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Abstract
Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.
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Affiliation(s)
- Steven K. Albanese
- Louis V. Gerstner, Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Andrea Volkamer
- Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin
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Zimmerman MI, Porter JR, Ward MD, Singh S, Vithani N, Meller A, Mallimadugula UL, Kuhn CE, Borowsky JH, Wiewiora RP, Hurley MFD, Harbison AM, Fogarty CA, Coffland JE, Fadda E, Voelz VA, Chodera JD, Bowman GR. SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome. bioRxiv 2020:2020.06.27.175430. [PMID: 32637963 PMCID: PMC7337393 DOI: 10.1101/2020.06.27.175430] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression, and replication, which depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate an unprecedented 0.1 seconds of the viral proteome. Our simulations capture dramatic opening of the apo Spike complex, far beyond that seen experimentally, which explains and successfully predicts the existence of 'cryptic' epitopes. Different Spike homologues modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also observe dramatic conformational changes across the proteome, which reveal over 50 'cryptic' pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas.
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Affiliation(s)
- Maxwell I. Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Justin R. Porter
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Michael D. Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Sukrit Singh
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Neha Vithani
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Upasana L. Mallimadugula
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Catherine E. Kuhn
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Jonathan H. Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Rafal P. Wiewiora
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Computational and Systems Biology Program, Sloan Kettering Institute, New York, New York 10065, United States
| | - Matthew F. D. Hurley
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Aoife M Harbison
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Kildare, Ireland
| | - Carl A Fogarty
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Kildare, Ireland
| | | | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Kildare, Ireland
| | - Vincent A. Voelz
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, New York, New York 10065, United States
| | - Gregory R. Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
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Gkeka P, Stoltz G, Barati Farimani A, Belkacemi Z, Ceriotti M, Chodera JD, Dinner AR, Ferguson AL, Maillet JB, Minoux H, Peter C, Pietrucci F, Silveira A, Tkatchenko A, Trstanova Z, Wiewiora R, Lelièvre T. Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems. J Chem Theory Comput 2020; 16:4757-4775. [PMID: 32559068 PMCID: PMC8312194 DOI: 10.1021/acs.jctc.0c00355] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
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Affiliation(s)
- Paraskevi Gkeka
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
| | - Gabriel Stoltz
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
| | | | - Zineb Belkacemi
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
| | - Michele Ceriotti
- Laboratory of Computational Science and Modelling, Institute of Materials, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Aaron R Dinner
- Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United States
| | | | - Hervé Minoux
- Integrated Drug Discovery, Sanofi R&D, 94403 Vitry-sur-Seine, France
| | | | - Fabio Pietrucci
- UMR CNRS 7590, MNHN, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, 75005 Paris, France
| | - Ana Silveira
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Zofia Trstanova
- School of Mathematics, The University of Edinburgh, Edinburgh EH9 3FD, U.K
| | - Rafal Wiewiora
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Tony Lelièvre
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
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41
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Işık M, Bergazin TD, Fox T, Rizzi A, Chodera JD, Mobley DL. Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge. J Comput Aided Mol Des 2020; 34:335-370. [PMID: 32107702 PMCID: PMC7138020 DOI: 10.1007/s10822-020-00295-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
Abstract
The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.
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Affiliation(s)
- Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA.
| | | | - Thomas Fox
- Computational Chemistry, Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach, Germany
| | - Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
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42
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Rizzi A, Jensen T, Slochower DR, Aldeghi M, Gapsys V, Ntekoumes D, Bosisio S, Papadourakis M, Henriksen NM, de Groot BL, Cournia Z, Dickson A, Michel J, Gilson MK, Shirts MR, Mobley DL, Chodera JD. The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations. J Comput Aided Mol Des 2020; 34:601-633. [PMID: 31984465 DOI: 10.1007/s10822-020-00290-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 01/13/2020] [Indexed: 12/22/2022]
Abstract
Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.
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Affiliation(s)
- Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA.
| | - Travis Jensen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - David R Slochower
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Matteo Aldeghi
- Max Planck Institute for Biophysical Chemistry, Computational Biomolecular Dynamics Group, Göttingen, Germany
| | - Vytautas Gapsys
- Max Planck Institute for Biophysical Chemistry, Computational Biomolecular Dynamics Group, Göttingen, Germany
| | - Dimitris Ntekoumes
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Stefano Bosisio
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Michail Papadourakis
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Niel M Henriksen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Atomwise, 717 Market St Suite 800, San Francisco, CA, 94103, USA
| | - Bert L de Groot
- Max Planck Institute for Biophysical Chemistry, Computational Biomolecular Dynamics Group, Göttingen, Germany
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Alex Dickson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California, 92697, USA.
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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Mey ASJS, Allen BK, Macdonald HEB, Chodera JD, Hahn DF, Kuhn M, Michel J, Mobley DL, Naden LN, Prasad S, Rizzi A, Scheen J, Shirts MR, Tresadern G, Xu H. Best Practices for Alchemical Free Energy Calculations [Article v1.0]. Living J Comput Mol Sci 2020; 2:18378. [PMID: 34458687 PMCID: PMC8388617 DOI: 10.33011/livecoms.2.1.18378] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.
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Affiliation(s)
- Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Maximilian Kuhn
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
- Cresset, Cambridgeshire, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, Irvine, USA
| | - Levi N. Naden
- Molecular Sciences Software Institute, Blacksburg VA, USA
| | | | - Andrea Rizzi
- Silicon Therapeutics, Boston, MA, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Jenke Scheen
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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44
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Işık M, Levorse D, Mobley DL, Rhodes T, Chodera JD. Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. J Comput Aided Mol Des 2019; 34:405-420. [PMID: 31858363 DOI: 10.1007/s10822-019-00271-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 12/07/2019] [Indexed: 11/24/2022]
Abstract
Partition coefficients describe the equilibrium partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol-water partition coefficients ([Formula: see text]), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II octanol-water partition coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 [Formula: see text] prediction challenge in a blind experimental benchmark. Following experimental data collection, the partition coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol-water log P dataset for this SAMPL6 Part II partition coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
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Affiliation(s)
- Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA
| | - Dorothy Levorse
- Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - Timothy Rhodes
- Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA.
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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45
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Slochower DR, Henriksen NM, Wang LP, Chodera JD, Mobley DL, Gilson MK. Binding Thermodynamics of Host-Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative. J Chem Theory Comput 2019; 15:6225-6242. [PMID: 31603667 PMCID: PMC7328435 DOI: 10.1021/acs.jctc.9b00748] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small-molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a druglike guest molecule have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations. The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root-mean-square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.
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Affiliation(s)
- David R Slochower
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Niel M Henriksen
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Lee-Ping Wang
- Department of Chemistry , University of California , Davis , California 95616 , United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute , Memorial Sloan Kettering Cancer Center , New York , New York 10065 , United States
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry , University of California , Irvine , California 92697 , United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
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46
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Wojnarowicz PM, Lima E Silva R, Ohnaka M, Lee SB, Chin Y, Kulukian A, Chang SH, Desai B, Garcia Escolano M, Shah R, Garcia-Cao M, Xu S, Kadam R, Goldgur Y, Miller MA, Ouerfelli O, Yang G, Arakawa T, Albanese SK, Garland WA, Stoller G, Chaudhary J, Norton L, Soni RK, Philip J, Hendrickson RC, Iavarone A, Dannenberg AJ, Chodera JD, Pavletich N, Lasorella A, Campochiaro PA, Benezra R. A Small-Molecule Pan-Id Antagonist Inhibits Pathologic Ocular Neovascularization. Cell Rep 2019; 29:62-75.e7. [PMID: 31577956 PMCID: PMC6896334 DOI: 10.1016/j.celrep.2019.08.073] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 08/09/2019] [Accepted: 08/23/2019] [Indexed: 02/01/2023] Open
Abstract
Id helix-loop-helix (HLH) proteins (Id1-4) bind E protein bHLH transcription factors, preventing them from forming active transcription complexes that drive changes in cell states. Id proteins are primarily expressed during development to inhibit differentiation, but they become re-expressed in adult tissues in diseases of the vasculature and cancer. We show that the genetic loss of Id1/Id3 reduces ocular neovascularization in mouse models of wet age-related macular degeneration (AMD) and retinopathy of prematurity (ROP). An in silico screen identifies AGX51, a small-molecule Id antagonist. AGX51 inhibits the Id1-E47 interaction, leading to ubiquitin-mediated degradation of Ids, cell growth arrest, and reduced viability. AGX51 is well-tolerated in mice and phenocopies the genetic loss of Id expression in AMD and ROP models by inhibiting retinal neovascularization. Thus, AGX51 is a first-in-class compound that antagonizes an interaction formerly considered undruggable and that may have utility in the management of multiple diseases.
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Affiliation(s)
- Paulina M Wojnarowicz
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Raquel Lima E Silva
- Departments of Ophthalmology and Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Masayuki Ohnaka
- Departments of Ophthalmology and Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Sang Bae Lee
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY 10032, USA
| | - Yvette Chin
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Anita Kulukian
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sung-Hee Chang
- Department of Medicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - Bina Desai
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Marta Garcia Escolano
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Riddhi Shah
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Marta Garcia-Cao
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sijia Xu
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Rashmi Kadam
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yehuda Goldgur
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Meredith A Miller
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ouathek Ouerfelli
- Organic Synthesis Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Guangli Yang
- Organic Synthesis Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Tsutomu Arakawa
- Alliance Protein Laboratories, a Division of KBI Biopharma, San Diego, CA 92121, USA
| | - Steven K Albanese
- Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Glenn Stoller
- Ophthalmic Consultants of Long Island, Lynbrook, NY 11563, USA
| | - Jaideep Chaudhary
- Center for Cancer Research and Therapeutic Development, Clark Atlanta University, Atlanta, GA 30314, USA
| | - Larry Norton
- Evelyn H. Lauder Breast Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Rajesh Kumar Soni
- Proteomics & Microchemistry Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - John Philip
- Proteomics & Microchemistry Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ronald C Hendrickson
- Proteomics & Microchemistry Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Antonio Iavarone
- Department of Neurology, Department of Pathology, Institute for Cancer Genetics, Columbia University Medical Center, New York, NY 10032, USA
| | - Andrew J Dannenberg
- Department of Medicine, Weill Cornell Medical College, New York, NY 10065, USA
| | - John D Chodera
- Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Nikola Pavletich
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Anna Lasorella
- Department of Pediatrics, Department of Pathology, Institute for Cancer Genetics, Columbia University Medical Center, New York, NY 10032, USA
| | - Peter A Campochiaro
- Departments of Ophthalmology and Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Robert Benezra
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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47
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Sang D, Pinglay S, Wiewiora RP, Selvan ME, Lou HJ, Chodera JD, Turk BE, Gümüş ZH, Holt LJ. Ancestral reconstruction reveals mechanisms of ERK regulatory evolution. eLife 2019; 8:38805. [PMID: 31407663 PMCID: PMC6692128 DOI: 10.7554/elife.38805] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 07/03/2019] [Indexed: 01/21/2023] Open
Abstract
Protein kinases are crucial to coordinate cellular decisions and therefore their activities are strictly regulated. Previously we used ancestral reconstruction to determine how CMGC group kinase specificity evolved (Howard et al., 2014). In the present study, we reconstructed ancestral kinases to study the evolution of regulation, from the inferred ancestor of CDKs and MAPKs, to modern ERKs. Kinases switched from high to low autophosphorylation activity at the transition to the inferred ancestor of ERKs 1 and 2. Two synergistic amino acid changes were sufficient to induce this change: shortening of the β3-αC loop and mutation of the gatekeeper residue. Restoring these two mutations to their inferred ancestral state led to a loss of dependence of modern ERKs 1 and 2 on the upstream activating kinase MEK in human cells. Our results shed light on the evolutionary mechanisms that led to the tight regulation of a kinase that is central in development and disease.
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Affiliation(s)
- Dajun Sang
- Institute for Systems Genetics, New York University Langone Medical Center, New York, United States
| | - Sudarshan Pinglay
- Institute for Systems Genetics, New York University Langone Medical Center, New York, United States
| | - Rafal P Wiewiora
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, United States.,Memorial Sloan Kettering Cancer Center, New York, United States
| | - Myvizhi E Selvan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Hua Jane Lou
- Department of Pharmacology, Yale University School of Medicine, New Haven, United States
| | - John D Chodera
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Benjamin E Turk
- Department of Pharmacology, Yale University School of Medicine, New Haven, United States
| | - Zeynep H Gümüş
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Liam J Holt
- Institute for Systems Genetics, New York University Langone Medical Center, New York, United States
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48
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Chen S, Wiewiora RP, Meng F, Babault N, Ma A, Yu W, Qian K, Hu H, Zou H, Wang J, Fan S, Blum G, Pittella-Silva F, Beauchamp KA, Tempel W, Jiang H, Chen K, Skene RJ, Zheng YG, Brown PJ, Jin J, Luo C, Chodera JD, Luo M. The dynamic conformational landscape of the protein methyltransferase SETD8. eLife 2019; 8:45403. [PMID: 31081496 PMCID: PMC6579520 DOI: 10.7554/elife.45403] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/08/2019] [Indexed: 12/27/2022] Open
Abstract
Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape. Our cells contain thousands of proteins that perform many different tasks. Such tasks often involve significant changes in the shape of a protein that allow it to interact with other proteins or ligands. Understanding these shape changes can be an essential step for predicting and manipulating how proteins work or designing new drugs. Some changes in protein shape happen quickly, whereas others take longer. Existing experimental approaches generally only capture some, but not all, of the different shapes an individual protein adopts. A family of proteins known as protein lysine methyltransferases (PKMTs) help to regulate the activities of other proteins by adding small tags called methyl groups to specific positions on their target proteins. PKMTs play important roles in many life processes including in activating genes, maintaining stem cells and controlling how organs develop. It is important for cells to properly control the activity of PKMTs because too much, or too little, activity can promote cancers and neurological diseases. For example, genetic mutations that increase the levels of a PKMT known as SETD8 appear to promote the progression of some breast cancers and childhood leukemia. There is a pressing need to develop new drugs that can inhibit SETD8 and other PKMTs in human patients. However, these efforts are hindered by the lack of understanding of exactly how the shape of PKMT proteins change as they operate in cells. Chen, Wiewiora et al. used a technique called X-ray crystallography to generate structural models of the human SETD8 protein in the presence or absence of native or foreign ligands. These models were used to develop computer simulations of how the shape of SETD8 changes as it operates. Further computational analysis and laboratory experiments revealed how slow changes in the shape of SETD8 contribute to the ability of the protein to attach methyl groups to other proteins. This work is a significant stepping-stone to developing a complete model of how the SETD8 protein works, as well as understanding how genetic mutations may affect the protein’s role in the body. The next step is to refine the model by integrating data from other approaches including biophysical models and mathematical calculations of the energy associated with the shape changes, with a long-term goal to better understand and then manipulate the function of SETD8.
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Affiliation(s)
- Shi Chen
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States.,Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Rafal P Wiewiora
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States.,Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Fanwang Meng
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Nicolas Babault
- Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Anqi Ma
- Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Wenyu Yu
- Structural Genomics Consortium, University of Toronto, Toronto, Canada
| | - Kun Qian
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, United States
| | - Hao Hu
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, United States
| | - Hua Zou
- Takeda California, Science Center Drive, San Diego, United States
| | - Junyi Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Shijie Fan
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Gil Blum
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Fabio Pittella-Silva
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Kyle A Beauchamp
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Wolfram Tempel
- Structural Genomics Consortium, University of Toronto, Toronto, Canada
| | - Hualiang Jiang
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Kaixian Chen
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Robert J Skene
- Takeda California, Science Center Drive, San Diego, United States
| | - Yujun George Zheng
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, United States
| | - Peter J Brown
- Structural Genomics Consortium, University of Toronto, Toronto, Canada
| | - Jian Jin
- Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Cheng Luo
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - John D Chodera
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Minkui Luo
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States.,Program of Pharmacology, Weill Cornell Medical College of Cornell University, New York, United States
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49
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Swenson DWH, Prinz JH, Noe F, Chodera JD, Bolhuis PG. OpenPathSampling: A Python Framework for Path Sampling Simulations. 2. Building and Customizing Path Ensembles and Sample Schemes. J Chem Theory Comput 2019; 15:837-856. [PMID: 30359525 PMCID: PMC6374748 DOI: 10.1021/acs.jctc.8b00627] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Indexed: 11/28/2022]
Abstract
The OpenPathSampling (OPS) package provides an easy-to-use framework to apply transition path sampling methodologies to complex molecular systems with a minimum of effort. Yet, the extensibility of OPS allows for the exploration of new path sampling algorithms by building on a variety of basic operations. In a companion paper [ Swenson et al. J. Chem. Theory Comput. 2018 , 10.1021/acs.jctc.8b00626 ] we introduced the basic concepts and the structure of the OPS package, and how it can be employed to perform standard transition path sampling and (replica exchange) transition interface sampling. In this paper, we elaborate on two theoretical developments that went into the design of OPS. The first development relates to the construction of path ensembles, the what is being sampled. We introduce a novel set-based notation for the path ensemble, which provides an alternative paradigm for constructing path ensembles and allows building arbitrarily complex path ensembles from fundamental ones. The second fundamental development is the structure for the customization of Monte Carlo procedures; how path ensembles are being sampled. We describe in detail the OPS objects that implement this approach to customization, the MoveScheme and the PathMover, and provide tools to create and manipulate these objects. We illustrate both the path ensemble building and sampling scheme customization with several examples. OPS thus facilitates both standard path sampling application in complex systems as well as the development of new path sampling methodology, beyond the default.
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Affiliation(s)
- David W. H. Swenson
- van’t
Hoff Institute for Molecular Sciences, University
of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands
- Computational
and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Jan-Hendrik Prinz
- Computational
and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Department
of Mathematics and Computer Science, Arnimallee 6, Freie Universität Berlin, 14195 Berlin, Germany
| | - Frank Noe
- Department
of Mathematics and Computer Science, Arnimallee 6, Freie Universität Berlin, 14195 Berlin, Germany
| | - John D. Chodera
- Computational
and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Peter G. Bolhuis
- van’t
Hoff Institute for Molecular Sciences, University
of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands
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50
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Wiewiora RP, Chen S, Luo M, Chodera JD. Markov Models of Functional Dynamics of Histone Lysine Methyltransferases by Millisecond-Timescale Molecular Simulation and Chemical Probing. Biophys J 2019. [DOI: 10.1016/j.bpj.2018.11.1016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
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