1
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Martínez-Ortega U, Aguayo-Ortiz R, Aguilar-Cazares D, Guerrero-Molina ED, Aguilar-Martínez V, Moreno-Rodríguez A, López-González JS, Vázquez-Ramos JM, Hernández-Luis F. Alchemical free energy-based optimization of quinazoline derivatives as potent EGFR inhibitors with cytotoxic activity. Bioorg Med Chem 2025; 124:118179. [PMID: 40199186 DOI: 10.1016/j.bmc.2025.118179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2025] [Revised: 03/27/2025] [Accepted: 03/30/2025] [Indexed: 04/10/2025]
Abstract
Gefitinib (GFB) is a well-established EGFR inhibitor used in the treatment of non-small cell lung cancer (NSCLC) that has shown resistance in certain cases of this cancer. In this work, we aimed to enhance GFB's inhibitory activity using alchemical free energy calculations, leading to the design of five new quinazoline derivatives. Among these, compound 8a was the most potent, inhibiting EGFR at 10 µM and showing significant antiproliferative effects at 25 µM. Further optimization identified two new compounds, NCU00 and NCU01, with improved EGFR inhibition and superior cytotoxicity in four NSCLC cell lines compared to GFB. Molecular dynamics simulations revealed crucial interactions that contribute to the enhanced inhibitory activity of NCU00 and NCU01. Toxicological assessments in mice showed no adverse effects on kidney or liver function, and NCU01 exhibited no developmental toxicity in zebrafish embryos. This study highlights the effectiveness of alchemical free energy methods in optimizing quinazoline-bearing EGFR inhibitors.
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Affiliation(s)
- U Martínez-Ortega
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - R Aguayo-Ortiz
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - D Aguilar-Cazares
- Departamento de Enfermedades Crónico-Degenerativas, Laboratorio de Cáncer Pulmonar, Instituto Nacional de Enfermedades Respiratorias, Ismael Cosio Villegas, Mexico City 14080, Mexico
| | - E D Guerrero-Molina
- Departamento de Bioquímica, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - V Aguilar-Martínez
- Laboratorio de Estudios Epidemiológicos, Clínicos, Diseños Experimentales e Investigación, Facultad de Ciencias Químicas, Universidad Autónoma Benito Juárez de Oaxaca, Oaxaca 68120, Mexico
| | - A Moreno-Rodríguez
- Laboratorio de Estudios Epidemiológicos, Clínicos, Diseños Experimentales e Investigación, Facultad de Ciencias Químicas, Universidad Autónoma Benito Juárez de Oaxaca, Oaxaca 68120, Mexico
| | - J S López-González
- Departamento de Enfermedades Crónico-Degenerativas, Laboratorio de Cáncer Pulmonar, Instituto Nacional de Enfermedades Respiratorias, Ismael Cosio Villegas, Mexico City 14080, Mexico
| | - J M Vázquez-Ramos
- Departamento de Bioquímica, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - F Hernández-Luis
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
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2
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Koby SB, Gutkin E, Patel S, Kurnikova MG. Automated On-the-Fly Optimization of Resource Allocation for Efficient Free Energy Simulations. J Chem Inf Model 2025; 65:4932-4951. [PMID: 40328725 PMCID: PMC12121625 DOI: 10.1021/acs.jcim.4c02107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2025] [Revised: 03/13/2025] [Accepted: 04/02/2025] [Indexed: 05/08/2025]
Abstract
Computing the free energy of protein-ligand binding by employing molecular dynamics (MD) simulations is becoming a valuable tool in the early stages of drug discovery. However, the cost and complexity of such simulations are often prohibitive for high-throughput studies. We present an automated workflow for the thermodynamic integration scheme with the "on-the-fly" optimization of computational resource allocation for each λ-window of both relative and absolute binding free energy simulations. This iterative workflow utilizes automatic equilibration detection and convergence testing via the Jensen-Shannon distance to determine optimal simulation stopping points in an entirely data-driven manner. It is broadly applicable to multiple free energy calculations, such as ligand binding, amino acid mutations, and others, while utilizing different estimators, e.g., free energy perturbation, BAR, MBAR, etc. We benchmark our workflow on the well-characterized systems, namely, cyclin-dependent kinase 2 and T4 lysozyme L99A/M102Q mutant, and the more flexible SARS-CoV-2 papain-like protease. We demonstrate that this proposed protocol can achieve more than 85% reduction in computational expense while maintaining similar levels of accuracy compared to other benchmarking protocols. We examine the performance of this protocol on both small and large molecular transformations. The cost-accuracy tradeoff of repeated runs is also investigated.
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Affiliation(s)
- S. Benjamin Koby
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Evgeny Gutkin
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Shree Patel
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Maria G. Kurnikova
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
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3
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Gkeka P, Svensson F, Magadán CR, de Groot MJ, Jerome SV. Computational Hit Finding: An Industry Perspective. J Med Chem 2025. [PMID: 40392533 DOI: 10.1021/acs.jmedchem.4c03087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2025]
Abstract
Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converged around mature software programs and stock chemical libraries up to ∼10 million in size. Recently, however, powered by massive increases in computational power, the emergence of ultralarge make-on-demand virtual libraries, the development of large capacity neural networks, the expansion of the domain of applicability of free energy calculations, and advances in protein structure prediction, the virtual screening field is currently seeing major change. We present a guide from industry practitioners summarizing key aspects on the changing computational hit finding landscape including practical recommendations for building a performant end-to-end screening workflow, strategies to mitigate risk by avoiding common pitfalls, determining success criteria, and a brief discussion of emerging technologies likely to impact drug discovery in the near future.
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Affiliation(s)
- Paraskevi Gkeka
- Integrated Drug Discovery, Molecular Design Sciences, Sanofi, Vitry-sur-Seine 91380, France
| | - Fredrik Svensson
- Cancer Research Horizons, Jonas Webb Building, Babraham Research Campus, Cambridge CB22 3AT, U.K
| | | | | | - Steven V Jerome
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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4
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Laracuente X, Delfing BM, Luo X, Olson A, Jeffries W, Bowers SR, Foreman KW, Lee KH, Paige M, Kehn-Hall K, Lockhart C, Klimov DK. Applying Absolute Free Energy Perturbation Molecular Dynamics to Diffusively Binding Ligands. J Chem Theory Comput 2025; 21:4286-4298. [PMID: 40189800 PMCID: PMC12020361 DOI: 10.1021/acs.jctc.5c00121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2025]
Abstract
We have developed and tested an absolute free energy perturbation (FEP) protocol, which combines all-atom molecular dynamics, replica exchange with solute tempering (REST) enhanced sampling, and a spherical harmonic restraint applied to a ligand. Our objective was to compute the binding free energy together with the underlying binding mechanism for a ligand, which binds diffusively to a protein. Such ligands represent nearly impossible targets for traditional FEP simulations. To test our FEP/REST protocol, we selected a conserved motif peptide KKPK termed minNLS from the nuclear localization signal sequence of the Venezuelan equine encephalitis virus capsid protein. This peptide fragment binds diffusively to importin-α transport protein without forming well-defined poses. Our FEP/REST simulations with a spherical restraint provided a converged estimate of minNLS binding free energy. We found that minNLS binds with moderate affinity to importin-α utilizing an unusual, purely entropic mechanism in which binding free energy is determined by favorable entropic gain. For this cationic minNLS peptide, a favorable binding entropic gain is primarily associated with the release of water from the solvation shells of charged amino acids. We demonstrated that FEP/REST simulations sample the KKPK bound ensemble well, allowing us to characterize the distribution of bound structures, binding interactions, and locations on the importin-α surface. Analysis of experimental studies offered support to our rationale behind the KKPK entropic binding mechanism.
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Affiliation(s)
- Xavier
E. Laracuente
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Bryan M. Delfing
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Xingyu Luo
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Audrey Olson
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - William Jeffries
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Steven R. Bowers
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Kenneth W. Foreman
- Department
of Chemistry and Biochemistry, George Mason
University, Fairfax, Virginia 22030, United States
| | - Kyung Hyeon Lee
- Department
of Chemistry and Biochemistry, George Mason
University, Fairfax, Virginia 22030, United States
- Center
for Molecular Engineering, George Mason
University, Manassas, Virginia 20110, United States
| | - Mikell Paige
- Department
of Chemistry and Biochemistry, George Mason
University, Fairfax, Virginia 22030, United States
- Center
for Molecular Engineering, George Mason
University, Manassas, Virginia 20110, United States
| | - Kylene Kehn-Hall
- Department
of Biomedical Sciences and Pathobiology, Virginia-Maryland College
of Veterinary Medicine, Virginia Polytechnic
Institute and State University, Blacksburg, Virginia 24061, United States
- Center
for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Christopher Lockhart
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Dmitri K. Klimov
- School
of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
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5
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Liu R, Yao Y, Huang W, Zhong Y, Luo HB, Li Z. Divide-and-Conquer ABFE: Improving Free Energy Calculations by Enhancing Water Sampling. J Chem Theory Comput 2025; 21:3712-3725. [PMID: 40127297 DOI: 10.1021/acs.jctc.4c01661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2025]
Abstract
Free energy perturbation (FEP) is a promising method for accurately predicting molecular interactions, widely applied in fields such as drug design, materials science, and catalysis. However, FEP calculations, particularly those in aqueous environments, often suffer from convergence issues due to insufficient sampling of water molecules. These challenges are particularly critical in solvation-related free energy calculations, such as small molecule-protein binding, interface interactions, and molecular adsorption on surfaces. To address these limitations, we present the divide-and-conquer absolute binding free energy (DC-ABFE) method. By partitioning the ligand or molecule into atomic groups and sequentially decoupling their van der Waals interactions, DC-ABFE improves water re-entry sampling, enhances phase-space overlap, and significantly enhances the convergence of free energy calculations. Our benchmark demonstrates that DC-ABFE achieves more reproducible and reliable binding free energy predictions compared to traditional FEP methods. DC-ABFE is applicable to a range of free energy calculations involving solvation effects. Additionally, this method establishes a stronger foundation for precise drug screening, offering a more robust tool for predicting binding affinities in drug discovery.
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Affiliation(s)
- Runduo Liu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P. R. China
| | - Yufen Yao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P. R. China
| | - Wanyi Huang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P. R. China
| | - Yilin Zhong
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P. R. China
| | - Hai-Bin Luo
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
- Song Li' Academician Workstation of Hainan University (School of Pharmaceutical Sciences), Yazhou Bay, Sanya 572000, China
| | - Zhe Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P. R. China
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6
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Zhou H, Fu H, Shao X, Cai W. Identification of novel inhibitors for epidermal growth factor receptor tyrosine kinase using absolute binding free-energy simulations. Int J Biol Macromol 2025; 304:140989. [PMID: 39952524 DOI: 10.1016/j.ijbiomac.2025.140989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2024] [Revised: 01/16/2025] [Accepted: 02/11/2025] [Indexed: 02/17/2025]
Abstract
Mutations in the kinase domain of the epidermal growth factor receptor (EGFR), a critical biological macromolecule involved in cell growth and division, can lead to drug resistance in patients undergoing chemotherapy with kinase inhibitors. Notably, the emergence of the C797S mutation poses new challenges for targeted EGFR therapy, highlighting the urgent need for agents effective against this triple mutation (L858R/T790M/C797S, EGFR™). Building on our previous finding that sulfonyl and piperidinyl groups significantly contribute to the EGFR™-inhibitor interactions, we have identified the best-in-class inhibitors containing these groups through functional-group-based screening and formally exact absolute binding free-energy calculations. Our new strategy offers greater flexibility than traditional workflows leaning on relative binding free-energy calculations and accommodates ligands with substantial structural variations. The result shows that the top candidate exhibits a binding affinity of -15.8 kcal/mol towards the EGFR™ mutant, surpassing BLU-945, a state-of-the-art fourth-generation inhibitor with a binding free energy of -12.6 kcal/mol. Subsequent free-energy decomposition indicates that the presented top candidate primarily enhances interactions with the K745, D800 and R841 residues, suggesting its potential to overcome resistance from the C797S mutation. Notably, K745 forms highly favorable hydrogen bonds and cation-π interactions with C6. Targeting lysine has emerged as a promising strategy, especially in cases where the C797S mutation renders traditional covalent inhibitors ineffective. We propose that these novel inhibitors represent promising drug candidates for non-small cell lung cancer treatment and offer new strategies to overcome drug resistance caused by EGFR mutation.
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Affiliation(s)
- Huaxin Zhou
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Haohao Fu
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China.
| | - Xueguang Shao
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China.
| | - Wensheng Cai
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China.
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7
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Li P, Pu T, Mei Y. FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug Discovery. J Chem Inf Model 2025; 65:2711-2721. [PMID: 40029615 DOI: 10.1021/acs.jcim.4c01986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
The binding affinity between a drug molecule and its target, measured by the absolute binding free energy (ABFE), is a crucial factor in the lead discovery phase of drug development. Recent research has highlighted the potential of in silico ABFE predictions to directly aid drug development by allowing for the ranking and prioritization of promising candidates. This work introduces an open-source Python workflow called FEP-SPell-ABFE, designed to automate ABFE calculations with minimal user involvement. The workflow requires only three key inputs: a receptor protein structure in PDB format, candidate ligands in SDF format, and a configuration file (config.yaml) that governs both the workflow and molecular dynamics simulation parameters. It produces a ranked list of ligands along with their binding free energies in the comma-separated values (CSV) format. The workflow leverages SLURM (Simple Linux Utility for Resource Management) for automating task execution and resource allocation across the modules. A usage example and several benchmark systems for validation are provided. The FEP-SPell-ABFE workflow, along with a practical example, is publicly accessible on GitHub at https://github.com/freeenergylab/FEP-SPell-ABFE, distributed under the MIT License.
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Affiliation(s)
- Pengfei Li
- Single Particle, LLC Suzhou, Jiangsu 215000, China
| | - Tingting Pu
- Single Particle, LLC Suzhou, Jiangsu 215000, China
| | - Ye Mei
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
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8
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Tayfuroglu O, Zengin IN, Koca MS, Kocak A. DeepConf: Leveraging ANI-ML Potentials for Exploring Local Minima with Application to Bioactive Conformations. J Chem Inf Model 2025; 65:2818-2833. [PMID: 40033575 PMCID: PMC11938341 DOI: 10.1021/acs.jcim.4c02053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Revised: 02/21/2025] [Accepted: 02/24/2025] [Indexed: 03/05/2025]
Abstract
Here, we introduce a low energy conformer generation algorithm using ANI-ML potentials at the DFT accuracy and benchmark in reproducing bioactive conformations. We show that the method is efficient when the initial starting structure is far from equilibrium, when the ML potentials are stuck in nonsmooth regions, and when the quality of the conformers in a less conformer size is demanded. We specifically focus on conformations due to rotations around the single bonds. For the first time, we assess the performance of ANI-ML potentials using our conformer generation algorithm, DeepConf, in addition to previously reported Auto3D (J. Chem. Inf. Model. 2022, 62, 5373-5382) using the same potentials to reproduce bioactive conformations as well as providing a guideline for bioactive conformation evaluation processes. Our results show that the ANI-ML potentials can reproduce the bioactive conformations with mean value of the root-mean-square-deviation (RMSD) less than 0.5 Å, outperforming the limit of conventional methods. The code offers several features including but not limited to geometry optimization, fast conformer generations via single point energies (SPE), different minimization algorithms, different ML-potentials, or high-quality conformers in the smallest amount of ensemble sizes. It is available free of charge (documentation and test files) at https://github.com/otayfuroglu/DeepConf.
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Affiliation(s)
- Omer Tayfuroglu
- Department of Chemistry, Gebze
Technical University, 41400 Kocaeli, Turkey
| | - Irem N. Zengin
- Department of Chemistry, Gebze
Technical University, 41400 Kocaeli, Turkey
| | - M. Serdar Koca
- Department of Chemistry, Gebze
Technical University, 41400 Kocaeli, Turkey
| | - Abdulkadir Kocak
- Department of Chemistry, Gebze
Technical University, 41400 Kocaeli, Turkey
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9
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Angelo M, Zhang W, Vilseck J, Aoki S. In silico λ-dynamics predicts protein binding specificities to modified RNAs. Nucleic Acids Res 2025; 53:gkaf166. [PMID: 40066880 PMCID: PMC11894534 DOI: 10.1093/nar/gkaf166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 02/19/2025] [Accepted: 02/20/2025] [Indexed: 03/15/2025] Open
Abstract
RNA modifications shape gene expression through a variety of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.
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Affiliation(s)
- Murphy Angelo
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, United States
| | - Wen Zhang
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, United States
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, United States
| | - Jonah Z Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, United States
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Scott T Aoki
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, United States
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, United States
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10
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Zheng T, Wang A, Han X, Xia Y, Xu X, Zhan J, Liu Y, Chen Y, Wang Z, Wu X, Gong S, Yan W. Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage. Chem Sci 2025; 16:2730-2740. [PMID: 39802691 PMCID: PMC11721737 DOI: 10.1039/d4sc06640e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Accepted: 12/25/2024] [Indexed: 01/16/2025] Open
Abstract
A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanics' (MM) limited functional forms, which offers high computational efficiency. With the rapid expansion of synthetically accessible chemical space, traditional look-up table approaches face significant challenges. In this study, we address this issue using a modern data-driven approach, developing ByteFF, an Amber-compatible force field for drug-like molecules. To create ByteFF, we generated an expansive and highly diverse molecular dataset at the B3LYP-D3(BJ)/DZVP level of theory. This dataset includes 2.4 million optimized molecular fragment geometries with analytical Hessian matrices, along with 3.2 million torsion profiles. We then trained an edge-augmented, symmetry-preserving molecular graph neural network (GNN) on this dataset, employing a carefully optimized training strategy. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space. ByteFF demonstrates state-of-the-art performance on various benchmark datasets, excelling in predicting relaxed geometries, torsional energy profiles, and conformational energies and forces. Its exceptional accuracy and expansive chemical space coverage make ByteFF a valuable tool for multiple stages of computational drug discovery.
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Affiliation(s)
- Tianze Zheng
- ByteDance Research, Beijing Beijing 100098 China
| | - Ailun Wang
- ByteDance Research Bellevue Washington 98004 USA
| | - Xu Han
- ByteDance Research, Beijing Beijing 100098 China
| | - Yu Xia
- ByteDance Research, Beijing Beijing 100098 China
| | - Xingyuan Xu
- ByteDance Research, Beijing Beijing 100098 China
| | - Jiawei Zhan
- ByteDance Research Bellevue Washington 98004 USA
| | - Yu Liu
- ByteDance Research Bellevue Washington 98004 USA
| | - Yang Chen
- ByteDance Research, Beijing Beijing 100098 China
| | - Zhi Wang
- ByteDance Research Bellevue Washington 98004 USA
| | - Xiaojie Wu
- ByteDance Research Bellevue Washington 98004 USA
| | - Sheng Gong
- ByteDance Research Bellevue Washington 98004 USA
| | - Wen Yan
- ByteDance Research Bellevue Washington 98004 USA
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11
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Furui K, Shimizu T, Akiyama Y, Kimura SR, Terada Y, Ohue M. PairMap: An Intermediate Insertion Approach for Improving the Accuracy of Relative Free Energy Perturbation Calculations for Distant Compound Transformations. J Chem Inf Model 2025; 65:705-721. [PMID: 39800967 PMCID: PMC11776053 DOI: 10.1021/acs.jcim.4c01634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2024] [Revised: 12/19/2024] [Accepted: 12/23/2024] [Indexed: 01/28/2025]
Abstract
Accurate prediction of the difference in binding free energy between compounds is crucial for reducing the high costs associated with drug discovery. Relative binding free energy perturbation (RBFEP) calculations are effective for small structural changes; however, large topological changes pose significant challenges for calculations, leading to high errors and difficulties in convergence. To address such issues, we propose a new approach─PairMap─that focuses on introducing appropriate intermediates for complex transformations between two input compounds. PairMap-generated intermediates exhaustively, determined the optimal conversion paths, and introduced thermodynamic cycles into the perturbation map to improve accuracy and reduce computational cost. PairMap succeeded in introducing appropriate intermediates that could not be discovered by existing simple approaches by comprehensively considering intermediates. Furthermore, we evaluated the accuracy of the prediction of binding free energy using 9 compounds selected from Wang et al.'s benchmark set, which included particularly complex transformations. The perturbation map generated by PairMap achieved excellent accuracy with a mean absolute error of 0.93 kcal/mol compared to 1.70 kcal/mol when using the perturbation map generated by the conventional Flare FEP intermediate introduction method. Moreover, in a scaffold hopping experiment conducted with the PDE5a target involving complex transformations, PairMap provided more accurate free energy predictions than ABFEP calculations, yielding more reliable results compared to experimental data. Additionally, PairMap can be utilized to introduce intermediates into congeneric series, demonstrating that complex links on the perturbation map can be resolved with minimal addition of intermediates and links. In conclusion, PairMap overcomes the limitations of existing methods by enabling RBFEP calculations for more complex transformations, further streamlining lead optimization in drug discovery.
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Affiliation(s)
- Kairi Furui
- Department
of Computer Science, School of Computing, Institute of Science Tokyo, Yokohama 226-8501, Japan
| | | | - Yutaka Akiyama
- Department
of Computer Science, School of Computing, Institute of Science Tokyo, Tokyo 152-8550, Japan
| | | | | | - Masahito Ohue
- Department
of Computer Science, School of Computing, Institute of Science Tokyo, Yokohama 226-8501, Japan
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12
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Zhang S, Xie W, Yang Z, Xu S, Zhao Q, Lu Y, Zhang K, Yan Z, Chen J. Revealing the dissolution mechanism of organic carbonyl electrodes in lithium-organic batteries. Chem Sci 2025:d4sc07932a. [PMID: 39911342 PMCID: PMC11792205 DOI: 10.1039/d4sc07932a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2024] [Accepted: 01/23/2025] [Indexed: 02/07/2025] Open
Abstract
Organic carbonyl electrode materials (OCEMs) have shown great promise for high-performance lithium batteries due to their high capacity, renewability, and environmental friendliness. Nevertheless, the severe dissolution of these materials in conventional electrolytes results in poor cycling stability, which hinders their practical application. Herein, a unified model considering the effects of both ion-solvation structures and electrolyte solvents is proposed to elucidate the dissolution mechanism of OCEMs in electrolytes. In this new model, dissolution is driven by the interactions of OCEMs with ion-solvation structures and free (uncoordinated) solvents in electrolytes. In non-polar electrolytes, the strong interactions between OCEMs and Li-anion aggregates accelerate the dissolution of OCEMs, leading to anomalously high solubility of OCEMs. Conversely, the high dissolution in strongly polar electrolytes is dominated by the interaction with free solvents. This unified model transcends the conventional perspective that dissociation solely depends on the solute-solvent interactions. Based on this model, we propose that tuning the effects of ion-solvation structures and free solvents by altering solvent polarity could be an effective strategy for inhibiting the dissolution of organic electrodes to achieve long-cycle Li-organic batteries.
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Affiliation(s)
- Shu Zhang
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Weiwei Xie
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Zhuo Yang
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Shuo Xu
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Qi Zhao
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Yong Lu
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Kai Zhang
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Zhenhua Yan
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
| | - Jun Chen
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University Tianjin 300071 China
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13
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Bhati A, Wan S, Coveney PV. Equilibrium and Nonequilibrium Ensemble Methods for Accurate, Precise and Reproducible Absolute Binding Free Energy Calculations. J Chem Theory Comput 2025; 21:440-462. [PMID: 39680850 PMCID: PMC11736689 DOI: 10.1021/acs.jctc.4c01389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Revised: 11/28/2024] [Accepted: 12/02/2024] [Indexed: 12/18/2024]
Abstract
Free energy calculations for protein-ligand complexes have become widespread in recent years owing to several conceptual, methodological and technological advances. Central among these is the use of ensemble methods which permits accurate, precise and reproducible predictions and is necessary for uncertainty quantification. Absolute binding free energies (ABFEs) are challenging to predict using alchemical methods and their routine application in drug discovery has remained out of reach until now. Here, we apply ensemble alchemical ABFE methods to a large data set comprising 219 ligand-protein complexes and obtain statistically robust results with high accuracy (<1 kcal/mol). We compare equilibrium and nonequilibrium methods for ABFE predictions at large scale and provide a systematic critical assessment of each method. The equilibrium method is more accurate, precise, faster, computationally more cost-effective and requires a much simpler protocol, making it preferable for large scale and blind applications. We find that the calculated free energy distributions are non-normal and discuss the consequences. We recommend a definitive protocol to perform ABFE calculations optimally. Using this protocol, it is possible to perform thousands of ABFE calculations within a few hours on modern exascale machines.
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Affiliation(s)
- Agastya
P. Bhati
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom
| | - Shunzhou Wan
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom
| | - Peter V. Coveney
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom
- Computational
Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam 1012, The Netherlands
- Advanced
Research Computing Centre, University College
London, London WC1H 9BT, United Kingdom
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14
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Friedman AJ, Hsu WT, Shirts MR. Multiple Topology Replica Exchange of Expanded Ensembles for Multidimensional Alchemical Calculations. J Chem Theory Comput 2025; 21:230-240. [PMID: 39743749 PMCID: PMC11732712 DOI: 10.1021/acs.jctc.4c01268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Relative free energy (RFE) calculations are now widely used in academia and the industry, but their accuracy is often limited by poor sampling of the complexes' conformational ensemble. To help address conformational sampling problems when simulating many relative binding free energies, we developed a novel method termed multiple topology replica exchange of expanded ensembles (MT-REXEE). This method enables parallel expanded ensemble calculations, facilitating iterative RFE computations while allowing conformational exchange between parallel transformations. These iterative transformations can be adaptable to any set of systems with a common backbone or central substructure. We demonstrate that the MT-REXEE method maintains thermodynamic cycle closure to the same extent as standard expanded ensemble calculations for both solvation free energy and relative binding free energy calculations. The transformations tested involve systems that incorporate diverse heavy atoms and multisite perturbations of a small molecule core resembling multisite λ dynamics, without necessitating modifications to the MD code. Our initial implementation is in GROMACS. We outline a systematic approach for the topology setup and provide instructions on how to perform inter-replica coordinate modifications. This work shows that MT-REXEE can be used to perform accurate and reproducible free energy estimates and prompts expansion to more complex test systems and other molecular dynamics simulation infrastructures.
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Affiliation(s)
- Anika J Friedman
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Wei-Tse Hsu
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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15
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Zhang BW, Fajer M, Chen W, Moraca F, Wang L. Leveraging the Thermodynamics of Protein Conformations in Drug Discovery. J Chem Inf Model 2025; 65:252-264. [PMID: 39681511 DOI: 10.1021/acs.jcim.4c01612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2024]
Abstract
As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.
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Affiliation(s)
- Bin W Zhang
- Schrödinger Inc., 1540 Broadway, 24th Floor, New York, New York 10036-4041, United States
| | - Mikolai Fajer
- Schrödinger Inc., 1540 Broadway, 24th Floor, New York, New York 10036-4041, United States
| | - Wei Chen
- Schrödinger Inc., 1540 Broadway, 24th Floor, New York, New York 10036-4041, United States
| | - Francesca Moraca
- Schrödinger Inc., 1540 Broadway, 24th Floor, New York, New York 10036-4041, United States
| | - Lingle Wang
- Schrödinger Inc., 1540 Broadway, 24th Floor, New York, New York 10036-4041, United States
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16
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Liesen M, Vilseck JZ. Superimposing Ligands with a Ligand Overlay as an Alternate Topology Model for λ-Dynamics-Based Calculations. J Phys Chem B 2024; 128:11359-11368. [PMID: 39515788 PMCID: PMC11587946 DOI: 10.1021/acs.jpcb.4c04805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 10/10/2024] [Accepted: 10/15/2024] [Indexed: 11/16/2024]
Abstract
Alchemical free energy (AFE) calculations can predict binding affinity changes as a function of structural modifications and have become powerful tools for lead optimization and drug discovery. Central to the setup and performance of AFE calculations is the manner of mapping alchemical transformations, known as the topology model. Single, dual, and hybrid topology models have been used with various AFE methods in the field. In recent works, λ-dynamics (λD) free energy calculations, specifically, have preferred the use of a hybrid multiple topology (HMT) for sampling multiple ligand perturbations. In this work, we evaluate a new topology method called ligand overlay (LO) for use with λD-based calculations, including the recently introduced λ-dynamics with a bias-updated Gibbs sampling (LaDyBUGS) approach. LO is a full multiple topology model that allows entire ligands to be sampled and restrained within a λ-dynamics framework. Relative binding free energies were computed with HMT or LO topology models with LaDyBUGS for 45 ligands across five protein benchmark systems. An overall Pearson R correlation of 0.98 and mean unsigned error of 0.32 kcal/mol were observed, suggesting that LO is a viable alternative topology model for λD-based calculations. We discuss the merits of using an HMT or LO model for future ligand studies with λD or LaDyBUGS calculations.
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Affiliation(s)
- Michael
P. Liesen
- Department
of Biochemistry and Molecular Biology, Indiana
University School of Medicine, Indianapolis, Indiana 46202, United States
- Center
for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Jonah Z. Vilseck
- Department
of Biochemistry and Molecular Biology, Indiana
University School of Medicine, Indianapolis, Indiana 46202, United States
- Center
for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
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17
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Molani F, Cho AE. Accurate protein-ligand binding free energy estimation using QM/MM on multi-conformers predicted from classical mining minima. Commun Chem 2024; 7:247. [PMID: 39468282 PMCID: PMC11519471 DOI: 10.1038/s42004-024-01328-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 10/14/2024] [Indexed: 10/30/2024] Open
Abstract
Accurate prediction of binding free energy is crucial for the rational design of drug candidates and understanding protein-ligand interactions. To address this, we have developed four protocols that combine QM/MM calculations and the mining minima (M2) method, tested on 9 targets and 203 ligands. Our protocols carry out free energy processing with or without conformational search on the selected conformers obtained from M2 calculations, where their force field atomic charge parameters are substituted with those obtained from a QM/MM calculation. The method achieved a high Pearson's correlation coefficient (0.81) with experimental binding free energies across diverse targets, demonstrating its generality. Using a differential evolution algorithm with a universal scaling factor of 0.2, we achieved a low mean absolute error of 0.60 kcal mol-1. This performance surpasses many existing methods and is comparable to popular relative binding free energy techniques but at significantly lower computational cost.
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Affiliation(s)
- Farzad Molani
- Department of Bioinformatics, Korea University, Sejong, Korea
| | - Art E Cho
- Department of Bioinformatics, Korea University, Sejong, Korea.
- inCerebro Co. Ltd., Gangnam-gu, Seoul, Korea.
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18
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Musleh S, Alibay I, Biggin PC, Bryce RA. Analysis of Glycan Recognition by Concanavalin A Using Absolute Binding Free Energy Calculations. J Chem Inf Model 2024; 64:8063-8073. [PMID: 39413277 PMCID: PMC11523069 DOI: 10.1021/acs.jcim.4c01088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 10/03/2024] [Accepted: 10/03/2024] [Indexed: 10/18/2024]
Abstract
Carbohydrates are key biological mediators of molecular recognition and signaling processes. In this case study, we explore the ability of absolute binding free energy (ABFE) calculations to predict the affinities of a set of five related carbohydrate ligands for the lectin protein, concanavalin A, ranging from 27-atom monosaccharides to a 120-atom complex-type N-linked glycan core pentasaccharide. ABFE calculations quantitatively rank and estimate the affinity of the ligands in relation to microcalorimetry, with a mean signed error in the binding free energy of -0.63 ± 0.04 kcal/mol. Consequently, the diminished binding efficiencies of the larger carbohydrate ligands are closely reproduced: the ligand efficiency values from isothermal titration calorimetry for the glycan core pentasaccharide and its constituent trisaccharide and monosaccharide compounds are respectively -0.14, -0.22, and -0.41 kcal/mol per heavy atom. ABFE calculations predict these ligand efficiencies to be -0.14 ± 0.02, -0.24 ± 0.03, and -0.46 ± 0.06 kcal/mol per heavy atom, respectively. Consequently, the ABFE method correctly identifies the high affinity of the key anchoring mannose residue and the negligible contribution to binding of both β-GlcNAc arms of the pentasaccharide. While challenges remain in sampling the conformation and interactions of these polar, flexible, and weakly bound ligands, we nevertheless find that the ABFE method performs well for this lectin system. The approach shows promise as a quantitative tool for predicting and deconvoluting carbohydrate-protein interactions, with potential application to design of therapeutics, vaccines, and diagnostics.
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Affiliation(s)
- Sondos Musleh
- Division
of Pharmacy and Optometry, The University
of Manchester, Manchester M13 9PT, U.K.
- Department
of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
| | - Irfan Alibay
- Open Free
Energy, Open Molecular Software Foundation, Davis, California 95616, United States
- Structural
Bioinformatics and Computational Biochemistry, Department of Biochemistry, The University of Oxford, South Parks Road, Oxford OX1 3QU, U.K.
| | - Philip C. Biggin
- Structural
Bioinformatics and Computational Biochemistry, Department of Biochemistry, The University of Oxford, South Parks Road, Oxford OX1 3QU, U.K.
| | - Richard A. Bryce
- Division
of Pharmacy and Optometry, The University
of Manchester, Manchester M13 9PT, U.K.
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19
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Qian R, Xue J, Xu Y, Huang J. Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery. J Chem Inf Model 2024; 64:7214-7237. [PMID: 39360948 DOI: 10.1021/acs.jcim.4c01024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.
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Affiliation(s)
- Runtong Qian
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Xue
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - You Xu
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Huang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
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20
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Min Y, Wei Y, Wang P, Wang X, Li H, Wu N, Bauer S, Zheng S, Shi Y, Wang Y, Wu J, Zhao D, Zeng J. From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2405404. [PMID: 39206846 PMCID: PMC11516055 DOI: 10.1002/advs.202405404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/29/2024] [Indexed: 09/04/2024]
Abstract
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.
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Affiliation(s)
- Yaosen Min
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Ye Wei
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Peizhuo Wang
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
- School of Life Science and TechnologyXidian UniversityXi'an710071ShaanxiChina
| | - Xiaoting Wang
- School of MedicineTsinghua UniversityBeijing100084China
| | - Han Li
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Nian Wu
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Stefan Bauer
- Department of Intelligent SystemsKTHStockholm10044Sweden
| | | | - Yu Shi
- Microsoft Research AsiaBeijing100080China
| | - Yingheng Wang
- Department of Electrical EngineeringTsinghua UniversityBeijing100084China
| | - Ji Wu
- Department of Electrical EngineeringTsinghua UniversityBeijing100084China
| | - Dan Zhao
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Jianyang Zeng
- School of EngineeringWestlake UniversityHangzhou310030China
- Research Center for Industries of the FutureWestlake UniversityHangzhou310030China
- Present address:
Westlake Laboratory of Life Sciences and BiomedicineWestlake UniversityHangzhou310024China
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21
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Pala D, Clark DE. Caught between a ROCK and a hard place: current challenges in structure-based drug design. Drug Discov Today 2024; 29:104106. [PMID: 39029868 DOI: 10.1016/j.drudis.2024.104106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/27/2024] [Accepted: 07/13/2024] [Indexed: 07/21/2024]
Abstract
The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.
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Affiliation(s)
- Daniele Pala
- Medicinal Chemistry and Drug Design Technologies Department, Chiesi Farmaceutici S.p.A, Research Center, Largo Belloli 11/a, 43122 Parma, Italy
| | - David E Clark
- Charles River, 6-9 Spire Green Centre, Flex Meadow, Harlow CM19 5TR, UK.
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22
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Tan S, Gong X, Liu H, Yao X. Identification of novel LRRK2 inhibitors by structure-based virtual screening and alchemical free energy calculation. Phys Chem Chem Phys 2024; 26:19775-19786. [PMID: 38984923 DOI: 10.1039/d4cp01762e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
The Leucine-rich repeat kinase 2 (LRRK2) target has been identified as a promising drug target for Parkinson's disease (PD) treatment. This study focuses on optimizing the activity of LRRK2 inhibitors using alchemical relative binding free energy (RBFE) calculations. Initially, we assessed various free energy calculation methods across different LRRK2 kinase inhibitor scaffolds. The results indicate that alchemical free energy calculations are promising for prospective predictions on LRRK2 inhibitors, especially for the aminopyrimidine scaffold with an RMSE of 1.15 kcal mol-1 and Rp of 0.83. Following this, we optimized a potent LRRK2 kinase inhibitor identified from previous virtual screenings, featuring a novel scaffold. Guided by RBFE predictions using alchemical methods, this optimization led to the discovery of compound LY2023-001. This compound, with a [1,2,4]triazolo[5,6-b]indole scaffold, exhibited enhanced inhibitory activity against G2019S LRRK2 (IC50 = 12.9 nM). Molecular dynamics (MD) simulations revealed that LY2023-001 formed stable hydrogen bonds with Glu1948, and Ala1950 in the G2019S LRRK2 protein. Additionally, its phenyl substituents engage in strong electrostatic interactions with Lys1906 and van der Waals interactions with Leu1885, Phe1890, Val1893, Ile1933, Met1947, Leu1949, Leu2001, Ala2016, and Asp2017. Our findings underscore the potential of computational methods in the successful optimization of small molecules, offering important insights for the development of novel LRRK2 inhibitors.
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Affiliation(s)
- Shuoyan Tan
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xiaoqing Gong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China.
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China.
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China.
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23
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Lagardère L, Maurin L, Adjoua O, El Hage K, Monmarché P, Piquemal JP, Hénin J. Lambda-ABF: Simplified, Portable, Accurate, and Cost-Effective Alchemical Free-Energy Computation. J Chem Theory Comput 2024; 20:4481-4498. [PMID: 38805379 DOI: 10.1021/acs.jctc.3c01249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
We introduce the lambda-Adaptive Biasing Force (lambda-ABF) method for the computation of alchemical free-energy differences. We propose a software implementation and showcase it on biomolecular systems. The method arises from coupling multiple-walker adaptive biasing force with λ-dynamics. The sampling of the alchemical variable is continuous and converges toward a uniform distribution, making manual optimization of the λ schedule unnecessary. Contrary to most other approaches, alchemical free-energy estimates are obtained immediately without any postprocessing. Free diffusion of λ improves orthogonal relaxation compared to fixed-λ thermodynamic integration or free-energy perturbation. Furthermore, multiple walkers provide generic orthogonal space coverage with minimal user input and negligible computational overhead. We show that our high-performance implementations coupling the Colvars library with NAMD and Tinker-HP can address real-world cases including ligand-receptor binding with both fixed-charge and polarizable models, with a demonstrably richer sampling than fixed-λ methods. The implementation is fully open-source, publicly available, and readily usable by practitioners of current alchemical methods. Thanks to the portable Colvars library, lambda-ABF presents a unified user interface regardless of the back-end (NAMD, Tinker-HP, or any software to be interfaced in the future), sparing users the effort of learning multiple interfaces. Finally, the Colvars Dashboard extension of the visual molecular dynamics (VMD) software provides an interactive monitoring and diagnostic tool for lambda-ABF simulations.
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Affiliation(s)
- Louis Lagardère
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Sorbonne Université, Institut Parisien de Chimie Physique et Théorique, FR2622 CNRS, 75005 Paris, France
- Qubit Pharmaceuticals, 29 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Lise Maurin
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Sorbonne Université, Laboratoire Jacques-Louis Lions, UMR 7589 CNRS, 75005 Paris, France
| | - Olivier Adjoua
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
| | - Krystel El Hage
- Qubit Pharmaceuticals, 29 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Pierre Monmarché
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Sorbonne Université, Laboratoire Jacques-Louis Lions, UMR 7589 CNRS, 75005 Paris, France
| | - Jean-Philip Piquemal
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Qubit Pharmaceuticals, 29 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Jérôme Hénin
- Laboratoire de Biochimie Théorique, Université Paris Cité, CNRS, UPR 9080, 75005 Paris, France
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24
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Zhang S, Giese TJ, Lee TS, York DM. Alchemical Enhanced Sampling with Optimized Phase Space Overlap. J Chem Theory Comput 2024; 20:3935-3953. [PMID: 38666430 PMCID: PMC11157682 DOI: 10.1021/acs.jctc.4c00251] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2024]
Abstract
An alchemical enhanced sampling (ACES) method has recently been introduced to facilitate importance sampling in free energy simulations. The method achieves enhanced sampling from Hamiltonian replica exchange within a dual topology framework while utilizing new smoothstep softcore potentials. A common sampling problem encountered in lead optimization is the functionalization of aromatic rings that exhibit distinct conformational preferences when interacting with the protein. It is difficult to converge the distribution of ring conformations due to the long time scale of ring flipping events; however, the ACES method addresses this issue by modeling the syn and anti ring conformations within a dual topology. ACES thereby samples the conformer distributions by alchemically tunneling between states, as opposed to traversing a physical pathway with a high rotational barrier. We demonstrate the use of ACES to overcome conformational sampling issues involving ring flipping in ML300-derived noncovalent inhibitors of SARS-CoV-2 Main Protease (Mpro). The demonstrations explore how the use of replica exchange and the choice of softcore selection affects the convergence of the ring conformation distributions. Furthermore, we examine how the accuracy of the calculated free energies is affected by the degree of phase space overlap (PSO) between adjacent states (i.e., between neighboring λ-windows) and the Hamiltonian replica exchange acceptance ratios. Both of these factors are sensitive to the spacing between the intermediate states. We introduce a new method for choosing a schedule of λ values. The method analyzes short "burn-in" simulations to construct a 2D map of the nonlocal PSO. The schedule is obtained by optimizing an alchemical pathway on the 2D map that equalizes the PSO between the λ intervals. The optimized phase space overlap λ-spacing method (Opt-PSO) leads to more numerous end-to-end single passes and round trips due to the correlation between PSO and Hamiltonian replica exchange acceptance ratios. The improved exchange statistics enhance the efficiency of ACES method. The method has been implemented into the FE-ToolKit software package, which is freely available.
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Affiliation(s)
- Shi Zhang
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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25
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Chéron N. Binding Sites of Bicarbonate in Phosphoenolpyruvate Carboxylase. J Chem Inf Model 2024; 64:3375-3385. [PMID: 38533570 DOI: 10.1021/acs.jcim.3c01830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Phosphoenolpyruvate carboxylase (PEPC) is used in plant metabolism for fruit maturation or seed development as well as in the C4 and crassulacean acid metabolism (CAM) mechanisms in photosynthesis, where it is used for the capture of hydrated CO2 (bicarbonate). To find the yet unknown binding site of bicarbonate in this enzyme, we have first identified putative binding sites with nonequilibrium molecular dynamics simulations and then ranked these sites with alchemical free energy calculations with corrections of computational artifacts. Fourteen pockets where bicarbonate could bind were identified, with three having realistic binding free energies with differences with the experimental value below 1 kcal/mol. One of these pockets is found far from the active site at 14 Å and predicted to be an allosteric binding site. In the two other binding sites, bicarbonate is in direct interaction with the magnesium ion; neither sequence alignment nor the study of mutant K606N allowed to discriminate between these two pockets, and both are good candidates as the binding site of bicarbonate in phosphoenolpyruvate carboxylase.
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Affiliation(s)
- Nicolas Chéron
- PASTEUR, Département de chimie, École normale supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
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26
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Cournia Z, Chipot C. Applications of Free-Energy Calculations to Biomolecular Processes. A Collection. J Phys Chem B 2024; 128:3299-3301. [PMID: 38600851 DOI: 10.1021/acs.jpcb.4c01283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Affiliation(s)
- Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n◦7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy Cedex, France
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 East 57th Street W225, Chicago, Illinois 60637, United States
- Theoretical and Computational Biophysics Group, Beckman Institute, and Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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27
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Cournia Z, Chipot C. Applications of Free-Energy Calculations to Biomolecular Processes. A Collection. J Chem Inf Model 2024; 64:2129-2131. [PMID: 38587007 DOI: 10.1021/acs.jcim.4c00349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Affiliation(s)
- Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n◦7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy Cedex, France
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 East 57th Street W225, Chicago, Illinois 60637, United States
- Theoretical and Computational Biophysics Group, Beckman Institute, and Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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28
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Luo D, Liu D, Qu X, Dong L, Wang B. Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning. J Chem Inf Model 2024; 64:1892-1906. [PMID: 38441880 DOI: 10.1021/acs.jcim.3c01961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.
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Affiliation(s)
- Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Dandan Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Xiaoyang Qu
- School of Pharmacy and Medical Technology, Putian University, Putian 351100, P. R. China
- Key Laboratory of Pharmaceutical Analysis and Laboratory Medicine (Putian University), Fujian Province University, Putian 351100, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, P. R. China
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29
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Ries B, Alibay I, Swenson DWH, Baumann HM, Henry MM, Eastwood JRB, Gowers RJ. Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations. J Chem Theory Comput 2024; 20:1862-1877. [PMID: 38330251 PMCID: PMC10941767 DOI: 10.1021/acs.jctc.3c01206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.
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Affiliation(s)
- Benjamin Ries
- Medicinal
Chemistry, Boehringer Ingelheim Pharma GmbH
& Co KG, Birkendorfer Str 65, 88397 Biberach an der Riss, Germany
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Irfan Alibay
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - David W. H. Swenson
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Hannah M. Baumann
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Michael M. Henry
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
- Computational
and Systems Biology Program, Sloan Kettering
Institute, Memorial Sloan Kettering Cancer Center, New York, 1275 New York, United States
| | - James R. B. Eastwood
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Richard J. Gowers
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
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30
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Beck TL, Carloni P, Asthagiri DN. All-Atom Biomolecular Simulation in the Exascale Era. J Chem Theory Comput 2024; 20:1777-1782. [PMID: 38382017 DOI: 10.1021/acs.jctc.3c01276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.
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Affiliation(s)
- Thomas L Beck
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Paolo Carloni
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- Department of Physics, RWTH Aachen University, D-52078 Aachen, Germany
| | - Dilipkumar N Asthagiri
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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31
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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32
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Wu N, Zhang R, Peng X, Fang L, Chen K, Jestilä JS. Elucidation of protein-ligand interactions by multiple trajectory analysis methods. Phys Chem Chem Phys 2024; 26:6903-6915. [PMID: 38334015 DOI: 10.1039/d3cp03492e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
The identification of interaction between protein and ligand including binding positions and strength plays a critical role in drug discovery. Molecular docking and molecular dynamics (MD) techniques have been widely applied to predict binding positions and binding affinity. However, there are few works that describe the systematic exploration of the MD trajectory evolution in this context, potentially leaving out important information. To address the problem, we build a framework, Moira (molecular dynamics trajectory analysis), which enables automating the whole process ranging from docking, MD simulations and various analyses as well as visualizations. We utilized Moira to analyze 400 MD simulations in terms of their geometric features (root mean square deviation and protein-ligand interaction profiler) and energetics (molecular mechanics Poisson-Boltzmann surface area) for these trajectories. Finally, we demonstrate the performance of different analysis techniques in distinguishing native poses among four poses.
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Affiliation(s)
- Nian Wu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
| | - Ruotian Zhang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
| | - Xingang Peng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
| | - Lincan Fang
- Department of Applied Physics, Aalto University, Espoo, Finland
| | - Kai Chen
- Institute of Catalysis, Zhejiang University, Hanghzhou, China
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33
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Zhou M, Zhao F, Yu L, Liu J, Wang J, Zhang JZH. An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1. Molecules 2024; 29:881. [PMID: 38398632 PMCID: PMC10892774 DOI: 10.3390/molecules29040881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.
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Affiliation(s)
- Mengchen Zhou
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
| | - Fanyu Zhao
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
| | - Lan Yu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jian Wang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
- Department of Chemistry, New York University, New York, NY 10003, USA
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China
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34
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Hu R, Zhang J, Kang Y, Wang Z, Pan P, Deng Y, Hsieh CY, Hou T. Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization. J Chem Theory Comput 2024; 20:1465-1478. [PMID: 38300792 DOI: 10.1021/acs.jctc.3c01154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.
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Affiliation(s)
- Renling Hu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Polytechnic Institute, Zhejiang University, Hangzhou 310058, Zhejiang, China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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35
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Sulimov AV, Ilin IS, Tashchilova AS, Kondakova OA, Kutov DC, Sulimov VB. Docking and other computing tools in drug design against SARS-CoV-2. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:91-136. [PMID: 38353209 DOI: 10.1080/1062936x.2024.2306336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.
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Affiliation(s)
- A V Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - I S Ilin
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - A S Tashchilova
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - O A Kondakova
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - D C Kutov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - V B Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
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36
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Angelo M, Zhang W, Vilseck JZ, Aoki ST. In silico λ-dynamics predicts protein binding specificities to modified RNAs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577511. [PMID: 38328125 PMCID: PMC10849657 DOI: 10.1101/2024.01.26.577511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.
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Affiliation(s)
- Murphy Angelo
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
| | - Wen Zhang
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA
| | - Jonah Z. Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Scott T. Aoki
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA
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37
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Herz AM, Kellici T, Morao I, Michel J. Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities. Methods Mol Biol 2024; 2716:241-264. [PMID: 37702943 DOI: 10.1007/978-1-0716-3449-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.
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Affiliation(s)
- Anna M Herz
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK
| | - Tahsin Kellici
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
- Merck & Co., Inc., Modelling and Informatics, West Point, PA, USA
| | - Inaki Morao
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK.
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38
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Robo MT, Hayes RL, Ding X, Pulawski B, Vilseck JZ. Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling. Nat Commun 2023; 14:8515. [PMID: 38129400 PMCID: PMC10740020 DOI: 10.1038/s41467-023-44208-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol-1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol-1 or less). Notably, large efficiency gains over thermodynamic integration of 18-66-fold for small perturbations and 100-200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated.
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Affiliation(s)
- Michael T Robo
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Biosciences Research Institute, 1210 Waterway Blvd Ste. 2000, Indianapolis, IN, 46202, USA
| | - Ryan L Hayes
- Chemical and Biomolecular Engineering, University of California, Irvine, California, 92617, USA
- Pharmaceutical Sciences, University of California, Irvine, CA, 92617, USA
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Chemistry, Tufts University, Medford, MA, 02144, USA
| | - Brian Pulawski
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Jonah Z Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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39
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Fu H, Chipot C, Shao X, Cai W. Standard Binding Free-Energy Calculations: How Far Are We from Automation? J Phys Chem B 2023; 127:10459-10468. [PMID: 37824848 DOI: 10.1021/acs.jpcb.3c04370] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.
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Affiliation(s)
- Haohao Fu
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Christophe Chipot
- Laboratoire International Associé CNRS and University of Illinois at Urbana-Champaign, UMR no. 7019, Université de Lorraine, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
- Department of Chemistry, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Hawai'i at Ma̅noa, 2545 McCarthy Mall, Honolulu, Hawaii 96822, United States
| | - Xueguang Shao
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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40
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York DM. Modern Alchemical Free Energy Methods for Drug Discovery Explained. ACS PHYSICAL CHEMISTRY AU 2023; 3:478-491. [PMID: 38034038 PMCID: PMC10683484 DOI: 10.1021/acsphyschemau.3c00033] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 12/02/2023]
Abstract
This Perspective provides a contextual explanation of the current state-of-the-art alchemical free energy methods and their role in drug discovery as well as highlights select emerging technologies. The narrative attempts to answer basic questions about what goes on "under the hood" in free energy simulations and provide general guidelines for how to run simulations and analyze the results. It is the hope that this work will provide a valuable introduction to students and scientists in the field.
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Affiliation(s)
- Darrin M. York
- Laboratory for Biomolecular
Simulation Research, Institute for Quantitative Biomedicine, and Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway, New Jersey 08854, United States
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41
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Case D, Aktulga HM, Belfon K, Cerutti DS, Cisneros GA, Cruzeiro VD, Forouzesh N, Giese TJ, Götz AW, Gohlke H, Izadi S, Kasavajhala K, Kaymak MC, King E, Kurtzman T, Lee TS, Li P, Liu J, Luchko T, Luo R, Manathunga M, Machado MR, Nguyen HM, O’Hearn KA, Onufriev AV, Pan F, Pantano S, Qi R, Rahnamoun A, Risheh A, Schott-Verdugo S, Shajan A, Swails J, Wang J, Wei H, Wu X, Wu Y, Zhang S, Zhao S, Zhu Q, Cheatham TE, Roe DR, Roitberg A, Simmerling C, York DM, Nagan MC, Merz KM. AmberTools. J Chem Inf Model 2023; 63:6183-6191. [PMID: 37805934 PMCID: PMC10598796 DOI: 10.1021/acs.jcim.3c01153] [Citation(s) in RCA: 573] [Impact Index Per Article: 286.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Indexed: 10/10/2023]
Abstract
AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.
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Affiliation(s)
- David
A. Case
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Hasan Metin Aktulga
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Kellon Belfon
- FOG
Pharmaceuticals Inc., Cambridge 02140, Massachusetts, United States
| | - David S. Cerutti
- Psivant, 451 D Street, Suite 205, Boston 02210, Massachusetts, United States
| | - G. Andrés Cisneros
- Department
of Physics, Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson 75801, Texas, United States
| | - Vinícius
Wilian D. Cruzeiro
- Department
of Chemistry and The PULSE Institute, Stanford
University, Stanford 94305, California, United States
| | - Negin Forouzesh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Timothy J. Giese
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Andreas W. Götz
- San
Diego Supercomputer Center, University of
California San Diego, La Jolla 92093-0505, California, United States
| | - Holger Gohlke
- Institute
for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Saeed Izadi
- Pharmaceutical
Development, Genentech, Inc., South San Francisco 94080, California, United
States
| | - Koushik Kasavajhala
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Mehmet C. Kaymak
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Edward King
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Tom Kurtzman
- Ph.D.
Programs in Chemistry, Biochemistry, and Biology, The Graduate Center of the City University of New York, 365 Fifth Avenue, New York 10016, New York, United States
- Department
of Chemistry, Lehman College, 250 Bedford Park Blvd West, Bronx 10468, New York, United States
| | - Tai-Sung Lee
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Pengfei Li
- Department
of Chemistry and Biochemistry, Loyola University
Chicago, Chicago 60660, Illinois, United States
| | - Jian Liu
- Beijing
National Laboratory for Molecular Sciences, Institute of Theoretical
and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Tyler Luchko
- Department
of Physics and Astronomy, California State
University, Northridge, Northridge 91330, California, United States
| | - Ray Luo
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Madushanka Manathunga
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | | | - Hai Minh Nguyen
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Kurt A. O’Hearn
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Alexey V. Onufriev
- Departments
of Computer Science and Physics, Virginia
Tech, Blacksburg 24061, Virginia, United
States
| | - Feng Pan
- Department
of Statistics, Florida State University, Tallahassee 32304, Florida, United States
| | - Sergio Pantano
- Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Ruxi Qi
- Cryo-EM
Center, Southern University of Science and
Technology, Shenzhen 518055, China
| | - Ali Rahnamoun
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Ali Risheh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Stephan Schott-Verdugo
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Akhil Shajan
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | - Jason Swails
- Entos, 4470 W Sunset
Blvd, Suite 107, Los Angeles 90027, California, United States
| | - Junmei Wang
- Department
of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh 15261, Pennsylvania, United States
| | - Haixin Wei
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Xiongwu Wu
- Laboratory
of Computational Biology, NHLBI, NIH, Bethesda 20892, Maryland, United States
| | - Yongxian Wu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Shi Zhang
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Shiji Zhao
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
- Nurix Therapeutics, Inc., San Francisco 94158, California, United States
| | - Qiang Zhu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Thomas E. Cheatham
- Department
of Medicinal Chemistry, The University of
Utah, 30 South 2000 East, Salt Lake City 84112, Utah, United
States
| | - Daniel R. Roe
- Laboratory
of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda 20892, Maryland, United States
| | - Adrian Roitberg
- Department
of Chemistry, The University of Florida, 440 Leigh Hall, Gainesville 32611-7200, Florida, United States
| | - Carlos Simmerling
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Darrin M. York
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Maria C. Nagan
- Department
of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Kenneth M. Merz
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
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42
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Gracia Carmona O, Oostenbrink C. Flexible Gaussian Accelerated Molecular Dynamics to Enhance Biological Sampling. J Chem Theory Comput 2023; 19:6521-6531. [PMID: 37649349 PMCID: PMC10536968 DOI: 10.1021/acs.jctc.3c00619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Indexed: 09/01/2023]
Abstract
Molecular dynamics simulations often struggle to obtain sufficient sampling to study complex molecular events due to high energy barriers separating the minima of interest. Multiple enhanced sampling techniques have been developed and improved over the years to tackle this issue. Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that works by adding a biasing potential, lifting the energy landscape up, and decreasing the height of its barriers. GaMD allows one to increase the sampling of events of interest without the need of a priori knowledge of the system or the relevant coordinates. All required acceleration parameters can be obtained from a previous search run. Upon its development, several improvements for the methodology have been proposed, among them selective GaMD in which the boosting potential is selectively applied to the region of interest. There are currently four selective GaMD methods that have shown promising results. However, all of these methods are constrained on the number, location, and scenarios in which this selective boosting potential can be applied to ligands, peptides, or protein-protein interactions. In this work, we showcase a GROMOS implementation of the GaMD methodology with a fully flexible selective GaMD approach that allows the user to define, in a straightforward way, multiple boosting potentials for as many regions as desired. We show and analyze the advantages of this flexible selective approach on two previously used test systems, the alanine dipeptide and the chignolin peptide, and extend these examples to study its applicability and potential to study conformational changes of glycans and glycosylated proteins.
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Affiliation(s)
- Oriol Gracia Carmona
- Institute
for Molecular Modeling and Simulation, Department of Material Sciences
and Process Engineering, University of Natural
Resources and Life Sciences, Vienna. Muthgasse 18, 1190 Vienna, Austria
| | - Chris Oostenbrink
- Institute
for Molecular Modeling and Simulation, Department of Material Sciences
and Process Engineering, University of Natural
Resources and Life Sciences, Vienna. Muthgasse 18, 1190 Vienna, Austria
- Christian
Doppler Laboratory for Molecular Informatics in the Biosciences, University of Natural Resources and Life Sciences, Vienna. Muthgasse 18, 1190 Vienna, Austria
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43
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Lapierre J, Hub JS. Converging PMF Calculations of Antibiotic Permeation across an Outer Membrane Porin with Subkilocalorie per Mole Accuracy. J Chem Inf Model 2023; 63:5319-5330. [PMID: 37560945 DOI: 10.1021/acs.jcim.3c00880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
The emergence of multidrug-resistant pathogens led to a critical need for new antibiotics. A key property of effective antibiotics against Gram-negative bacteria is their ability to permeate through the bacterial outer membrane via transmembrane porin proteins. Molecular dynamics (MD) simulations are, in principle, capable of modeling antibiotic permeation across outer membrane porins (OMPs). However, owing to sampling problems, it has remained challenging to obtain converged potentials of mean force (PMFs) for antibiotic permeation across OMPs. Here, we investigated the convergence of PMFs along a single collective variable aimed at quantifying the permeation of the antibiotic fosmidomycin across the OprO porin. We compared standard umbrella sampling (US) with three advanced flavors of the US technique: (i) Hamiltonian replica exchange with solute tempering in combination with US, (ii) simulated tempering-enhanced US, and (iii) replica-exchange US. To quantify the PMF convergence and to reveal hysteresis problems, we computed several independent sets of US simulations starting from pulling simulations in the outward and inward permeation directions. We find that replica-exchange US in combination with well-chosen restraints is highly successful for obtaining converged PMFs of fosmidomycin permeation through OprO, reaching PMFs converged to subkilocalorie per mole accuracy.
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Affiliation(s)
- Jeremy Lapierre
- Theoretical Physics and Center for Biophysics, Saarland University, Saarbrücken 66123, Germany
| | - Jochen S Hub
- Theoretical Physics and Center for Biophysics, Saarland University, Saarbrücken 66123, Germany
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44
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Lockhart C, Luo X, Olson A, Delfing BM, Laracuente XE, Foreman KW, Paige M, Kehn-Hall K, Klimov DK. Can Free Energy Perturbation Simulations Coupled with Replica-Exchange Molecular Dynamics Study Ligands with Distributed Binding Sites? J Chem Inf Model 2023; 63:4791-4802. [PMID: 37531558 PMCID: PMC10947611 DOI: 10.1021/acs.jcim.3c00631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
Free energy perturbation coupled with replica exchange with solute tempering (FEP/REST) offers a rigorous approach to compute relative free energy changes for ligands. To determine the applicability of FEP/REST for the ligands with distributed binding poses, we considered two alchemical transformations involving three putative inhibitors I0, I1, and I2 of the Venezuelan equine encephalitis virus nuclear localization signal sequence binding to the importin-α (impα) transporter protein. I0 → I1 and I0 → I2 transformations, respectively, increase or decrease the polarity of the parent molecule. Our objective was three-fold─(i) to verify FEP/REST technical performance and convergence, (ii) to estimate changes in binding free energy ΔΔG, and (iii) to determine the utility of FEP/REST simulations for conformational binding analysis. Our results are as follows. First, our FEP/REST implementation properly follows FEP/REST formalism and produces converged ΔΔG estimates. Due to ligand inherent unbinding, the better FEP/REST strategy lies in performing multiple independent trajectories rather than extending their length. Second, I0 → I1 and I0 → I2 transformations result in overall minor changes in inhibitor binding free energy, slightly strengthening the affinity of I1 and weakening that of I2. Electrostatic interactions dominate binding interactions, determining the enthalpic changes. The two transformations cause opposite entropic changes, which ultimately govern binding affinities. Importantly, we confirm the validity of FEP/REST free energy estimates by comparing them with our previous REST simulations, directly probing binding of three ligands to impα. Third, we established that FEP/REST simulations can sample binding ensembles of ligands. Thus, FEP/REST can be applied (i) to study the energetics of the ligand binding without defined poses and showing minor differences in affinities |ΔΔG| ≲ 0.5 kcal/mol and (ii) to collect ligand binding conformational ensembles.
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Affiliation(s)
| | - Xingyu Luo
- School of Systems Biology, George Mason University, Manassas, VA 20110
| | - Audrey Olson
- School of Systems Biology, George Mason University, Manassas, VA 20110
| | - Bryan M. Delfing
- School of Systems Biology, George Mason University, Manassas, VA 20110
| | | | - Kenneth W. Foreman
- Department of Chemistry and Biochemistry, George Mason University, Fairfax, VA 22030
| | - Mikell Paige
- Department of Chemistry and Biochemistry, George Mason University, Fairfax, VA 22030
| | - Kylene Kehn-Hall
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
| | - Dmitri K. Klimov
- School of Systems Biology, George Mason University, Manassas, VA 20110
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45
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Jiang W. Enhanced Configurational Sampling Approaches to Alchemical Ligand Binding Free Energy Simulations: Current Status and Challenges. J Phys Chem B 2023; 127:6835-6841. [PMID: 37499215 DOI: 10.1021/acs.jpcb.3c02020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Ligand binding free energy simulations (LB-FES) have been routine tasks in modern drug discovery campaign. A long-standing challenge for LB-FES is the difficulty in adequately sampling nontrivial environmental reorganizations in response to ligand binding. Therefore, various enhanced configurational sampling (ECS) approaches were devised to speed up fluctuations of relevant slow degrees of freedom (SDOF) and ensure simulation convergence. However, in contrast to the achievements in parametrization, software performance, and workflow automation, efficient ECS methodology suitable for high throughput screening remains in an early stage of development. Here, a review of ECS developments with LB-FES is presented, revisiting current approaches and underlining the major technical pitfalls and challenges. This Perspective focuses on alchemical LB-FES on account of their predominant role in high throughput drug screening as well as the established partnership with ECS. The critical aspects of designing ECS approaches, from both theoretical and applied perspectives, are described. This work is intended to provide a contemporary review of the scientific, technical, and practical issues associated with the accelerating convergence of alchemical LB-FES.
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Affiliation(s)
- Wei Jiang
- Computational Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Argonne, Illinois 60439, United States
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46
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Sheng Z, Bimela JS, Wang M, Li Z, Guo Y, Ho DD. An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design. Front Immunol 2023; 14:1190416. [PMID: 37275896 PMCID: PMC10235760 DOI: 10.3389/fimmu.2023.1190416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 04/28/2023] [Indexed: 06/07/2023] Open
Abstract
Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.
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Affiliation(s)
- Zizhang Sheng
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Jude S. Bimela
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Maple Wang
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Zhiteng Li
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Yicheng Guo
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - David D. Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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47
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Gracia Carmona O, Gillhofer M, Tomasiak L, De Ruiter A, Oostenbrink C. Accelerated Enveloping Distribution Sampling to Probe the Presence of Water Molecules. J Chem Theory Comput 2023. [PMID: 37167545 DOI: 10.1021/acs.jctc.3c00109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Determining the presence of water molecules at protein-ligand interfaces is still a challenging task in free-energy calculations. The inappropriate placement of water molecules results in the stabilization of wrong conformational orientations of the ligand. With classical alchemical perturbation methods, such as thermodynamic integration (TI), it is essential to know the amount of water molecules in the active site of the respective ligands. However, the resolution of the crystal structure and the correct assignment of the electron density do not always lead to a clear placement of water molecules. In this work, we apply the one-step perturbation method named accelerated enveloping distribution sampling (AEDS) to determine the presence of water molecules in the active site by probing them in a fast and straightforward way. Based on these results, we combined the AEDS method with standard TI to calculate accurate binding free energies in the presence of buried water molecules. The main idea is to perturb the water molecules with AEDS such that they are allowed to alternate between regular water molecules and non-interacting dummy particles while treating the ligand with TI over an alchemical pathway. We demonstrate the use of AEDS to probe the presence of water molecules for six different test systems. For one of these, previous calculations showed difficulties to reproduce the experimental binding free energies, and here, we use the combined TI-AEDS approach to tackle these issues.
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Affiliation(s)
- Oriol Gracia Carmona
- Institute for Molecular Modeling and Simulation, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | - Michael Gillhofer
- Institute for Molecular Modeling and Simulation, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | - Lisa Tomasiak
- Institute for Molecular Modeling and Simulation, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | - Anita De Ruiter
- Institute for Molecular Modeling and Simulation, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | - Chris Oostenbrink
- Institute for Molecular Modeling and Simulation, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
- Christian Doppler Laboratory for Molecular Informatics in the Biosciences, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
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48
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Morado J, Mortenson PN, Nissink JWM, Essex JW, Skylaris CK. Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins. J Chem Inf Model 2023; 63:2810-2827. [PMID: 37071825 PMCID: PMC10170518 DOI: 10.1021/acs.jcim.2c01510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials.
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Affiliation(s)
- João Morado
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Paul N Mortenson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - J Willem M Nissink
- Computational Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Chris-Kriton Skylaris
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
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49
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Del ng BM, Olson A, Laracuente XE, Foreman KW, Paige M, Kehn-Hall K, Lockhart C, Klimov DK. Binding of Venezuelan Equine Encephalitis Virus Inhibitors to Importin-α Receptors Explored with All-Atom Replica Exchange Molecular Dynamics. J Phys Chem B 2023; 127:3175-3186. [PMID: 37001021 PMCID: PMC10358320 DOI: 10.1021/acs.jpcb.3c00429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Although Venezuelan equine encephalitis virus (VEEV) is a life-threatening pathogen with a capacity for epidemic outbreaks, there are no FDA-approved VEEV antivirals for humans. VEEV cytotoxicity is partially attributed to the formation of a tetrameric complex between the VEEV capsid protein, the nuclear import proteins importin-α and importin-β, and the nuclear export protein CRM1, which together block trafficking through the nuclear pore complex. Experimental studies have identified small molecules from the CL6662 scaffold as potential inhibitors of the viral nuclear localization signal (NLS) sequence binding to importin-α. However, little is known about the molecular mechanism of CL6662 inhibition. To address this issue, we employed all-atom replica exchange molecular dynamics simulations to probe, in atomistic detail, the binding mechanism of CL6662 ligands to importin-α. Three ligands, including G281-1485 and two congeners with varying hydrophobicities, were considered. We investigated the distribution of ligand binding poses, their locations, and ligand specificities measured by the strength of binding interactions. We found that G281-1485 binds nonspecifically without forming well-defined binding poses throughout the NLS binding site. Binding of the less hydrophobic congener becomes strongly on-target with respect to the NLS binding site but remains nonspecific. However, a more hydrophobic congener is a strongly specific binder and the only ligand out of three to form a well-defined binding pose, while partially overlapping with the NLS binding site. On the basis of free energy estimates, we argue that all three ligands weakly compete with the viral NLS sequence for binding to importin-α in an apparent compromise to preserve host NLS binding. We further show that all-atom replica exchange binding simulations are a viable tool for studying ligands binding nonspecifically without forming well-defined binding poses.
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Affiliation(s)
- Bryan M. Del ng
- School of Systems Biology, George Mason University, Manassas, VA 20110, USA
| | - Audrey Olson
- School of Systems Biology, George Mason University, Manassas, VA 20110, USA
| | | | - Kenneth W. Foreman
- Department of Chemistry and Biochemistry, George Mason University, Manassas, VA 20110, USA
| | - Mikell Paige
- Department of Chemistry and Biochemistry, George Mason University, Manassas, VA 20110, USA
| | - Kylene Kehn-Hall
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | | | - Dmitri K. Klimov
- School of Systems Biology, George Mason University, Manassas, VA 20110, USA
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50
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Muegge I, Hu Y. Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery. ACS Med Chem Lett 2023; 14:244-250. [PMID: 36923913 PMCID: PMC10009785 DOI: 10.1021/acsmedchemlett.2c00541] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 02/07/2023] [Indexed: 02/18/2023] Open
Abstract
Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.
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Affiliation(s)
- Ingo Muegge
- Alkermes,
Inc, 852 Winter Street, Waltham, Massachusetts 02451-1420, United States
| | - Yuan Hu
- Frontier
Medicines Corp, 451 D
Street, Suite 207, Boston, Massachusetts 02210, United States
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