1
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Nguyen HH, Tufts J, Minh DDL. On Inactivation of the Coronavirus Main Protease. J Chem Inf Model 2024; 64:1644-1656. [PMID: 38423522 PMCID: PMC10936523 DOI: 10.1021/acs.jcim.3c01518] [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: 10/05/2023] [Revised: 01/12/2024] [Accepted: 01/24/2024] [Indexed: 03/02/2024]
Abstract
A deeper understanding of the inactive conformations of the coronavirus main protease (MPro) could inform the design of allosteric drugs. Based on extensive molecular dynamics simulations, we built a Markov State Model to investigate structural changes that can inactivate the SARS-CoV-2 MPro. In a subset of structures, one subunit of the homodimer assumes an inactive conformation that resembles an inactive crystal structure. However, contradicting the widely held half-of-sites activity hypothesis, the most populated enzyme structures have two active subunits. We then used transition path theory (TPT) and the Jensen-Shannon Divergence (JSD) to pinpoint residues involved in the inactivation process. A π stack between Phe140 and His163 is a key feature that can distinguish active and inactive conformations of MPro. Each subunit has unique inactive conformations stabilized by π stacking interactions involving residues Phe140, Tyr118, His163, and His172, a hydrogen bonding network centered around His163 and His172, and a modified network of interactions in the dimer interface. The importance of these residues in maintaining an active structure explains the sensitivity of enzymatic activity to site-directed mutagenesis.
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Affiliation(s)
- Hong Ha Nguyen
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Jim Tufts
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - David D. L. Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, United States
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2
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Meller A, Kelly D, Smith LG, Bowman GR. Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants. Protein Sci 2024; 33:e4902. [PMID: 38358129 PMCID: PMC10868452 DOI: 10.1002/pro.4902] [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: 09/01/2023] [Revised: 12/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular BiophysicsWashington University in St. LouisSt. LouisMissouriUSA
- Medical Scientist Training ProgramWashington University in St. LouisSt. LouisMissouriUSA
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Devin Kelly
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Louis G. Smith
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gregory R. Bowman
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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3
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Smith L, Novak B, Osato M, Mobley DL, Bowman GR. PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model. J Chem Theory Comput 2024; 20:1036-1050. [PMID: 38291966 PMCID: PMC10867841 DOI: 10.1021/acs.jctc.3c00870] [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: 08/08/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 02/01/2024]
Abstract
Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.
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Affiliation(s)
- Louis
G. Smith
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Borna Novak
- Department
of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Medical
Scientist Training Program, Washington University
in St. Louis, St. Louis, Missouri 63130, United
States
| | - Meghan Osato
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - David L. Mobley
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - Gregory R. Bowman
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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4
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Arbon R, Zhu Y, Mey ASJS. Markov State Models: To Optimize or Not to Optimize. J Chem Theory Comput 2024; 20:977-988. [PMID: 38163961 PMCID: PMC10809420 DOI: 10.1021/acs.jctc.3c01134] [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: 10/13/2023] [Revised: 12/10/2023] [Accepted: 12/11/2023] [Indexed: 01/03/2024]
Abstract
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.
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Affiliation(s)
- Robert
E. Arbon
- EaStCHEM
School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh EH9 3FJ, United Kingdom
- Redesign
Science, 180 Varick St., New York, New York 10014, United States
| | - Yanchen Zhu
- EaStCHEM
School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh EH9 3FJ, United Kingdom
| | - Antonia S. J. S. Mey
- EaStCHEM
School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh EH9 3FJ, United Kingdom
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5
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Lalmansingh JM, Keeley AT, Ruff KM, Pappu RV, Holehouse AS. SOURSOP: A Python Package for the Analysis of Simulations of Intrinsically Disordered Proteins. J Chem Theory Comput 2023; 19:5609-5620. [PMID: 37463458 DOI: 10.1021/acs.jctc.3c00190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Conformational heterogeneity is a defining hallmark of intrinsically disordered proteins and protein regions (IDRs). The functions of IDRs and the emergent cellular phenotypes they control are associated with sequence-specific conformational ensembles. Simulations of conformational ensembles that are based on atomistic and coarse-grained models are routinely used to uncover the sequence-specific interactions that may contribute to IDR functions. These simulations are performed either independently or in conjunction with data from experiments. Functionally relevant features of IDRs can span a range of length scales. Extracting these features requires analysis routines that quantify a range of properties. Here, we describe a new analysis suite simulation analysis of unfolded regions of proteins (SOURSOP), an object-oriented and open-source toolkit designed for the analysis of simulated conformational ensembles of IDRs. SOURSOP implements several analysis routines motivated by principles in polymer physics, offering a unique collection of simple-to-use functions to characterize IDR ensembles. As an extendable framework, SOURSOP supports the development and implementation of new analysis routines that can be easily packaged and shared.
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Affiliation(s)
- Jared M Lalmansingh
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Alex T Keeley
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana-Champaign, Illinois 61801, United States
| | - Kiersten M Ruff
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Rohit V Pappu
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Alex S Holehouse
- Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
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6
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Meller A, Ward M, Borowsky J, Kshirsagar M, Lotthammer JM, Oviedo F, Ferres JL, Bowman GR. Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network. Nat Commun 2023; 14:1177. [PMID: 36859488 PMCID: PMC9977097 DOI: 10.1038/s41467-023-36699-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/09/2023] [Indexed: 03/03/2023] Open
Abstract
Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
- Medical Scientist Training Program, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, MO, 63110, USA
| | - Michael Ward
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
| | - Jonathan Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
| | | | - Jeffrey M Lotthammer
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
| | - Felipe Oviedo
- AI for Good Research Lab, Microsoft, Redmond, WA, USA
| | | | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA.
- Department of Biochemistry and Molecular Biophysics, University of Pennsylvania, 3620 Hamilton Walk, Philadelphia, PA, 19104, USA.
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7
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Lalmansingh JM, Keeley AT, Ruff KM, Pappu RV, Holehouse AS. SOURSOP: A Python package for the analysis of simulations of intrinsically disordered proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.16.528879. [PMID: 36824878 PMCID: PMC9949127 DOI: 10.1101/2023.02.16.528879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Conformational heterogeneity is a defining hallmark of intrinsically disordered proteins and protein regions (IDRs). The functions of IDRs and the emergent cellular phenotypes they control are associated with sequence-specific conformational ensembles. Simulations of conformational ensembles that are based on atomistic and coarse-grained models are routinely used to uncover the sequence-specific interactions that may contribute to IDR functions. These simulations are performed either independently or in conjunction with data from experiments. Functionally relevant features of IDRs can span a range of length scales. Extracting these features requires analysis routines that quantify a range of properties. Here, we describe a new analysis suite SOURSOP, an object-oriented and open-source toolkit designed for the analysis of simulated conformational ensembles of IDRs. SOURSOP implements several analysis routines motivated by principles in polymer physics, offering a unique collection of simple-to-use functions to characterize IDR ensembles. As an extendable framework, SOURSOP supports the development and implementation of new analysis routines that can be easily packaged and shared.
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8
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Meller A, Lotthammer JM, Smith LG, Novak B, Lee LA, Kuhn CC, Greenberg L, Leinwand LA, Greenberg MJ, Bowman GR. Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains. eLife 2023; 12:83602. [PMID: 36705568 PMCID: PMC9995120 DOI: 10.7554/elife.83602] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023] Open
Abstract
The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least six of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 ms of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin's binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). In a blind prediction for an isoform (Myh7b) whose blebbistatin sensitivity was unknown, we find good agreement between predicted and measured IC50s (0.67 μM vs. 0.36 μM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Medical Scientist Training Program, Washington University in St. LouisPhiladelphiaUnited States
| | - Jeffrey M Lotthammer
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Louis G Smith
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Department of Biochemistry and Biophysics, University of PennsylvaniaPhiladelphiaUnited States
| | - Borna Novak
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Medical Scientist Training Program, Washington University in St. LouisPhiladelphiaUnited States
| | - Lindsey A Lee
- Molecular, Cellular, and Developmental Biology Department, University of Colorado BoulderBoulderUnited States
- BioFrontiers InstituteBoulderUnited States
| | - Catherine C Kuhn
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Lina Greenberg
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Leslie A Leinwand
- Molecular, Cellular, and Developmental Biology Department, University of Colorado BoulderBoulderUnited States
- BioFrontiers InstituteBoulderUnited States
| | - Michael J Greenberg
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Department of Biochemistry and Biophysics, University of PennsylvaniaPhiladelphiaUnited States
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9
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Tamadonfar KO, Di Venanzio G, Pinkner JS, Dodson KW, Kalas V, Zimmerman MI, Bazan Villicana J, Bowman GR, Feldman MF, Hultgren SJ. Structure-function correlates of fibrinogen binding by Acinetobacter adhesins critical in catheter-associated urinary tract infections. Proc Natl Acad Sci U S A 2023; 120:e2212694120. [PMID: 36652481 PMCID: PMC9942807 DOI: 10.1073/pnas.2212694120] [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: 07/25/2022] [Accepted: 11/17/2022] [Indexed: 01/19/2023] Open
Abstract
Multidrug-resistant Acinetobacter baumannii infections are an urgent clinical problem and can cause difficult-to-treat nosocomial infections. During such infections, like catheter-associated urinary tract infections (CAUTI), A. baumannii rely on adhesive, extracellular fibers, called chaperone-usher pathway (CUP) pili for critical binding interactions. The A. baumannii uropathogenic strain, UPAB1, and the pan-European subclone II isolate, ACICU, use the CUP pili Abp1 and Abp2 (previously termed Cup and Prp, respectively) in tandem to establish CAUTIs, specifically to facilitate bacterial adherence and biofilm formation on the implanted catheter. Abp1 and Abp2 pili are tipped with two domain tip adhesins, Abp1D and Abp2D, respectively. We discovered that both adhesins bind fibrinogen, a critical host wound response protein that is released into the bladder upon catheterization and is subsequently deposited on the catheter. The crystal structures of the Abp1D and Abp2D receptor-binding domains were determined and revealed that they both contain a large, distally oriented pocket, which mediates binding to fibrinogen and other glycoproteins. Genetic, biochemical, and biophysical studies revealed that interactions with host proteins are governed by several critical residues in and along the edge of the binding pocket, one of which regulates the structural stability of an anterior loop motif. K34, located outside of the pocket but interacting with the anterior loop, also regulates the binding affinity of the protein. This study illuminates the mechanistic basis of the critical fibrinogen-coated catheter colonization step in A. baumannii CAUTI pathogenesis.
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Affiliation(s)
- Kevin O. Tamadonfar
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
- Center for Women’s Infectious Disease Research, Washington University School of Medicine, St Louis, MO63110
| | - Gisela Di Venanzio
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
| | - Jerome S. Pinkner
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
- Center for Women’s Infectious Disease Research, Washington University School of Medicine, St Louis, MO63110
| | - Karen W. Dodson
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
- Center for Women’s Infectious Disease Research, Washington University School of Medicine, St Louis, MO63110
| | - Vasilios Kalas
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
- Center for Women’s Infectious Disease Research, Washington University School of Medicine, St Louis, MO63110
- Department of Medicine, McGaw Medical Center of Northwestern University, Chicago, IL60611
| | - Maxwell I. Zimmerman
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO63110
| | - Jesus Bazan Villicana
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
- Center for Women’s Infectious Disease Research, Washington University School of Medicine, St Louis, MO63110
| | - Gregory R. Bowman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO63110
- Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University School of Medicine, St. Louis, MO63110
| | - Mario F. Feldman
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
| | - Scott J. Hultgren
- Department of Molecular Microbiology, Washington University School of Medicine, St Louis, MO63110
- Center for Women’s Infectious Disease Research, Washington University School of Medicine, St Louis, MO63110
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10
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Meller A, De Oliveira S, Davtyan A, Abramyan T, Bowman GR, van den Bedem H. Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors. Front Mol Biosci 2023; 10:1171143. [PMID: 37143823 PMCID: PMC10151774 DOI: 10.3389/fmolb.2023.1171143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/07/2023] [Indexed: 05/06/2023] Open
Abstract
Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, MO, United States
- Medical Scientist Training Program, Washington University in St. Louis, St. Louis, MO, United States
| | | | - Aram Davtyan
- Atomwise, Inc., San Francisco, CA, United States
| | | | - Gregory R. Bowman
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Gregory R. Bowman, ; Henry van den Bedem,
| | - Henry van den Bedem
- Atomwise, Inc., San Francisco, CA, United States
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States
- *Correspondence: Gregory R. Bowman, ; Henry van den Bedem,
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11
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Schnee P, Choudalakis M, Weirich S, Khella MS, Carvalho H, Pleiss J, Jeltsch A. Mechanistic basis of the increased methylation activity of the SETD2 protein lysine methyltransferase towards a designed super-substrate peptide. Commun Chem 2022; 5:139. [PMID: 36697904 PMCID: PMC9814698 DOI: 10.1038/s42004-022-00753-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 10/07/2022] [Indexed: 01/28/2023] Open
Abstract
Protein lysine methyltransferases have important regulatory functions in cells, but mechanisms determining their activity and specificity are incompletely understood. Naturally, SETD2 introduces H3K36me3, but previously an artificial super-substrate (ssK36) was identified, which is methylated >100-fold faster. The ssK36-SETD2 complex structure cannot fully explain this effect. We applied molecular dynamics (MD) simulations and biochemical experiments to unravel the mechanistic basis of the increased methylation of ssK36, considering peptide conformations in solution, association of peptide and enzyme, and formation of transition-state (TS) like conformations of the enzyme-peptide complex. We observed in MD and FRET experiments that ssK36 adopts a hairpin conformation in solution with V35 and K36 placed in the loop. The hairpin conformation has easier access into the active site of SETD2 and it unfolds during the association process. Peptide methylation experiments revealed that introducing a stable hairpin conformation in the H3K36 peptide increased its methylation by SETD2. In MD simulations of enzyme-peptide complexes, the ssK36 peptide approached TS-like structures more frequently than H3K36 and distinct, substrate-specific TS-like structures were observed. Hairpin association, hairpin unfolding during association, and substrate-specific catalytically competent conformations may also be relevant for other PKMTs and hairpins could represent a promising starting point for SETD2 inhibitor development.
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Affiliation(s)
- Philipp Schnee
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Michel Choudalakis
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Sara Weirich
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Mina S Khella
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.,Biochemistry Department, Faculty of Pharmacy, Ain Shams University, African Union Organization Street, Abbassia, Cairo, 11566, Egypt
| | - Henrique Carvalho
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
| | - Albert Jeltsch
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
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12
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Pajak J, Arya G. Molecular dynamics of DNA translocation by FtsK. Nucleic Acids Res 2022; 50:8459-8470. [PMID: 35947697 PMCID: PMC9410874 DOI: 10.1093/nar/gkac668] [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: 01/04/2022] [Revised: 07/16/2022] [Accepted: 07/23/2022] [Indexed: 12/24/2022] Open
Abstract
The bacterial FtsK motor harvests energy from ATP to translocate double-stranded DNA during cell division. Here, we probe the molecular mechanisms underlying coordinated DNA translocation in FtsK by performing long timescale simulations of its hexameric assembly and individual subunits. From these simulations we predict signaling pathways that connect the ATPase active site to DNA-gripping residues, which allows the motor to coordinate its translocation activity with its ATPase activity. Additionally, we utilize well-tempered metadynamics simulations to compute free-energy landscapes that elucidate the extended-to-compact transition involved in force generation. We show that nucleotide binding promotes a compact conformation of a motor subunit, whereas the apo subunit is flexible. Together, our results support a mechanism whereby each ATP-bound subunit of the motor conforms to the helical pitch of DNA, and ATP hydrolysis/product release causes a subunit to lose grip of DNA. By ordinally engaging and disengaging with DNA, the FtsK motor unidirectionally translocates DNA.
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Affiliation(s)
- Joshua Pajak
- Dept. of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA
- Dept. of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Gaurav Arya
- Dept. of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA
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13
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Cruz MA, Frederick TE, Mallimadugula UL, Singh S, Vithani N, Zimmerman MI, Porter JR, Moeder KE, Amarasinghe GK, Bowman GR. A cryptic pocket in Ebola VP35 allosterically controls RNA binding. Nat Commun 2022; 13:2269. [PMID: 35477718 PMCID: PMC9046395 DOI: 10.1038/s41467-022-29927-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/07/2022] [Indexed: 11/08/2022] Open
Abstract
Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola's replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.
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Affiliation(s)
- Matthew A Cruz
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Thomas E Frederick
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Upasana L Mallimadugula
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Sukrit Singh
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Neha Vithani
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Justin R Porter
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Katelyn E Moeder
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Gaya K Amarasinghe
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Gregory R Bowman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, 63110, USA.
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14
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Sladek V, Harada R, Shigeta Y. Residue Folding Degree-Relationship to Secondary Structure Categories and Use as Collective Variable. Int J Mol Sci 2021; 22:ijms222313042. [PMID: 34884847 PMCID: PMC8657879 DOI: 10.3390/ijms222313042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/23/2021] [Accepted: 11/29/2021] [Indexed: 11/22/2022] Open
Abstract
Recently, we have shown that the residue folding degree, a network-based measure of folded content in proteins, is able to capture backbone conformational transitions related to the formation of secondary structures in molecular dynamics (MD) simulations. In this work, we focus primarily on developing a collective variable (CV) for MD based on this residue-bound parameter to be able to trace the evolution of secondary structure in segments of the protein. We show that this CV can do just that and that the related energy profiles (potentials of mean force, PMF) and transition barriers are comparable to those found by others for particular events in the folding process of the model mini protein Trp-cage. Hence, we conclude that the relative segment folding degree (the newly proposed CV) is a computationally viable option to gain insight into the formation of secondary structures in protein dynamics. We also show that this CV can be directly used as a measure of the amount of α-helical content in a selected segment.
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Affiliation(s)
- Vladimir Sladek
- Institute of Chemistry, Slovak Academy of Sciences, 845 38 Bratislava, Slovakia
- Correspondence:
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, Tsukuba 305-8577, Ibaraki, Japan; (R.H.); (Y.S.)
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, Tsukuba 305-8577, Ibaraki, Japan; (R.H.); (Y.S.)
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15
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Naturally Occurring Genetic Variants in the Oxytocin Receptor Alter Receptor Signaling Profiles. ACS Pharmacol Transl Sci 2021; 4:1543-1555. [PMID: 34661073 PMCID: PMC8506602 DOI: 10.1021/acsptsci.1c00095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Indexed: 01/04/2023]
Abstract
![]()
The hormone oxytocin
is commonly administered during childbirth
to initiate and strengthen uterine contractions and prevent postpartum
hemorrhage. However, patients have wide variation in the oxytocin
dose required for a clinical response. To begin to uncover the mechanisms
underlying this variability, we screened the 11 most prevalent missense
genetic variants in the oxytocin receptor (OXTR)
gene. We found that five variants, V45L, P108A, L206V, V281M, and
E339K, significantly altered oxytocin-induced Ca2+ signaling
or β-arrestin recruitment and proceeded to assess the effects
of these variants on OXTR trafficking to the cell membrane, desensitization,
and internalization. The variants P108A and L206V increased OXTR localization
to the cell membrane, whereas V281M and E339K caused OXTR to be retained
inside the cell. We examined how the variants altered the balance
between OXTR activation and desensitization, which is critical for
appropriate oxytocin dosing. The E339K variant impaired OXTR activation,
internalization, and desensitization to roughly equal extents. In
contrast, V281M decreased OXTR activation but had no effect on internalization
and desensitization. V45L and P108A did not alter OXTR activation
but did impair β-arrestin recruitment, internalization, and
desensitization. Molecular dynamics simulations predicted that V45L
and P108A prevent extension of the first intracellular loop of OXTR,
thus inhibiting β-arrestin binding. Overall, our data suggest
mechanisms by which OXTR genetic variants could alter
clinical response to oxytocin.
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16
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Glielmo A, Husic BE, Rodriguez A, Clementi C, Noé F, Laio A. Unsupervised Learning Methods for Molecular Simulation Data. Chem Rev 2021; 121:9722-9758. [PMID: 33945269 PMCID: PMC8391792 DOI: 10.1021/acs.chemrev.0c01195] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Indexed: 12/21/2022]
Abstract
Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.
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Affiliation(s)
- Aldo Glielmo
- International
School for Advanced Studies (SISSA) 34014 Trieste, Italy
| | - Brooke E. Husic
- Freie
Universität Berlin, Department of Mathematics
and Computer Science, 14195 Berlin, Germany
| | - Alex Rodriguez
- International Centre for Theoretical
Physics (ICTP), Condensed Matter and Statistical
Physics Section, 34100 Trieste, Italy
| | - Cecilia Clementi
- Freie
Universität Berlin, Department for
Physics, 14195 Berlin, Germany
- Rice
University Houston, Department of Chemistry, Houston, Texas 77005, United States
| | - Frank Noé
- Freie
Universität Berlin, Department of Mathematics
and Computer Science, 14195 Berlin, Germany
- Freie
Universität Berlin, Department for
Physics, 14195 Berlin, Germany
- Rice
University Houston, Department of Chemistry, Houston, Texas 77005, United States
| | - Alessandro Laio
- International
School for Advanced Studies (SISSA) 34014 Trieste, Italy
- International Centre for Theoretical
Physics (ICTP), Condensed Matter and Statistical
Physics Section, 34100 Trieste, Italy
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17
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Vithani N, Ward MD, Zimmerman MI, Novak B, Borowsky JH, Singh S, Bowman GR. SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential. Biophys J 2021; 120:2880-2889. [PMID: 33794150 PMCID: PMC8007187 DOI: 10.1016/j.bpj.2021.03.024] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/17/2021] [Accepted: 03/25/2021] [Indexed: 01/12/2023] Open
Abstract
Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between 20 and 30 proteins to carry out their viral replication cycle, including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2'-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? Although experimentally derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over 1 ms of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16's conformational ensemble to activate it. Second, guided by this activation mechanism and Markov state models, we investigate whether Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV1 and MERS but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket.
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Affiliation(s)
- Neha Vithani
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, Missouri
| | - Michael D Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, Missouri
| | - Maxwell I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, Missouri
| | - Borna Novak
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, Missouri; Medical Scientist Training Program, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Jonathan H Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, Missouri
| | - Sukrit Singh
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, Missouri
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, Missouri.
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18
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Zimmerman MI, Porter JR, Ward MD, Singh S, Vithani N, Meller A, Mallimadugula UL, Kuhn CE, Borowsky JH, Wiewiora RP, Hurley MFD, Harbison AM, Fogarty CA, Coffland JE, Fadda E, Voelz VA, Chodera JD, Bowman GR. SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nat Chem 2021; 13:651-659. [PMID: 34031561 PMCID: PMC8249329 DOI: 10.1038/s41557-021-00707-0] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/14/2021] [Indexed: 01/20/2023]
Abstract
SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression and replication that depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate 0.1 seconds of the viral proteome. Our adaptive sampling simulations predict dramatic opening of the apo spike complex, far beyond that seen experimentally, explaining and predicting the existence of 'cryptic' epitopes. Different spike variants modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also discover dramatic conformational changes across the proteome, which reveal over 50 'cryptic' pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas.
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Affiliation(s)
- Maxwell I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Justin R Porter
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Michael D Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Sukrit Singh
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Neha Vithani
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Upasana L Mallimadugula
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Catherine E Kuhn
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Jonathan H Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA
| | - Rafal P Wiewiora
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, NY, New York, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, NY, New York, USA
| | | | - Aoife M Harbison
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - Carl A Fogarty
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | | | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, PA, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, NY, New York, USA
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA.
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St Louis, MO, USA.
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19
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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20
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Ward MD, Zimmerman MI, Meller A, Chung M, Swamidass SJ, Bowman GR. Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets. Nat Commun 2021; 12:3023. [PMID: 34021153 PMCID: PMC8140102 DOI: 10.1038/s41467-021-23246-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/16/2021] [Indexed: 12/05/2022] Open
Abstract
Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.
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Affiliation(s)
- Michael D Ward
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - Artur Meller
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - Moses Chung
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - S J Swamidass
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Gregory R Bowman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA.
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA.
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21
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Pajak J, Atz R, Hilbert BJ, Morais MC, Kelch BA, Jardine PJ, Arya G. Viral packaging ATPases utilize a glutamate switch to couple ATPase activity and DNA translocation. Proc Natl Acad Sci U S A 2021; 118:e2024928118. [PMID: 33888587 PMCID: PMC8092589 DOI: 10.1073/pnas.2024928118] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Many viruses utilize ringed packaging ATPases to translocate double-stranded DNA into procapsids during replication. A critical step in the mechanochemical cycle of such ATPases is ATP binding, which causes a subunit within the motor to grip DNA tightly. Here, we probe the underlying molecular mechanism by which ATP binding is coupled to DNA gripping and show that a glutamate-switch residue found in AAA+ enzymes is central to this coupling in viral packaging ATPases. Using free-energy landscapes computed through molecular dynamics simulations, we determined the stable conformational state of the ATPase active site in ATP- and ADP-bound states. Our results show that the catalytic glutamate residue transitions from an active to an inactive pose upon ATP hydrolysis and that a residue assigned as the glutamate switch is necessary for regulating this transition. Furthermore, we identified via mutual information analyses the intramolecular signaling pathway mediated by the glutamate switch that is responsible for coupling ATP binding to conformational transitions of DNA-gripping motifs. We corroborated these predictions with both structural and functional experimental measurements. Specifically, we showed that the crystal structure of the ADP-bound P74-26 packaging ATPase is consistent with the structural coupling predicted from simulations, and we further showed that disrupting the predicted signaling pathway indeed decouples ATPase activity from DNA translocation activity in the φ29 DNA packaging motor. Our work thus establishes a signaling pathway that couples chemical and mechanical events in viral DNA packaging motors.
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Affiliation(s)
- Joshua Pajak
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708
| | - Rockney Atz
- Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN 55455
| | - Brendan J Hilbert
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605
| | - Marc C Morais
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX 77550
| | - Brian A Kelch
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605
| | - Paul J Jardine
- Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN 55455
| | - Gaurav Arya
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708;
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22
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Cubuk J, Alston JJ, Incicco JJ, Singh S, Stuchell-Brereton MD, Ward MD, Zimmerman MI, Vithani N, Griffith D, Wagoner JA, Bowman GR, Hall KB, Soranno A, Holehouse AS. The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA. Nat Commun 2021; 12:1936. [PMID: 33782395 PMCID: PMC8007728 DOI: 10.1038/s41467-021-21953-3] [Citation(s) in RCA: 264] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 02/18/2021] [Indexed: 12/15/2022] Open
Abstract
The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.
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Affiliation(s)
- Jasmine Cubuk
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Jhullian J Alston
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - J Jeremías Incicco
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Sukrit Singh
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Melissa D Stuchell-Brereton
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Michael D Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Neha Vithani
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Griffith
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Jason A Wagoner
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA
| | - Kathleen B Hall
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrea Soranno
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA.
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA.
| | - Alex S Holehouse
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA.
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, MO, USA.
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23
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Cubuk J, Alston JJ, Incicco JJ, Singh S, Stuchell-Brereton MD, Ward MD, Zimmerman MI, Vithani N, Griffith D, Wagoner JA, Bowman GR, Hall KB, Soranno A, Holehouse AS. The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.17.158121. [PMID: 32587966 PMCID: PMC7310622 DOI: 10.1101/2020.06.17.158121] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.
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24
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Vithani N, Ward MD, Zimmerman MI, Novak B, Borowsky JH, Singh S, Bowman GR. SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.12.10.420109. [PMID: 33330873 PMCID: PMC7743098 DOI: 10.1101/2020.12.10.420109] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between twenty and thirty proteins to carry out their viral replication cycle including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2'-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? While experimentally-derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over one millisecond of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16's conformational ensemble in order to activate it. Second, guided by this activation mechanism and Markov state models (MSMs), we investigate if Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV-2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV-1 and MERS, but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket. STATEMENT OF SIGNIFICANCE Coronaviruses are a major threat to human health. These viruses employ molecular machines, called proteins, to infect host cells and replicate. Characterizing the structure and dynamics of these proteins could provide a basis for designing small molecule antivirals. In this work, we use computer simulations to understand the moving parts of an essential SARS-CoV-2 protein, understand how a binding partner turns it on and off, and identify a novel pocket that antivirals could target to shut this protein off. The pocket is also present in other coronaviruses but not in the related human protein, so it could be a valuable target for pan-coronavirus antivirals.
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Affiliation(s)
- Neha Vithani
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Michael D. Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Maxwell I. Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Borna Novak
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Medical Scientist Training Program, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, United States
| | - Jonathan H. Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Sukrit Singh
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Gregory R. Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
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25
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Zimmerman MI, Porter JR, Ward MD, Singh S, Vithani N, Meller A, Mallimadugula UL, Kuhn CE, Borowsky JH, Wiewiora RP, Hurley MFD, Harbison AM, Fogarty CA, Coffland JE, Fadda E, Voelz VA, Chodera JD, Bowman GR. SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.27.175430. [PMID: 32637963 PMCID: PMC7337393 DOI: 10.1101/2020.06.27.175430] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression, and replication, which depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate an unprecedented 0.1 seconds of the viral proteome. Our simulations capture dramatic opening of the apo Spike complex, far beyond that seen experimentally, which explains and successfully predicts the existence of 'cryptic' epitopes. Different Spike homologues modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also observe dramatic conformational changes across the proteome, which reveal over 50 'cryptic' pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas.
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Affiliation(s)
- Maxwell I. Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Justin R. Porter
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Michael D. Ward
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Sukrit Singh
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Neha Vithani
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Upasana L. Mallimadugula
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Catherine E. Kuhn
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Jonathan H. Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Rafal P. Wiewiora
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Computational and Systems Biology Program, Sloan Kettering Institute, New York, New York 10065, United States
| | - Matthew F. D. Hurley
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Aoife M Harbison
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Kildare, Ireland
| | - Carl A Fogarty
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Kildare, Ireland
| | | | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Kildare, Ireland
| | - Vincent A. Voelz
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, New York, New York 10065, United States
| | - Gregory R. Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
- Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis, Missouri 63130, United States
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26
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Gkeka P, Stoltz G, Barati Farimani A, Belkacemi Z, Ceriotti M, Chodera JD, Dinner AR, Ferguson AL, Maillet JB, Minoux H, Peter C, Pietrucci F, Silveira A, Tkatchenko A, Trstanova Z, Wiewiora R, Lelièvre T. Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems. J Chem Theory Comput 2020; 16:4757-4775. [PMID: 32559068 PMCID: PMC8312194 DOI: 10.1021/acs.jctc.0c00355] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
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Affiliation(s)
- Paraskevi Gkeka
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
| | - Gabriel Stoltz
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
| | | | - Zineb Belkacemi
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
| | - Michele Ceriotti
- Laboratory of Computational Science and Modelling, Institute of Materials, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Aaron R Dinner
- Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United States
| | | | - Hervé Minoux
- Integrated Drug Discovery, Sanofi R&D, 94403 Vitry-sur-Seine, France
| | | | - Fabio Pietrucci
- UMR CNRS 7590, MNHN, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, 75005 Paris, France
| | - Ana Silveira
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Zofia Trstanova
- School of Mathematics, The University of Edinburgh, Edinburgh EH9 3FD, U.K
| | - Rafal Wiewiora
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Tony Lelièvre
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
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27
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Porter JR, Meller A, Zimmerman MI, Greenberg MJ, Bowman GR. Conformational distributions of isolated myosin motor domains encode their mechanochemical properties. eLife 2020; 9:e55132. [PMID: 32479265 PMCID: PMC7259954 DOI: 10.7554/elife.55132] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/04/2020] [Indexed: 01/25/2023] Open
Abstract
Myosin motor domains perform an extraordinary diversity of biological functions despite sharing a common mechanochemical cycle. Motors are adapted to their function, in part, by tuning the thermodynamics and kinetics of steps in this cycle. However, it remains unclear how sequence encodes these differences, since biochemically distinct motors often have nearly indistinguishable crystal structures. We hypothesized that sequences produce distinct biochemical phenotypes by modulating the relative probabilities of an ensemble of conformations primed for different functional roles. To test this hypothesis, we modeled the distribution of conformations for 12 myosin motor domains by building Markov state models (MSMs) from an unprecedented two milliseconds of all-atom, explicit-solvent molecular dynamics simulations. Comparing motors reveals shifts in the balance between nucleotide-favorable and nucleotide-unfavorable P-loop conformations that predict experimentally measured duty ratios and ADP release rates better than sequence or individual structures. This result demonstrates the power of an ensemble perspective for interrogating sequence-function relationships.
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Affiliation(s)
- Justin R Porter
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine in St. LouisSt. LouisUnited States
| | - Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine in St. LouisSt. LouisUnited States
| | - Maxwell I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine in St. LouisSt. LouisUnited States
| | - Michael J Greenberg
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine in St. LouisSt. LouisUnited States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine in St. LouisSt. LouisUnited States
- Center for the Science and Engineering of Living Systems, Washington University in St. LouisSt. LouisUnited States
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28
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Behring JB, van der Post S, Mooradian AD, Egan MJ, Zimmerman MI, Clements JL, Bowman GR, Held JM. Spatial and temporal alterations in protein structure by EGF regulate cryptic cysteine oxidation. Sci Signal 2020; 13:eaay7315. [PMID: 31964804 PMCID: PMC7263378 DOI: 10.1126/scisignal.aay7315] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Stimulation of plasma membrane receptor tyrosine kinases (RTKs), such as the epidermal growth factor receptor (EGFR), locally increases the abundance of reactive oxygen species (ROS). These ROS then oxidize cysteine residues in proteins to potentiate downstream signaling. Spatial confinement of ROS is an important regulatory mechanism of redox signaling that enables the stimulation of different RTKs to oxidize distinct sets of downstream proteins. To uncover additional mechanisms that specify cysteines that are redox regulated by EGF stimulation, we performed time-resolved quantification of the EGF-dependent oxidation of 4200 cysteine sites in A431 cells. Fifty-one percent of cysteines were statistically significantly oxidized by EGF stimulation. Furthermore, EGF induced three distinct spatiotemporal patterns of cysteine oxidation in functionally organized protein networks, consistent with the spatial confinement model. Unexpectedly, protein crystal structure analysis and molecular dynamics simulations indicated widespread redox regulation of cryptic cysteine residues that are solvent exposed only upon changes in protein conformation. Phosphorylation and increased flux of nucleotide substrates served as two distinct modes by which EGF specified the cryptic cysteine residues that became solvent exposed and redox regulated. Because proteins that are structurally regulated by different RTKs or cellular perturbations are largely unique, these findings suggest that solvent exposure and redox regulation of cryptic cysteine residues contextually delineate redox signaling networks.
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Affiliation(s)
- Jessica B Behring
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Sjoerd van der Post
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Arshag D Mooradian
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Matthew J Egan
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Jenna L Clements
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Jason M Held
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA.
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29
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Affiliation(s)
- Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
- Department of Physics, Freie Universität Berlin, Berlin, Germany
| | - Edina Rosta
- Department of Chemistry, Kings College London, London, England
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30
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Porter JR, Moeder KE, Sibbald CA, Zimmerman MI, Hart KM, Greenberg MJ, Bowman GR. Cooperative Changes in Solvent Exposure Identify Cryptic Pockets, Switches, and Allosteric Coupling. Biophys J 2019; 116:818-830. [PMID: 30744991 DOI: 10.1016/j.bpj.2018.11.3144] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 11/09/2018] [Accepted: 11/14/2018] [Indexed: 01/19/2023] Open
Abstract
Proteins are dynamic molecules that undergo conformational changes to a broad spectrum of different excited states. Unfortunately, the small populations of these states make it difficult to determine their structures or functional implications. Computer simulations are an increasingly powerful means to identify and characterize functionally relevant excited states. However, this advance has uncovered a further challenge: it can be extremely difficult to identify the most salient features of large simulation data sets. We reasoned that many functionally relevant conformational changes are likely to involve large, cooperative changes to the surfaces that are available to interact with potential binding partners. To examine this hypothesis, we introduce a method that returns a prioritized list of potentially functional conformational changes by segmenting protein structures into clusters of residues that undergo cooperative changes in their solvent exposure, along with the hierarchy of interactions between these groups. We term these groups exposons to distinguish them from other types of clusters that arise in this analysis and others. We demonstrate, using three different model systems, that this method identifies experimentally validated and functionally relevant conformational changes, including conformational switches, allosteric coupling, and cryptic pockets. Our results suggest that key functional sites are hubs in the network of exposons. As a further test of the predictive power of this approach, we apply it to discover cryptic allosteric sites in two different β-lactamase enzymes that are widespread sources of antibiotic resistance. Experimental tests confirm our predictions for both systems. Importantly, we provide the first evidence, to our knowledge, for a cryptic allosteric site in CTX-M-9 β-lactamase. Experimentally testing this prediction did not require any mutations and revealed that this site exerts the most potent allosteric control over activity of any pockets found in β-lactamases to date. Discovery of a similar pocket that was previously overlooked in the well-studied TEM-1 β-lactamase demonstrates the utility of exposons.
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Affiliation(s)
- Justin R Porter
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri
| | - Katelyn E Moeder
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri
| | - Carrie A Sibbald
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri
| | - Maxwell I Zimmerman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri
| | - Kathryn M Hart
- Department of Chemistry, Williams College, Williamstown, Massachusetts
| | - Michael J Greenberg
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri
| | - Gregory R Bowman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri; Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, Missouri.
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31
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Sun X, Singh S, Blumer KJ, Bowman GR. Simulation of spontaneous G protein activation reveals a new intermediate driving GDP unbinding. eLife 2018; 7:e38465. [PMID: 30289386 PMCID: PMC6224197 DOI: 10.7554/elife.38465] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 10/04/2018] [Indexed: 12/12/2022] Open
Abstract
Activation of heterotrimeric G proteins is a key step in many signaling cascades. However, a complete mechanism for this process, which requires allosteric communication between binding sites that are ~30 Å apart, remains elusive. We construct an atomically detailed model of G protein activation by combining three powerful computational methods: metadynamics, Markov state models (MSMs), and CARDS analysis of correlated motions. We uncover a mechanism that is consistent with a wide variety of structural and biochemical data. Surprisingly, the rate-limiting step for GDP release correlates with tilting rather than translation of the GPCR-binding helix 5. β-Strands 1 - 3 and helix 1 emerge as hubs in the allosteric network that links conformational changes in the GPCR-binding site to disordering of the distal nucleotide-binding site and consequent GDP release. Our approach and insights provide foundations for understanding disease-implicated G protein mutants, illuminating slow events in allosteric networks, and examining unbinding processes with slow off-rates.
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Affiliation(s)
- Xianqiang Sun
- Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineMissouriUnited States
| | - Sukrit Singh
- Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineMissouriUnited States
| | - Kendall J Blumer
- Department of Cell Biology and PhysiologyWashington University School of MedicineMissouriUnited States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineMissouriUnited States
- Center for Biological Systems EngineeringWashington University School of MedicineMissouriUnited States
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