1
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Clark F, Robb GR, Cole DJ, Michel J. Automated Adaptive Absolute Binding Free Energy Calculations. J Chem Theory Comput 2024. [PMID: 39254715 PMCID: PMC11428140 DOI: 10.1021/acs.jctc.4c00806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a fully automated ABFE workflow based on the automated selection of λ windows, the ensemble-based detection of equilibration, and the adaptive allocation of sampling time based on inter-replicate statistics. We find that the automated selection of intermediate states with consistent overlap is rapid, robust, and simple to implement. Robust detection of equilibration is achieved with a paired t-test between the free energy estimates at initial and final portions of a an ensemble of runs. We determine reasonable default parameters for all algorithms and show that the full workflow produces equivalent results to a nonadaptive scheme over a variety of test systems, while often accelerating equilibration. Our complete workflow is implemented in the open-source package A3FE (https://github.com/michellab/a3fe).
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Affiliation(s)
- Finlay Clark
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Graeme R Robb
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
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2
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van Gunsteren WF, Oostenbrink C. Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives. J Chem Inf Model 2024; 64:6281-6304. [PMID: 39136351 DOI: 10.1021/acs.jcim.4c00823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.
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Affiliation(s)
- Wilfred F van Gunsteren
- Institute for Molecular Physical Science, Swiss Federal Institute of Technology, ETH, CH-8093 Zurich, Switzerland
| | - Chris Oostenbrink
- Institute of Molecular Modelling and Simulation, BOKU University, 1190 Vienna, Austria
- Christian Doppler Laboratory for Molecular Informatics in the Biosciences, BOKU University, Muthgasse 18, 1190 Vienna, Austria
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3
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Summa CM, Langford DP, Dinshaw SH, Webb J, Rick SW. Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding. J Chem Theory Comput 2024; 20:2812-2819. [PMID: 38538531 DOI: 10.1021/acs.jctc.4c00057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Computer simulation methods can aid in the rational design of drugs aimed at a specific target, typically a protein. The affinity of a drug for its target is given by the free energy of binding. Binding can be further characterized by the enthalpy and entropy changes in the process. Methods exist to determine exact free energies, enthalpies, and entropies that are dependent only on the quality of the potential model and adequate sampling of conformational degrees of freedom. Entropy and enthalpy are roughly an order of magnitude more difficult to calculate than the free energy. This project combines a replica exchange method for enhanced sampling, designed to be efficient for protein-sized systems, with free energy calculations. This approach, replica exchange with dynamical scaling (REDS), uses two conventional simulations at different temperatures so that the entropy can be found from the temperature dependence of the free energy. A third replica is placed between them, with a modified Hamiltonian that allows it to span the temperature range of the conventional replicas. REDS provides temperature-dependent data and aids in sampling. It is applied to the bromodomain-containing protein 4 (BRD4) system. We find that for the force fields used, the free energies are accurate but the entropies and enthalpies are not, with the entropic contribution being too positive. Reproducing the entropy and enthalpy of binding appears to be a more stringent test of the force fields than reproducing the free energy.
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Affiliation(s)
- Christopher M Summa
- Department of Computer Science, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Dillon P Langford
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Sam H Dinshaw
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Jennifer Webb
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Steven W Rick
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
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4
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Baumann H, Dybeck E, McClendon CL, Pickard FC, Gapsys V, Pérez-Benito L, Hahn DF, Tresadern G, Mathiowetz AM, Mobley DL. Broadening the Scope of Binding Free Energy Calculations Using a Separated Topologies Approach. J Chem Theory Comput 2023; 19:5058-5076. [PMID: 37487138 PMCID: PMC10413862 DOI: 10.1021/acs.jctc.3c00282] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Indexed: 07/26/2023]
Abstract
Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.
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Affiliation(s)
- Hannah
M. Baumann
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
| | - Eric Dybeck
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Christopher L. McClendon
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Frank C. Pickard
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Vytautas Gapsys
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Laura Pérez-Benito
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - David F. Hahn
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Gary Tresadern
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Alan M. Mathiowetz
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - David L. Mobley
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
- Department
of Chemistry, University of California,
Irvine, Irvine, California 92697, United States
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5
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2023; 19:4863-4882. [PMID: 37450482 PMCID: PMC11219094 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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6
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.530278. [PMID: 36945557 PMCID: PMC10028896 DOI: 10.1101/2023.03.07.530278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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7
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Patel LA, Chau P, Debesai S, Darwin L, Neale C. Drug Discovery by Automated Adaptation of Chemical Structure and Identity. J Chem Theory Comput 2022; 18:5006-5024. [PMID: 35834740 DOI: 10.1021/acs.jctc.1c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.
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8
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Markthaler D, Fleck M, Stankiewicz B, Hansen N. Exploring the Effect of Enhanced Sampling on Protein Stability Prediction. J Chem Theory Comput 2022; 18:2569-2583. [PMID: 35298174 DOI: 10.1021/acs.jctc.1c01012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.
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Affiliation(s)
- Daniel Markthaler
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Maximilian Fleck
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Bartosz Stankiewicz
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Niels Hansen
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
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9
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Carvalho Martins L, Cino EA, Ferreira RS. PyAutoFEP: An Automated Free Energy Perturbation Workflow for GROMACS Integrating Enhanced Sampling Methods. J Chem Theory Comput 2021; 17:4262-4273. [PMID: 34142828 DOI: 10.1021/acs.jctc.1c00194] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Free energy perturbation (FEP) calculations are now routinely used in drug discovery to estimate the relative FEB (RFEB) of small molecules to a biomolecular target of interest. Using enhanced sampling can improve the correlation between predictions and experimental data, especially in systems with conformational changes. Due to the large number of perturbations required in drug discovery campaigns, the manual setup of FEP calculations is no longer viable. Here, we introduce PyAutoFEP, a flexible and open-source tool to aid the setup of RFEB FEP. PyAutoFEP is written in Python3, and automates the generation of perturbation maps, dual topologies, system building and molecular dynamics (MD), and analysis. PyAutoFEP supports multiple force fields, incorporates replica exchange with solute tempering (REST) and replica exchange with solute scaling (REST2) enhanced sampling methods, and allows flexible λ values along perturbation windows. To validate PyAutoFEP, it was applied to a set of 14 Farnesoid X receptor ligands, a system included in the drug design data resource grand challenge 2. An 88% mean correct sign prediction was achieved, and 75% of the predictions had an error below 1.5 kcal/mol. Results using Amber03/GAFF, CHARMM36m/CGenFF, and OPLS-AA/M/LigParGen had Pearson's r values of 0.71 ± 0.13, 0.30 ± 0.27, and 0.66 ± 0.20, respectively. The Amber03/GAFF and OPLS-AA/M/LigParGen results were on par with the top grand challenge 2 submissions. Applying REST2 improved the results using CHARMM36m/CGenFF (Pearson's r = 0.43 ± 0.21) but had little impact on the other force fields. CHARMM36-YF and CHARMM36-WYF modifications did not yield improved predictions compared to CHARMM36m. Finally, we estimated the probability of finding a molecule 1 pKi better than a lead when using PyAutoFEP to screen 10 or 100 analogues. The probabilities, when compared to random sampling, increased up to sevenfold when 100 molecules were to be screened, suggesting that PyAutoFEP would likely be useful for lead optimization. PyAutoFEP is available on GitHub at https://github.com/lmmpf/PyAutoFEP.
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Affiliation(s)
- Luan Carvalho Martins
- Graduate Program in Bioinformatics. Institute for Biological Sciences, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Elio A Cino
- Biochemistry and Immunology Department, Institute for Biological Sciences, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Rafaela Salgado Ferreira
- Biochemistry and Immunology Department, Institute for Biological Sciences, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
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10
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Vilseck JZ, Ding X, Hayes RL, Brooks CL. Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands. J Chem Theory Comput 2021; 17:3895-3907. [PMID: 34101448 DOI: 10.1021/acs.jctc.1c00176] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In this work, the discrete λ variant of the Gibbs sampler-based λ-dynamics (d-GSλD) method is developed to enable multiple functional group perturbations to be investigated at one or more sites of substitution off a common ligand core. The theoretical framework and special considerations for constructing discrete λ states for multisite d-GSλD are presented. The precision and accuracy of the d-GSλD method is evaluated with three test cases of increasing complexity. Specifically, methyl → methyl symmetric perturbations in water, 1,4-benzene hydration free energies and protein-ligand binding affinities for an example HIV-1 reverse transcriptase inhibitor series are computed with d-GSλD. Complementary MSλD calculations were also performed to compare with d-GSλD's performance. Excellent agreement between d-GSλD and MSλD is observed, with mean unsigned errors of 0.12 and 0.22 kcal/mol for computed hydration and binding free energy test cases, respectively. Good agreement with experiment is also observed, with errors of 0.5-0.7 kcal/mol. These findings support the applicability of the d-GSλD free energy method for a variety of molecular design problems, including structure-based drug design. Finally, a discussion of d-GSλD versus MSλD approaches is presented to compare and contrast features of both methods.
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Affiliation(s)
- Jonah Z Vilseck
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.,Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Xinqiang Ding
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ryan L Hayes
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Charles L Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.,Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, United States
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11
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Baumann HM, Gapsys V, de Groot BL, Mobley DL. Challenges Encountered Applying Equilibrium and Nonequilibrium Binding Free Energy Calculations. J Phys Chem B 2021; 125:4241-4261. [PMID: 33905257 PMCID: PMC8240641 DOI: 10.1021/acs.jpcb.0c10263] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Binding free energy calculations have become increasingly valuable to drive decision making in drug discovery projects. However, among other issues, inadequate sampling can reduce accuracy, limiting the value of the technique. In this paper, we apply absolute binding free energy calculations to ligands binding to T4 lysozyme L99A and HSP90 using equilibrium and nonequilibrium approaches. We highlight sampling problems encountered in these systems, such as slow side chain rearrangements and slow changes of water placement upon ligand binding. These same types of challenges are also likely to show up in other protein-ligand systems, and we propose some strategies to diagnose and test for such problems in alchemical free energy calculations. We also explore similarities and differences in how the equilibrium and the nonequilibrium approaches handle these problems. Our results show the large amount of work still to be done to make free energy calculations robust and reliable and provide insight for future research in this area.
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Affiliation(s)
- Hannah M Baumann
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, D-37077 Göttingen, Germany
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, D-37077 Göttingen, Germany
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
- Department of Chemistry, University of California, Irvine, California 92617, United States
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12
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Khuttan S, Azimi S, Wu JZ, Gallicchio E. Alchemical transformations for concerted hydration free energy estimation with explicit solvation. J Chem Phys 2021; 154:054103. [DOI: 10.1063/5.0036944] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
- Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Joe Z. Wu
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
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13
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Wade AD, Huggins DJ. Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method. J Chem Inf Model 2020; 60:5580-5594. [PMID: 32810401 DOI: 10.1021/acs.jcim.0c00610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this work, a novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented. This new method uses alchemical single step perturbation calculations to rapidly optimize the van der Waals interactions of a small molecule in a protein-ligand complex in order to maximize its binding affinity. The results of the optimizer are used to predict beneficial growth vectors on the ligand, and good agreement is found between the predictions from the optimizer and a more rigorous free energy calculation, with a Spearman's rank order correlation of 0.59. The advantage of the method presented here is the significant speed up of over 10-fold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed. Where experimental data were available, mutations from hydrogen to a methyl group at sites highlighted by the optimizer were calculated with MBAR, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol.
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Affiliation(s)
- Alexander D Wade
- TCM Group, Cavendish Laboratory, University of Cambridge, 19 J J Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - David J Huggins
- Tri-Institutional Therapeutics Discovery Institute, Belfer Research Building, 413 East 69th Street, 16th Floor, Box 300, New York, United States.,Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States
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14
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Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, Buch R, Fiorin G, Hénin J, Jiang W, McGreevy R, Melo MCR, Radak BK, Skeel RD, Singharoy A, Wang Y, Roux B, Aksimentiev A, Luthey-Schulten Z, Kalé LV, Schulten K, Chipot C, Tajkhorshid E. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 2020; 153:044130. [PMID: 32752662 PMCID: PMC7395834 DOI: 10.1063/5.0014475] [Citation(s) in RCA: 1379] [Impact Index Per Article: 344.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023] Open
Abstract
NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.
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Affiliation(s)
| | - David J. Hardy
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Julio D. C. Maia
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - John E. Stone
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - João V. Ribeiro
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Rafael C. Bernardi
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | | | - Giacomo Fiorin
- National Heart, Lung and Blood Institute, National
Institutes of Health, Bethesda, Maryland 20814,
USA
| | - Jérôme Hénin
- Laboratoire de Biochimie Théorique UPR 9080, CNRS
and Université de Paris, Paris, France
| | | | - Ryan McGreevy
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | | | - Brian K. Radak
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Robert D. Skeel
- School of Mathematical and Statistical Sciences,
Arizona State University, Tempe, Arizona 85281,
USA
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State
University, Tempe, Arizona 85281, USA
| | - Yi Wang
- Department of Physics, The Chinese University of
Hong Kong, Shatin, Hong Kong, China
| | - Benoît Roux
- Department of Biochemistry, University of
Chicago, Chicago, Illinois 60637, USA
| | | | | | | | | | - Christophe Chipot
- Authors to whom correspondence should be addressed:
and . URL: http://www.ks.uiuc.edu
| | - Emad Tajkhorshid
- Authors to whom correspondence should be addressed:
and . URL: http://www.ks.uiuc.edu
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15
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Nutho B, Pengthaisong S, Tankrathok A, Lee VS, Ketudat Cairns JR, Rungrotmongkol T, Hannongbua S. Structural Basis of Specific Glucoimidazole and Mannoimidazole Binding by Os3BGlu7. Biomolecules 2020; 10:biom10060907. [PMID: 32549280 PMCID: PMC7356692 DOI: 10.3390/biom10060907] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/09/2020] [Accepted: 06/11/2020] [Indexed: 01/07/2023] Open
Abstract
β-Glucosidases and β-mannosidases hydrolyze substrates that differ only in the epimer of the nonreducing terminal sugar moiety, but most such enzymes show a strong preference for one activity or the other. Rice Os3BGlu7 and Os7BGlu26 β-glycosidases show a less strong preference, but Os3BGlu7 and Os7BGlu26 prefer glucosides and mannosides, respectively. Previous studies of crystal structures with glucoimidazole (GIm) and mannoimidazole (MIm) complexes and metadynamic simulations suggested that Os7BGlu26 hydrolyzes mannosides via the B2,5 transition state (TS) conformation preferred for mannosides and glucosides via their preferred 4H3/4E TS conformation. However, MIm is weakly bound by both enzymes. In the present study, we found that MIm was not bound in the active site of crystallized Os3BGlu7, but GIm was tightly bound in the -1 subsite in a 4H3/4E conformation via hydrogen bonds with the surrounding residues. One-microsecond molecular dynamics simulations showed that GIm was stably bound in the Os3BGlu7 active site and the glycone-binding site with little distortion. In contrast, MIm initialized in the B2,5 conformation rapidly relaxed to a E3/4H3 conformation and moved out into a position in the entrance of the active site, where it bound more stably despite making fewer interactions. The lack of MIm binding in the glycone site in protein crystals and simulations implies that the energy required to distort MIm to the B2,5 conformation for optimal active site residue interactions is sufficient to offset the energy of those interactions in Os3BGlu7. This balance between distortion and binding energy may also provide a rationale for glucosidase versus mannosidase specificity in plant β-glycosidases.
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Affiliation(s)
- Bodee Nutho
- Center of Excellence in Computational Chemistry (CECC), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;
| | - Salila Pengthaisong
- School of Chemistry, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand; (S.P.); (A.T.)
- Center for Biomolecular Structure, Function and Application, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Anupong Tankrathok
- School of Chemistry, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand; (S.P.); (A.T.)
- Center for Biomolecular Structure, Function and Application, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Vannajan Sanghiran Lee
- Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - James R. Ketudat Cairns
- School of Chemistry, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand; (S.P.); (A.T.)
- Center for Biomolecular Structure, Function and Application, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
- Correspondence: (J.R.K.C.); (T.R.); (S.H.); Tel.: +66-4422-4304 (J.R.K.C.); +66-2218-5426 (T.R.); +66-2218-7602 (S.H.)
| | - Thanyada Rungrotmongkol
- Biocatalyst and Environmental Biotechnology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand
- Correspondence: (J.R.K.C.); (T.R.); (S.H.); Tel.: +66-4422-4304 (J.R.K.C.); +66-2218-5426 (T.R.); +66-2218-7602 (S.H.)
| | - Supot Hannongbua
- Center of Excellence in Computational Chemistry (CECC), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;
- Correspondence: (J.R.K.C.); (T.R.); (S.H.); Tel.: +66-4422-4304 (J.R.K.C.); +66-2218-5426 (T.R.); +66-2218-7602 (S.H.)
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16
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Hahn DF, König G, Hünenberger PH. Overcoming Orthogonal Barriers in Alchemical Free Energy Calculations: On the Relative Merits of λ-Variations, λ-Extrapolations, and Biasing. J Chem Theory Comput 2020; 16:1630-1645. [DOI: 10.1021/acs.jctc.9b00853] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- David F. Hahn
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Gerhard König
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Philippe H. Hünenberger
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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17
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Cappel D, Jerome S, Hessler G, Matter H. Impact of Different Automated Binding Pose Generation Approaches on Relative Binding Free Energy Simulations. J Chem Inf Model 2020; 60:1432-1444. [DOI: 10.1021/acs.jcim.9b01118] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | - Steven Jerome
- Schrödinger Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Gerhard Hessler
- Integrated Drug Discovery (IDD), Synthetic Molecular Design, Sanofi-Aventis Deutschland GmbH, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Hans Matter
- Integrated Drug Discovery (IDD), Synthetic Molecular Design, Sanofi-Aventis Deutschland GmbH, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany
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18
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Armacost KA, Riniker S, Cournia Z. Novel Directions in Free Energy Methods and Applications. J Chem Inf Model 2020; 60:1-5. [DOI: 10.1021/acs.jcim.9b01174] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Kira A. Armacost
- Computational and Structural Chemistry, MRL, Merck & Co., Inc. West Point, Pennsylvania 19486, United States
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens, Soranou Ephessiou 4, 11527 Athens, Greece
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19
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Hu X, Maffucci I, Contini A. Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations. Curr Med Chem 2020; 26:7598-7622. [DOI: 10.2174/0929867325666180514110824] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 02/26/2018] [Accepted: 04/18/2018] [Indexed: 12/30/2022]
Abstract
Background:
The inclusion of direct effects mediated by water during the ligandreceptor
recognition is a hot-topic of modern computational chemistry applied to drug discovery
and development. Docking or virtual screening with explicit hydration is still debatable,
despite the successful cases that have been presented in the last years. Indeed, how to select
the water molecules that will be included in the docking process or how the included waters
should be treated remain open questions.
Objective:
In this review, we will discuss some of the most recent methods that can be used in
computational drug discovery and drug development when the effect of a single water, or of a
small network of interacting waters, needs to be explicitly considered.
Results:
Here, we analyse the software to aid the selection, or to predict the position, of water
molecules that are going to be explicitly considered in later docking studies. We also present
software and protocols able to efficiently treat flexible water molecules during docking, including
examples of applications. Finally, we discuss methods based on molecular dynamics
simulations that can be used to integrate docking studies or to reliably and efficiently compute
binding energies of ligands in presence of interfacial or bridging water molecules.
Conclusions:
Software applications aiding the design of new drugs that exploit water molecules,
either as displaceable residues or as bridges to the receptor, are constantly being developed.
Although further validation is needed, workflows that explicitly consider water will
probably become a standard for computational drug discovery soon.
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Affiliation(s)
- Xiao Hu
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
| | - Irene Maffucci
- Pasteur, Département de Chimie, École Normale Supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 75005 Paris, France
| | - Alessandro Contini
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
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20
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Sakae Y, Zhang BW, Levy RM, Deng N. Absolute Protein Binding Free Energy Simulations for Ligands with Multiple Poses, a Thermodynamic Path That Avoids Exhaustive Enumeration of the Poses. J Comput Chem 2020; 41:56-68. [PMID: 31621932 PMCID: PMC7140983 DOI: 10.1002/jcc.26078] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 08/07/2019] [Accepted: 08/31/2019] [Indexed: 12/28/2022]
Abstract
We propose a free energy calculation method for receptor-ligand binding, which have multiple binding poses that avoids exhaustive enumeration of the poses. For systems with multiple binding poses, the standard procedure is to enumerate orientations of the binding poses, restrain the ligand to each orientation, and then, calculate the binding free energies for each binding pose. In this study, we modify a part of the thermodynamic cycle in order to sample a broader conformational space of the ligand in the binding site. This modification leads to more accurate free energy calculation without performing separate free energy simulations for each binding pose. We applied our modification to simple model host-guest systems as a test, which have only two binding poses, by using a single decoupling method (SDM) in implicit solvent. The results showed that the binding free energies obtained from our method without knowing the two binding poses were in good agreement with the benchmark results obtained by explicit enumeration of the binding poses. Our method is applicable to other alchemical binding free energy calculation methods such as the double decoupling method (DDM) in explicit solvent. We performed a calculation for a protein-ligand system with explicit solvent using our modified thermodynamic path. The results of the free energy simulation along our modified path were in good agreement with the results of conventional DDM, which requires a separate binding free energy calculation for each of the binding poses of the example of phenol binding to T4 lysozyme in explicit solvent. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Yoshitake Sakae
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
| | - Bin W Zhang
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
- Department of Chemistry, Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York, 10038
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21
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Pal RK, Gallicchio E. Perturbation potentials to overcome order/disorder transitions in alchemical binding free energy calculations. J Chem Phys 2019; 151:124116. [DOI: 10.1063/1.5123154] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Rajat K. Pal
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
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22
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Kiani YS, Ranaghan KE, Jabeen I, Mulholland AJ. Molecular Dynamics Simulation Framework to Probe the Binding Hypothesis of CYP3A4 Inhibitors. Int J Mol Sci 2019; 20:E4468. [PMID: 31510073 PMCID: PMC6769491 DOI: 10.3390/ijms20184468] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 08/22/2019] [Accepted: 09/01/2019] [Indexed: 12/20/2022] Open
Abstract
The Cytochrome P450 family of heme-containing proteins plays a major role in catalyzing phase I metabolic reactions, and the CYP3A4 subtype is responsible for the metabolism of many currently marketed drugs. Additionally, CYP3A4 has an inherent affinity for a broad spectrum of structurally diverse chemical entities, often leading to drug-drug interactions mediated by the inhibition or induction of the metabolic enzyme. The current study explores the binding of selected highly efficient CYP3A4 inhibitors by docking and molecular dynamics (MD) simulation protocols and their binding free energy calculated using the WaterSwap method. The results indicate the importance of binding pocket residues including Phe57, Arg105, Arg106, Ser119, Arg212, Phe213, Thr309, Ser312, Ala370, Arg372, Glu374, Gly481 and Leu483 for interaction with CYP3A4 inhibitors. The residue-wise decomposition of the binding free energy from the WaterSwap method revealed the importance of binding site residues Arg106 and Arg372 in the stabilization of all the selected CYP3A4-inhibitor complexes. The WaterSwap binding energies were further complemented with the MM(GB/PB)SA results and it was observed that the binding energies calculated by both methods do not differ significantly. Overall, our results could guide towards the use of multiple computational approaches to achieve a better understanding of CYP3A4 inhibition, subsequently leading to the design of highly specific and efficient new chemical entities with suitable ADMETox properties and reduced side effects.
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Affiliation(s)
- Yusra Sajid Kiani
- Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Kara E Ranaghan
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, UK.
| | - Ishrat Jabeen
- Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, UK.
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23
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Vilseck JZ, Sohail N, Hayes RL, Brooks CL. Overcoming Challenging Substituent Perturbations with Multisite λ-Dynamics: A Case Study Targeting β-Secretase 1. J Phys Chem Lett 2019; 10:4875-4880. [PMID: 31386370 PMCID: PMC7015761 DOI: 10.1021/acs.jpclett.9b02004] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alchemical free energy calculations have made a dramatic impact upon the field of structure-based drug design by allowing functional group modifications to be explored computationally prior to experimental synthesis and assay evaluation, thereby informing and directing synthetic strategies. In furthering the advancement of this area, a series of 21 β-secretase 1 (BACE1) inhibitors developed by Janssen Pharmaceuticals were examined to evaluate the ability to explore large substituent perturbations, some of which contain scaffold modifications, with multisite λ-dynamics (MSλD), an innovative alchemical free energy framework. Our findings indicate that MSλD is able to efficiently explore all structurally diverse ligand end-states simultaneously within a single MD simulation with a high degree of precision and with reduced computational costs compared to the widely used approach TI/MBAR. Furthermore, computational predictions were shown to be accurate to within 0.5-0.8 kcal/mol when CM1A partial atomic charges were combined with CHARMM or OPLS-AA-based force fields, demonstrating that MSλD is force field independent and a viable alternative to FEP or TI approaches for drug design.
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Affiliation(s)
- Jonah Z. Vilseck
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
| | - Noor Sohail
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
| | - Ryan L. Hayes
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
| | - Charles L. Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109
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24
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Käck H, Doyle K, Hughes SJ, Bodnarchuk MS, Lönn H, Van De Poël A, Palmer N. DPP1 Inhibitors: Exploring the Role of Water in the S2 Pocket of DPP1 with Substituted Pyrrolidines. ACS Med Chem Lett 2019; 10:1222-1227. [PMID: 31413809 DOI: 10.1021/acsmedchemlett.9b00261] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 07/15/2019] [Indexed: 12/22/2022] Open
Abstract
A series of pyrrolidine amino nitrile DPP1 inhibitors have been developed and characterized. The S2 pocket structure-activity relationship for these compounds shows significant gains in potency for DPP1 from interacting further with target residues and a network of water molecules in the binding pocket. Herein we describe the X-ray crystal structures of several of these compounds alongside an analysis of factors influencing the inhibitory potency toward DPP1 of which stabilization of the water network, demonstrated using Grand Canonical Monte Carlo simulations and free energy calculations, is attributed as a main factor.
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Affiliation(s)
- Helena Käck
- Structure, Biophysics and Fragments, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Kevin Doyle
- Charles River Discovery Services, Cambridge, U.K
| | - Samantha J. Hughes
- Charles River Discovery Services, Cambridge, U.K
- Medicinal Chemistry, Oncology R&D, AstraZeneca, Cambridge, U.K
| | | | - Hans Lönn
- Medicinal Chemistry, Respiratory, Inflammation and Autoimmune (RIA), BioPharmaceuticals R&D, AstraZeneca (RIA), Gothenburg, Sweden
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25
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Abstract
This Review illustrates the evaluation of permeability of lipid membranes from molecular dynamics (MD) simulation primarily using water and oxygen as examples. Membrane entrance, translocation, and exit of these simple permeants (one hydrophilic and one hydrophobic) can be simulated by conventional MD, and permeabilities can be evaluated directly by Fick's First Law, transition rates, and a global Bayesian analysis of the inhomogeneous solubility-diffusion model. The assorted results, many of which are applicable to simulations of nonbiological membranes, highlight the limitations of the homogeneous solubility diffusion model; support the utility of inhomogeneous solubility diffusion and compartmental models; underscore the need for comparison with experiment for both simple solvent systems (such as water/hexadecane) and well-characterized membranes; and demonstrate the need for microsecond simulations for even simple permeants like water and oxygen. Undulations, subdiffusion, fractional viscosity dependence, periodic boundary conditions, and recent developments in the field are also discussed. Last, while enhanced sampling methods and increasingly sophisticated treatments of diffusion add substantially to the repertoire of simulation-based approaches, they do not address directly the critical need for force fields with polarizability and multipoles, and constant pH methods.
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Affiliation(s)
- Richard M Venable
- Laboratory of Computational Biology, National Lung, Heart, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Andreas Krämer
- Laboratory of Computational Biology, National Lung, Heart, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Richard W Pastor
- Laboratory of Computational Biology, National Lung, Heart, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
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26
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Hahn DF, Hünenberger PH. Alchemical Free-Energy Calculations by Multiple-Replica λ-Dynamics: The Conveyor Belt Thermodynamic Integration Scheme. J Chem Theory Comput 2019; 15:2392-2419. [PMID: 30821973 DOI: 10.1021/acs.jctc.8b00782] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
A new method is proposed to calculate alchemical free-energy differences based on molecular dynamics (MD) simulations, called the conveyor belt thermodynamic integration (CBTI) scheme. As in thermodynamic integration (TI), K replicas of the system are simulated at different values of the alchemical coupling parameter λ. The number K is taken to be even, and the replicas are equally spaced on a forward-turn-backward-turn path, akin to a conveyor belt (CB) between the two physical end-states; and as in λ-dynamics (λD), the λ-values associated with the individual systems evolve in time along the simulation. However, they do so in a concerted fashion, determined by the evolution of a single dynamical variable Λ of period 2π controlling the advance of the entire CB. Thus, a change of Λ is always associated with K/2 equispaced replicas moving forward and K/2 equispaced replicas moving backward along λ. As a result, the effective free-energy profile of the replica system along Λ is periodic of period 2 πK-1, and the magnitude of its variations decreases rapidly upon increasing K, at least as K-1 in the limit of large K. When a sufficient number of replicas is used, these variations become small, which enables a complete and quasi-homogeneous coverage of the λ-range by the replica system, without application of any biasing potential. If desired, a memory-based biasing potential can still be added to further homogenize the sampling, the preoptimization of which is computationally inexpensive. The final free-energy profile along λ is calculated similarly to TI, by binning of the Hamiltonian λ-derivative as a function of λ considering all replicas simultaneously, followed by quadrature integration. The associated quadrature error can be kept very low owing to the continuous and quasi-homogeneous λ-sampling. The CBTI scheme can be viewed as a continuous/deterministic/dynamical analog of the Hamiltonian replica-exchange/permutation (HRE/HRP) schemes or as a correlated multiple-replica analog of the λD or λ-local elevation umbrella sampling (λ-LEUS) schemes. Compared to TI, it shares the advantage of the latter schemes in terms of enhanced orthogonal sampling, i.e. the availability of variable-λ paths to circumvent conformational barriers present at specific λ-values. Compared to HRE/HRP, it permits a deterministic and continuous sampling of the λ-range, is expected to be less sensitive to possible artifacts of the thermo- and barostating schemes, and bypasses the need to carefully preselect a λ-ladder and a swapping-attempt frequency. Compared to λ-LEUS, it eliminates (or drastically reduces) the dead time associated with the preoptimization of a biasing potential. The goal of this article is to provide the mathematical/physical formulation of the proposed CBTI scheme, along with an initial application of the method to the calculation of the hydration free energy of methanol.
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Affiliation(s)
- David F Hahn
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences , ETH Zürich , Vladimir-Prelog-Weg 2 , 8093 Zürich , Switzerland
| | - Philippe H Hünenberger
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences , ETH Zürich , Vladimir-Prelog-Weg 2 , 8093 Zürich , Switzerland
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27
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Gilabert JF, Lecina D, Estrada J, Guallar V. Monte Carlo Techniques for Drug Design: The Success Case of PELE. BIOMOLECULAR SIMULATIONS IN STRUCTURE-BASED DRUG DISCOVERY 2018. [DOI: 10.1002/9783527806836.ch5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Joan F. Gilabert
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Daniel Lecina
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Jorge Estrada
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Victor Guallar
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
- ICREA; Passeig Lluís Companys 23 08010 Barcelona Spain
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28
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Witek J, Wang S, Schroeder B, Lingwood R, Dounas A, Roth HJ, Fouché M, Blatter M, Lemke O, Keller B, Riniker S. Rationalization of the Membrane Permeability Differences in a Series of Analogue Cyclic Decapeptides. J Chem Inf Model 2018; 59:294-308. [PMID: 30457855 DOI: 10.1021/acs.jcim.8b00485] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cyclization and selected backbone N-methylations are found to be often necessary but not sufficient conditions for peptidic drugs to have a good bioavailability. Thus, the design of cyclic peptides with good passive membrane permeability and good solubility remains a challenge. The backbone scaffold of a recently published series of cyclic decapeptides with six selected backbone N-methylations was designed to favor the adoption of a closed conformation with β-turns and four transannular hydrogen bonds. Although this conformation was indeed adopted by the peptides as determined by NMR measurements, substantial differences in the membrane permeability were observed. In this work, we aim to rationalize the impact of discrete side chain modifications on membrane permeability for six of these cyclic decapeptides. The thermodynamic and kinetic properties were investigated using molecular dynamics simulations and Markov state modeling in water and chloroform. The study highlights the influence that side-chain modifications can have on the backbone conformation. Peptides with a d-proline in the β-turns were more likely to adopt, even in water, the closed conformation with transannular hydrogen bonds, which facilitates transition through the membrane. The population of the closed conformation in water was found to correlate positively with PAMPA log Pe.
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Affiliation(s)
- Jagna Witek
- Laboratory of Physical Chemistry , ETH Zürich , Vladimir-Prelog-Weg 2 , 8093 Zürich , Switzerland
| | - Shuzhe Wang
- Laboratory of Physical Chemistry , ETH Zürich , Vladimir-Prelog-Weg 2 , 8093 Zürich , Switzerland
| | - Benjamin Schroeder
- Laboratory of Physical Chemistry , ETH Zürich , Vladimir-Prelog-Weg 2 , 8093 Zürich , Switzerland
| | - Robin Lingwood
- Laboratory of Physical Chemistry , ETH Zürich , Vladimir-Prelog-Weg 2 , 8093 Zürich , Switzerland
| | - Andreas Dounas
- Laboratory of Physical Chemistry , ETH Zürich , Vladimir-Prelog-Weg 2 , 8093 Zürich , Switzerland
| | - Hans-Jörg Roth
- Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus , 4056 Basel , Switzerland
| | - Marianne Fouché
- Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus , 4056 Basel , Switzerland
| | - Markus Blatter
- Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus , 4056 Basel , Switzerland
| | - Oliver Lemke
- Department of Biology, Chemistry, Pharmacy , Freie Universität Berlin , Takustrasse 3 , 14195 Berlin , Germany
| | - Bettina Keller
- Department of Biology, Chemistry, Pharmacy , Freie Universität Berlin , Takustrasse 3 , 14195 Berlin , Germany
| | - Sereina Riniker
- Department of Biology, Chemistry, Pharmacy , Freie Universität Berlin , Takustrasse 3 , 14195 Berlin , Germany
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29
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Bruce Macdonald HE, Cave-Ayland C, Ross GA, Essex JW. Ligand Binding Free Energies with Adaptive Water Networks: Two-Dimensional Grand Canonical Alchemical Perturbations. J Chem Theory Comput 2018; 14:6586-6597. [PMID: 30451501 PMCID: PMC6293443 DOI: 10.1021/acs.jctc.8b00614] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
![]()
Computational methods
to calculate ligand binding affinities are
increasing in popularity, due to improvements in simulation algorithms,
computational resources, and easy-to-use software. However, issues
can arise in relative ligand binding free energy simulations if the
ligands considered have different active site water networks, as simulations
are typically performed with a predetermined number of water molecules
(fixed N ensembles) in preassigned locations. If an alchemical perturbation
is attempted where the change should result in a different active
site water network, the water molecules may not be able to adapt appropriately
within the time scales of the simulations—particularly if the
active site is occluded. By combining the grand canonical ensemble
(μVT) to sample active site water molecules, with conventional
alchemical free energy methods, the water network is able to dynamically
adapt to the changing ligand. We refer to this approach as grand canonical
alchemical perturbation (GCAP). In this work we demonstrate GCAP for
two systems; Scytalone Dehydratase (SD) and Adenosine A2A receptor. For both systems, GCAP is shown to perform
well at reproducing experimental binding affinities. Calculating the
relative binding affinities with a naïve, conventional
attempt to solvate the active site illustrates how poor results can
be if proper consideration of water molecules in occluded pockets
is neglected. GCAP results are shown to be consistent with time-consuming
double decoupling simulations. In addition, by obtaining the free
energy surface for ligand perturbations, as a function of both the
free energy coupling parameter and water chemical potential, it is
possible to directly deconvolute the binding energetics in terms of
protein–ligand direct interactions and protein binding site
hydration.
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Affiliation(s)
| | | | - Gregory A Ross
- Computational and Systems Biology Program, Sloan Kettering Institute , Memorial Sloan Kettering Cancer Center , New York, New York 10065 , United States
| | - Jonathan W Essex
- School of Chemistry , University of Southampton , Southampton , SO17 1BJ , U.K
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30
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Awoonor-Williams E, Rowley CN. How Reactive are Druggable Cysteines in Protein Kinases? J Chem Inf Model 2018; 58:1935-1946. [DOI: 10.1021/acs.jcim.8b00454] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Ernest Awoonor-Williams
- Department of Chemistry, Memorial University of Newfoundland, St. John’s, NL A1B 3X9, Canada
| | - Christopher N. Rowley
- Department of Chemistry, Memorial University of Newfoundland, St. John’s, NL A1B 3X9, Canada
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31
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Vermaas JV, Rempe SB, Tajkhorshid E. Electrostatic lock in the transport cycle of the multidrug resistance transporter EmrE. Proc Natl Acad Sci U S A 2018; 115:E7502-E7511. [PMID: 30026196 PMCID: PMC6094130 DOI: 10.1073/pnas.1722399115] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
EmrE is a small, homodimeric membrane transporter that exploits the established electrochemical proton gradient across the Escherichia coli inner membrane to export toxic polyaromatic cations, prototypical of the wider small-multidrug resistance transporter family. While prior studies have established many fundamental aspects of the specificity and rate of substrate transport in EmrE, low resolution of available structures has hampered identification of the transport coupling mechanism. Here we present a complete, refined atomic structure of EmrE optimized against available cryo-electron microscopy (cryo-EM) data to delineate the critical interactions by which EmrE regulates its conformation during the transport process. With the model, we conduct molecular dynamics simulations of the transporter in explicit membranes to probe EmrE dynamics under different substrate loading and conformational states, representing different intermediates in the transport cycle. The refined model is stable under extended simulation. The water dynamics in simulation indicate that the hydrogen-bonding networks around a pair of solvent-exposed glutamate residues (E14) depend on the loading state of EmrE. One specific hydrogen bond from a tyrosine (Y60) on one monomer to a glutamate (E14) on the opposite monomer is especially critical, as it locks the protein conformation when the glutamate is deprotonated. The hydrogen bond provided by Y60 lowers the [Formula: see text] of one glutamate relative to the other, suggesting both glutamates should be protonated for the hydrogen bond to break and a substrate-free transition to take place. These findings establish the molecular mechanism for the coupling between proton transfer reactions and protein conformation in this proton-coupled secondary transporter.
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Affiliation(s)
- Josh V Vermaas
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Biological and Engineering Sciences Center, Sandia National Laboratories, Albuquerque, NM 87185
| | - Susan B Rempe
- Biological and Engineering Sciences Center, Sandia National Laboratories, Albuquerque, NM 87185
| | - Emad Tajkhorshid
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801;
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
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32
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Nittinger E, Flachsenberg F, Bietz S, Lange G, Klein R, Rarey M. Placement of Water Molecules in Protein Structures: From Large-Scale Evaluations to Single-Case Examples. J Chem Inf Model 2018; 58:1625-1637. [DOI: 10.1021/acs.jcim.8b00271] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Eva Nittinger
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Florian Flachsenberg
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Stefan Bietz
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Gudrun Lange
- Bayer CropScience AG, Industriepark Hoechst G836, 65926 Frankfurt am Main, Germany
| | - Robert Klein
- Bayer CropScience AG, Industriepark Hoechst G836, 65926 Frankfurt am Main, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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33
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König G, Brooks BR, Thiel W, York DM. On the convergence of multi-scale free energy simulations. MOLECULAR SIMULATION 2018; 44:1062-1081. [PMID: 30581251 DOI: 10.1080/08927022.2018.1475741] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
In this work we employ simple model systems to evaluate the relative performance of two of the most important free energy methods: The Zwanzig equation (also known as "Free energy perturbation") and Bennett's acceptance ratio method (BAR). Although our examples should be transferable to other kinds of free energy simulations, we focus on applications of multi-scale free energy simulations. Such calculations are especially complex, since they connect two different levels of theory with very different requirements in terms of speed, accuracy, sampling and parallelizability. We try to reconcile all those different factors by developing some simple criteria to guide the early stages of the development of a free energy protocol. This is accomplished by quantifying how many λ intermediate steps and how many potential energy evaluations are necessary in order to reach a certain level of convergence.
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Affiliation(s)
- Gerhard König
- Max-Planck-Institut für Kohlenforschung, 45470 Mülheim an der Ruhr, Germany, EU.,Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.,Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Walter Thiel
- Max-Planck-Institut für Kohlenforschung, 45470 Mülheim an der Ruhr, Germany, EU
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
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34
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Giese TJ, York DM. A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method. J Chem Theory Comput 2018; 14:1564-1582. [PMID: 29357243 PMCID: PMC5849537 DOI: 10.1021/acs.jctc.7b01175] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
There has been a resurgence of interest in free energy methods motivated by the performance enhancements offered by molecular dynamics (MD) software written for specialized hardware, such as graphics processing units (GPUs). In this work, we exploit the properties of a parameter-interpolated thermodynamic integration (PI-TI) method to connect states by their molecular mechanical (MM) parameter values. This pathway is shown to be better behaved for Mg2+ → Ca2+ transformations than traditional linear alchemical pathways (with and without soft-core potentials). The PI-TI method has the practical advantage that no modification of the MD code is required to propagate the dynamics, and unlike with linear alchemical mixing, only one electrostatic evaluation is needed (e.g., single call to particle-mesh Ewald) leading to better performance. In the case of AMBER, this enables all the performance benefits of GPU-acceleration to be realized, in addition to unlocking the full spectrum of features available within the MD software, such as Hamiltonian replica exchange (HREM). The TI derivative evaluation can be accomplished efficiently in a post-processing step by reanalyzing the statistically independent trajectory frames in parallel for high throughput. We also show how one can evaluate the particle mesh Ewald contribution to the TI derivative evaluation without needing to perform two reciprocal space calculations. We apply the PI-TI method with HREM on GPUs in AMBER to predict p Ka values in double stranded RNA molecules and make comparison with experiments. Convergence to under 0.25 units for these systems required 100 ns or more of sampling per window and coupling of windows with HREM. We find that MM charges derived from ab initio QM/MM fragment calculations improve the agreement between calculation and experimental results.
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Affiliation(s)
- Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
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35
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Callegari D, Ranaghan KE, Woods CJ, Minari R, Tiseo M, Mor M, Mulholland AJ, Lodola A. L718Q mutant EGFR escapes covalent inhibition by stabilizing a non-reactive conformation of the lung cancer drug osimertinib. Chem Sci 2018; 9:2740-2749. [PMID: 29732058 PMCID: PMC5911825 DOI: 10.1039/c7sc04761d] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 02/03/2018] [Indexed: 12/15/2022] Open
Abstract
Impact of L718Q mutation on the inhibitory activity of osimertinib on EGFR revealed by free-energy simulations.
Osimertinib is a third-generation inhibitor approved for the treatment of non-small cell lung cancer. It overcomes resistance to first-generation inhibitors by incorporating an acrylamide group which alkylates Cys797 of EGFR T790M. The mutation of a residue in the P-loop (L718Q) was shown to cause resistance to osimertinib, but the molecular mechanism of this process is unknown. Here, we investigated the inhibitory process for EGFR T790M (susceptible to osimertinib) and EGFR T790M/L718Q (resistant to osimertinib), by modelling the chemical step (i.e., alkylation of Cys797) using QM/MM simulations and the recognition step by MD simulations coupled with free-energy calculations. The calculations indicate that L718Q has a negligible impact on both the activation energy for Cys797 alkylation and the free-energy of binding for the formation of the non-covalent complex. The results show that Gln718 affects the conformational space of the EGFR–osimertinib complex, stabilizing a conformation of acrylamide which prevents reaction with Cys797.
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Affiliation(s)
- D Callegari
- Department of Food and Drug , University of Parma , Parma , Italy .
| | - K E Ranaghan
- School of Chemistry , University of Bristol , Bristol , UK
| | - C J Woods
- School of Chemistry , University of Bristol , Bristol , UK
| | - R Minari
- Medical Oncology Unit , University Hospital of Parma , Parma , Italy
| | - M Tiseo
- Medical Oncology Unit , University Hospital of Parma , Parma , Italy
| | - M Mor
- Department of Food and Drug , University of Parma , Parma , Italy .
| | - A J Mulholland
- School of Chemistry , University of Bristol , Bristol , UK
| | - A Lodola
- Department of Food and Drug , University of Parma , Parma , Italy .
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36
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Fowler PW, Cole K, Gordon NC, Kearns AM, Llewelyn MJ, Peto TEA, Crook DW, Walker AS. Robust Prediction of Resistance to Trimethoprim in Staphylococcus aureus. Cell Chem Biol 2018; 25:339-349.e4. [PMID: 29307840 DOI: 10.1016/j.chembiol.2017.12.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/24/2017] [Accepted: 12/08/2017] [Indexed: 01/28/2023]
Abstract
The rise of antibiotic resistance threatens modern medicine; to combat it new diagnostic methods are required. Sequencing the whole genome of a pathogen offers the potential to accurately determine which antibiotics will be effective to treat a patient. A key limitation of this approach is that it cannot classify rare or previously unseen mutations. Here we demonstrate that alchemical free energy methods, a well-established class of methods from computational chemistry, can successfully predict whether mutations in Staphylococcus aureus dihydrofolate reductase confer resistance to trimethoprim. We also show that the method is quantitatively accurate by calculating how much the most common resistance-conferring mutation, F99Y, reduces the binding free energy of trimethoprim and comparing predicted and experimentally measured minimum inhibitory concentrations for seven different mutations. Finally, by considering up to 32 free energy calculations for each mutation, we estimate its specificity and sensitivity.
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Affiliation(s)
- Philip W Fowler
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford OX3 9DU, UK.
| | - Kevin Cole
- Department of Infectious Diseases and Microbiology, Royal Sussex County Hospital, Brighton, Brighton and Sussex Medical School, Brighton BN1 9PS, UK
| | - N Claire Gordon
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford OX3 9DU, UK
| | - Angela M Kearns
- Antimicrobial Resistance and Healthcare Associated Infections Reference Unit, Public Health England, Colindale NW9 5EQ, UK
| | - Martin J Llewelyn
- Department of Infectious Diseases and Microbiology, Royal Sussex County Hospital, Brighton, Brighton and Sussex Medical School, Brighton BN1 9PS, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford OX3 9DU, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford OX3 9DU, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford OX3 9DU, UK
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37
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Ross GA, Bruce Macdonald HE, Cave-Ayland C, Cabedo Martinez AI, Essex JW. Replica-Exchange and Standard State Binding Free Energies with Grand Canonical Monte Carlo. J Chem Theory Comput 2017; 13:6373-6381. [PMID: 29091438 PMCID: PMC5729546 DOI: 10.1021/acs.jctc.7b00738] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
![]()
The
ability of grand canonical Monte Carlo (GCMC) to create and
annihilate molecules in a given region greatly aids the identification
of water sites and water binding free energies in protein cavities.
However, acceptance rates without the application of biased moves
can be low, resulting in large variations in the observed water occupancies.
Here, we show that replica-exchange of the chemical potential significantly
reduces the variance of the GCMC data. This improvement comes at a
negligible increase in computational expense when simulations comprise
of runs at different chemical potentials. Replica-exchange GCMC is
also found to substantially increase the precision of water binding
free energies as calculated with grand canonical integration, which
has allowed us to address a missing standard state correction.
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Affiliation(s)
- Gregory A Ross
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center , New York, New York 10065, United States
| | | | | | - Ana I Cabedo Martinez
- Department of Chemistry, University of Southampton , Southampton, SO17 1BJ, United Kingdom
| | - Jonathan W Essex
- Department of Chemistry, University of Southampton , Southampton, SO17 1BJ, United Kingdom
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38
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Witek J, Mühlbauer M, Keller BG, Blatter M, Meissner A, Wagner T, Riniker S. Interconversion Rates between Conformational States as Rationale for the Membrane Permeability of Cyclosporines. Chemphyschem 2017; 18:3309-3314. [DOI: 10.1002/cphc.201700995] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Indexed: 11/10/2022]
Affiliation(s)
- Jagna Witek
- Laboratory of Physical Chemistry; ETH Zürich; Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
| | - Max Mühlbauer
- Laboratory of Physical Chemistry; ETH Zürich; Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
| | - Bettina G. Keller
- Department of Biology, Chemistry, Pharmacy; Freie Universität Berlin; Takustrasse 3 14195 Berlin Germany
| | - Markus Blatter
- Novartis Institutes for BioMedical Research; Novartis Pharma AG; Novartis Campus 4056 Basel Switzerland
| | - Axel Meissner
- Novartis Institutes for BioMedical Research; Novartis Pharma AG; Novartis Campus 4056 Basel Switzerland
| | - Trixie Wagner
- Novartis Institutes for BioMedical Research; Novartis Pharma AG; Novartis Campus 4056 Basel Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry; ETH Zürich; Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
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39
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Sidler D, Cristòfol-Clough M, Riniker S. Efficient Round-Trip Time Optimization for Replica-Exchange Enveloping Distribution Sampling (RE-EDS). J Chem Theory Comput 2017; 13:3020-3030. [PMID: 28510459 DOI: 10.1021/acs.jctc.7b00286] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Replica-exchange enveloping distribution sampling (RE-EDS) allows the efficient estimation of free-energy differences between multiple end-states from a single molecular dynamics (MD) simulation. In EDS, a reference state is sampled, which can be tuned by two types of parameters, i.e., smoothness parameters(s) and energy offsets, such that all end-states are sufficiently sampled. However, the choice of these parameters is not trivial. Replica exchange (RE) or parallel tempering is a widely applied technique to enhance sampling. By combining EDS with the RE technique, the parameter choice problem could be simplified and the challenge shifted toward an optimal distribution of the replicas in the smoothness-parameter space. The choice of a certain replica distribution can alter the sampling efficiency significantly. In this work, global round-trip time optimization (GRTO) algorithms are tested for the use in RE-EDS simulations. In addition, a local round-trip time optimization (LRTO) algorithm is proposed for systems with slowly adapting environments, where a reliable estimate for the round-trip time is challenging to obtain. The optimization algorithms were applied to RE-EDS simulations of a system of nine small-molecule inhibitors of phenylethanolamine N-methyltransferase (PNMT). The energy offsets were determined using our recently proposed parallel energy-offset (PEOE) estimation scheme. While the multistate GRTO algorithm yielded the best replica distribution for the ligands in water, the multistate LRTO algorithm was found to be the method of choice for the ligands in complex with PNMT. With this, the 36 alchemical free-energy differences between the nine ligands were calculated successfully from a single RE-EDS simulation 10 ns in length. Thus, RE-EDS presents an efficient method for the estimation of relative binding free energies.
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Affiliation(s)
- Dominik Sidler
- Laboratory of Physical Chemistry, ETH Zürich , Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | | | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich , Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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40
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Spyrakis F, Ahmed MH, Bayden AS, Cozzini P, Mozzarelli A, Kellogg GE. The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery. J Med Chem 2017; 60:6781-6827. [PMID: 28475332 DOI: 10.1021/acs.jmedchem.7b00057] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.
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Affiliation(s)
- Francesca Spyrakis
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino , Via Pietro Giuria 9, 10125 Torino, Italy
| | - Mostafa H Ahmed
- Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University , Richmond, Virginia 23298-0540, United States
| | - Alexander S Bayden
- CMD Bioscience , 5 Science Park, New Haven, Connecticut 06511, United States
| | - Pietro Cozzini
- Dipartimento di Scienze degli Alimenti e del Farmaco, Laboratorio di Modellistica Molecolare, Università degli Studi di Parma , Parco Area delle Scienze 59/A, 43121 Parma, Italy
| | - Andrea Mozzarelli
- Dipartimento di Scienze degli Alimenti e del Farmaco, Laboratorio di Biochimica, Università degli Studi di Parma , Parco Area delle Scienze 23/A, 43121 Parma, Italy.,Istituto di Biofisica, Consiglio Nazionale delle Ricerche , Via Moruzzi 1, 56124 Pisa, Italy
| | - Glen E Kellogg
- Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University , Richmond, Virginia 23298-0540, United States
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Sidler D, Schwaninger A, Riniker S. Replica exchange enveloping distribution sampling (RE-EDS): A robust method to estimate multiple free-energy differences from a single simulation. J Chem Phys 2017; 145:154114. [PMID: 27782485 DOI: 10.1063/1.4964781] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
In molecular dynamics (MD) simulations, free-energy differences are often calculated using free energy perturbation or thermodynamic integration (TI) methods. However, both techniques are only suited to calculate free-energy differences between two end states. Enveloping distribution sampling (EDS) presents an attractive alternative that allows to calculate multiple free-energy differences in a single simulation. In EDS, a reference state is simulated which "envelopes" the end states. The challenge of this methodology is the determination of optimal reference-state parameters to ensure equal sampling of all end states. Currently, the automatic determination of the reference-state parameters for multiple end states is an unsolved issue that limits the application of the methodology. To resolve this, we have generalised the replica-exchange EDS (RE-EDS) approach, introduced by Lee et al. [J. Chem. Theory Comput. 10, 2738 (2014)] for constant-pH MD simulations. By exchanging configurations between replicas with different reference-state parameters, the complexity of the parameter-choice problem can be substantially reduced. A new robust scheme to estimate the reference-state parameters from a short initial RE-EDS simulation with default parameters was developed, which allowed the calculation of 36 free-energy differences between nine small-molecule inhibitors of phenylethanolamine N-methyltransferase from a single simulation. The resulting free-energy differences were in excellent agreement with values obtained previously by TI and two-state EDS simulations.
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Affiliation(s)
- Dominik Sidler
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Arthur Schwaninger
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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42
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Effect of solvent model when probing protein dynamics with molecular dynamics. J Mol Graph Model 2017; 71:80-87. [DOI: 10.1016/j.jmgm.2016.11.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 11/02/2016] [Accepted: 11/03/2016] [Indexed: 12/17/2022]
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Montgomery AP, Xiao K, Wang X, Skropeta D, Yu H. Computational Glycobiology: Mechanistic Studies of Carbohydrate-Active Enzymes and Implication for Inhibitor Design. STRUCTURAL AND MECHANISTIC ENZYMOLOGY 2017; 109:25-76. [DOI: 10.1016/bs.apcsb.2017.04.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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44
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Lenselink E, Louvel J, Forti AF, van Veldhoven JPD, de Vries H, Mulder-Krieger T, McRobb FM, Negri A, Goose J, Abel R, van
Vlijmen HWT, Wang L, Harder E, Sherman W, IJzerman AP, Beuming T. Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation. ACS OMEGA 2016; 1:293-304. [PMID: 30023478 PMCID: PMC6044636 DOI: 10.1021/acsomega.6b00086] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 08/15/2016] [Indexed: 05/11/2023]
Abstract
The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.
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Affiliation(s)
- Eelke
B. Lenselink
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
| | - Julien Louvel
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
| | - Anna F. Forti
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
| | - Jacobus P. D. van Veldhoven
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
| | - Henk de Vries
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
| | - Thea Mulder-Krieger
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
| | - Fiona M. McRobb
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ana Negri
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Joseph Goose
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Herman W. T. van
Vlijmen
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
| | - Lingle Wang
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Edward Harder
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Woody Sherman
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Adriaan P. IJzerman
- Division
of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300 RA, The Netherlands
- E-mail: . Phone: +31-71-5274651. Fax: +31-71-5274277 (A.P.I.)
| | - Thijs Beuming
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
- E-mail: . Phone: +1 (212) 548-2333. Fax: +1 (212) 295-5801 (T.B.)
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Misini Ignjatović M, Caldararu O, Dong G, Muñoz-Gutierrez C, Adasme-Carreño F, Ryde U. Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. J Comput Aided Mol Des 2016; 30:707-730. [PMID: 27565797 PMCID: PMC5078160 DOI: 10.1007/s10822-016-9942-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 08/17/2016] [Indexed: 11/25/2022]
Abstract
We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970-1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.
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Affiliation(s)
- Majda Misini Ignjatović
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Octav Caldararu
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Geng Dong
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Camila Muñoz-Gutierrez
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Talca, Chile
| | - Francisco Adasme-Carreño
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Talca, Chile
| | - Ulf Ryde
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden.
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Michel J, Foloppe N, Essex JW. Rigorous Free Energy Calculations in Structure-Based Drug Design. Mol Inform 2016; 29:570-8. [PMID: 27463452 DOI: 10.1002/minf.201000051] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2010] [Accepted: 05/28/2010] [Indexed: 11/07/2022]
Abstract
Structure-based drug design could benefit greatly from computational methodologies that accurately predict the binding affinity of small compounds to target biomolecules. However, the current scoring functions used to rank compounds in virtual screens by docking are not sufficiently accurate to guide reliably the design of tight binding ligands. Thus, there is strong interest in methodologies based on molecular simulations of protein-ligand complexes which are perceived to be more accurate and, with advances in computing power, amenable to routine use. This report provides an overview of the technical details necessary to understand, execute and analyze binding free energy calculations, using free energy perturbation or thermodynamic integration methods. Examples of possible applications in structure-based drug design are discussed. Current methodological limitations are highlighted as well as a number of ongoing developments to improve the scope, reliability, and practicalities of free energy calculations. These efforts are paving the way for a more common use of free energy calculations in molecular design.
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Affiliation(s)
- Julien Michel
- Institute of Structural and Molecular Biology, The University of Edinburgh, Edinburgh, EH9 3JR, UK. .,Department of Chemistry, Yale University, New Haven, CT-06520, USA.
| | | | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
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47
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Bodnarchuk MS. Water, water, everywhere… It's time to stop and think. Drug Discov Today 2016; 21:1139-46. [DOI: 10.1016/j.drudis.2016.05.009] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 04/15/2016] [Accepted: 05/13/2016] [Indexed: 12/11/2022]
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Jin T, Yu H, Huang XF. Selective binding modes and allosteric inhibitory effects of lupane triterpenes on protein tyrosine phosphatase 1B. Sci Rep 2016; 6:20766. [PMID: 26865097 PMCID: PMC4749975 DOI: 10.1038/srep20766] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 01/04/2016] [Indexed: 12/31/2022] Open
Abstract
Protein Tyrosine Phosphatase 1B (PTP1B) has been recognized as a promising therapeutic target for treating obesity, diabetes, and certain cancers for over a decade. Previous drug design has focused on inhibitors targeting the active site of PTP1B. However, this has not been successful because the active site is positively charged and conserved among the protein tyrosine phosphatases. Therefore, it is important to develop PTP1B inhibitors with alternative inhibitory strategies. Using computational studies including molecular docking, molecular dynamics simulations, and binding free energy calculations, we found that lupane triterpenes selectively inhibited PTP1B by targeting its more hydrophobic and less conserved allosteric site. These findings were verified using two enzymatic assays. Furthermore, the cell culture studies showed that lupeol and betulinic acid inhibited the PTP1B activity stimulated by TNFα in neurons. Our study indicates that lupane triterpenes are selective PTP1B allosteric inhibitors with significant potential for treating those diseases with elevated PTP1B activity.
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Affiliation(s)
- Tiantian Jin
- Centre for Translational Neuroscience, School of Medicine, University of Wollongong, and Illawarra Health and Medical Research Institute (IHMRI), Wollongong, NSW 2522, Australia
| | - Haibo Yu
- School of Chemistry, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Xu-Feng Huang
- Centre for Translational Neuroscience, School of Medicine, University of Wollongong, and Illawarra Health and Medical Research Institute (IHMRI), Wollongong, NSW 2522, Australia
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Bodnarchuk MS, Heyes DM, Dini D, Chahine S, Edwards S. Role of Deprotonation Free Energies in pKa Prediction and Molecule Ranking. J Chem Theory Comput 2015; 10:2537-45. [PMID: 26580774 DOI: 10.1021/ct400914w] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A computationally efficient classical molecular simulation technique is derived for ranking the pKa values of a set of chemically similar congeneric molecules in an implicit solvent model of water. This uses the deprotonation free energy of the titratable group in the gas and aqueous phases obtained by thermodynamic integration (TI). For a series of alcohols and acids a strong linear correlation is demonstrated between the experimental pKa and the deprotonation free energy difference in the gas and liquid phases. These calculations also show that classical TI is more efficient than slow-growth TI in calculating deprotonation free energies for the series of molecules considered herein.
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Affiliation(s)
- M S Bodnarchuk
- Department of Mechanical Engineering, Imperial College London , Exhibition Road, London, SW7 2AZ, U.K
| | - D M Heyes
- Department of Mechanical Engineering, Imperial College London , Exhibition Road, London, SW7 2AZ, U.K
| | - D Dini
- Department of Mechanical Engineering, Imperial College London , Exhibition Road, London, SW7 2AZ, U.K
| | - S Chahine
- BP Marine Limited, Marine Technology Centre , Whitchurch Hill, Pangbourne, RG8 7QR, U.K
| | - S Edwards
- BP Marine Limited, Marine Technology Centre , Whitchurch Hill, Pangbourne, RG8 7QR, U.K
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50
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Ross GA, Bodnarchuk MS, Essex JW. Water Sites, Networks, And Free Energies with Grand Canonical Monte Carlo. J Am Chem Soc 2015; 137:14930-43. [PMID: 26509924 DOI: 10.1021/jacs.5b07940] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Water molecules play integral roles in the formation of many protein-ligand complexes, and recent computational efforts have been focused on predicting the thermodynamic properties of individual waters and how they may be exploited in rational drug design. However, when water molecules form highly coupled hydrogen-bonding networks, there is, as yet, no method that can rigorously calculate the free energy to bind the entire network or assess the degree of cooperativity between waters. In this work, we report theoretical and methodological developments to the grand canonical Monte Carlo simulation technique. Central to our results is a rigorous equation that can be used to calculate efficiently the binding free energies of water networks of arbitrary size and complexity. Using a single set of simulations, our methods can locate waters, estimate their binding affinities, capture the cooperativity of the water network, and evaluate the hydration free energy of entire protein binding sites. Our techniques have been applied to multiple test systems and compare favorably to thermodynamic integration simulations and experimental data. The implications of these methods in drug design are discussed.
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Affiliation(s)
- Gregory A Ross
- School of Chemistry, University of Southampton , Highfield, Southampton SO17 1BJ, United Kingdom
| | - Michael S Bodnarchuk
- School of Mechanical Engineering, Imperial College London , Exhibition Road, London, SW1 2AZ, United Kingdom
| | - Jonathan W Essex
- School of Chemistry, University of Southampton , Highfield, Southampton SO17 1BJ, United Kingdom
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