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Khuttan S, Gallicchio E. What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling. J Chem Theory Comput 2024; 20:1489-1501. [PMID: 38252868 PMCID: PMC10867849 DOI: 10.1021/acs.jctc.3c01250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.
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Affiliation(s)
- Sheenam Khuttan
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
| | - Emilio Gallicchio
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
- Ph.D.
Program in Chemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
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2
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Chen L, Wu Y, Wu C, Silveira A, Sherman W, Xu H, Gallicchio E. Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands. J Chem Inf Model 2024; 64:250-264. [PMID: 38147877 DOI: 10.1021/acs.jcim.3c01705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.
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Affiliation(s)
- Lieyang Chen
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Yujie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Chuanjie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Ana Silveira
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Huafeng Xu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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3
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Robo MT, Hayes RL, Ding X, Pulawski B, Vilseck JZ. Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling. Nat Commun 2023; 14:8515. [PMID: 38129400 PMCID: PMC10740020 DOI: 10.1038/s41467-023-44208-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol-1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol-1 or less). Notably, large efficiency gains over thermodynamic integration of 18-66-fold for small perturbations and 100-200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated.
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Affiliation(s)
- Michael T Robo
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Biosciences Research Institute, 1210 Waterway Blvd Ste. 2000, Indianapolis, IN, 46202, USA
| | - Ryan L Hayes
- Chemical and Biomolecular Engineering, University of California, Irvine, California, 92617, USA
- Pharmaceutical Sciences, University of California, Irvine, CA, 92617, USA
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Chemistry, Tufts University, Medford, MA, 02144, USA
| | - Brian Pulawski
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Jonah Z Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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4
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Procacci P. Dealing with Induced Fit, Conformational Selection, and Secondary Poses in Molecular Dynamics Simulations for Reliable Free Energy Predictions. J Chem Theory Comput 2023; 19:8942-8954. [PMID: 38037326 PMCID: PMC10720345 DOI: 10.1021/acs.jctc.3c00867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023]
Abstract
In this study, we have tested the performance of standard molecular dynamics (MD) simulations, replicates of shorter standard MD simulations, and Hamiltonian Replica Exchange (HREM) simulations for the sampling of two macrocyclic hosts for guest delivery, characterized by induced fit (phenyl-based host) and conformation selection (naphthyl-based host) and of the ODR-BRD4(I) drug-receptor system where the ligand can assume two main poses. For the optimization of the HREM simulation, we have proposed and tested an on-the-fly iterative scheme for equalizing the acceptance ratio along the replica progression at a constant replica number resulting in a moderate impact of the sampling efficiency. Concerning standard MD, we have found that, while splitting the total allocated simulation time in short MD replicates can reproduce the sampling efficiency of HREM in the phenyl-based host and in the ODR-BRD4(I) complex, in the naphthyl-based macrocycle, characterized by long-lived metastable states, enhanced sampling techniques are the only viable alternative for a reliable canonical sampling of the rugged conformational landscape.
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Affiliation(s)
- Piero Procacci
- Dipartimento di Chimica “Ugo
Schiff”, Università degli
Studi di Firenze, Via
della Lastruccia 3, 50019 Sesto Fiorentino, Italy
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5
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Khuttan S, Azimi S, Wu JZ, Dick S, Wu C, Xu H, Gallicchio E. Taming multiple binding poses in alchemical binding free energy prediction: the β-cyclodextrin host-guest SAMPL9 blinded challenge. Phys Chem Chem Phys 2023; 25:24364-24376. [PMID: 37676233 DOI: 10.1039/d3cp02125d] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
We apply the Alchemical Transfer Method (ATM) and a bespoke fixed partial charge force field to the SAMPL9 bCD host-guest binding free energy prediction challenge that comprises a combination of complexes formed between five phenothiazine guests and two cyclodextrin hosts. Multiple chemical forms, competing binding poses, and computational modeling challenges pose significant obstacles to obtaining reliable computational predictions for these systems. The phenothiazine guests exist in solution as racemic mixtures of enantiomers related by nitrogen inversions that bind the hosts in various binding poses, each requiring an individual free energy analysis. Due to the large size of the guests and the conformational reorganization of the hosts, which prevent a direct absolute binding free energy route, binding free energies are obtained by a series of absolute and relative binding alchemical steps for each chemical species in each binding pose. Metadynamics-accelerated conformational sampling was found to be necessary to address the poor convergence of some numerical estimates affected by conformational trapping. Despite these challenges, our blinded predictions quantitatively reproduced the experimental affinities for the β-cyclodextrin host and, to a lesser extent, those with a methylated derivative. The work illustrates the challenges of obtaining reliable free energy data in in silico drug design for even seemingly simple systems and introduces some of the technologies available to tackle them.
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Affiliation(s)
- Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York, New York, USA.
- PhD Program in Biochemistry, Graduate Center of the City University of New York, USA
| | - Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York, New York, USA.
- PhD Program in Biochemistry, Graduate Center of the City University of New York, USA
| | - Joe Z Wu
- Department of Chemistry, Brooklyn College of the City University of New York, New York, USA.
- PhD Program in Chemistry, Graduate Center of the City University of New York, USA
| | | | | | | | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, USA.
- PhD Program in Biochemistry, Graduate Center of the City University of New York, USA
- PhD Program in Chemistry, Graduate Center of the City University of New York, USA
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6
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Lockhart C, Luo X, Olson A, Delfing BM, Laracuente XE, Foreman KW, Paige M, Kehn-Hall K, Klimov DK. Can Free Energy Perturbation Simulations Coupled with Replica-Exchange Molecular Dynamics Study Ligands with Distributed Binding Sites? J Chem Inf Model 2023; 63:4791-4802. [PMID: 37531558 PMCID: PMC10947611 DOI: 10.1021/acs.jcim.3c00631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
Free energy perturbation coupled with replica exchange with solute tempering (FEP/REST) offers a rigorous approach to compute relative free energy changes for ligands. To determine the applicability of FEP/REST for the ligands with distributed binding poses, we considered two alchemical transformations involving three putative inhibitors I0, I1, and I2 of the Venezuelan equine encephalitis virus nuclear localization signal sequence binding to the importin-α (impα) transporter protein. I0 → I1 and I0 → I2 transformations, respectively, increase or decrease the polarity of the parent molecule. Our objective was three-fold─(i) to verify FEP/REST technical performance and convergence, (ii) to estimate changes in binding free energy ΔΔG, and (iii) to determine the utility of FEP/REST simulations for conformational binding analysis. Our results are as follows. First, our FEP/REST implementation properly follows FEP/REST formalism and produces converged ΔΔG estimates. Due to ligand inherent unbinding, the better FEP/REST strategy lies in performing multiple independent trajectories rather than extending their length. Second, I0 → I1 and I0 → I2 transformations result in overall minor changes in inhibitor binding free energy, slightly strengthening the affinity of I1 and weakening that of I2. Electrostatic interactions dominate binding interactions, determining the enthalpic changes. The two transformations cause opposite entropic changes, which ultimately govern binding affinities. Importantly, we confirm the validity of FEP/REST free energy estimates by comparing them with our previous REST simulations, directly probing binding of three ligands to impα. Third, we established that FEP/REST simulations can sample binding ensembles of ligands. Thus, FEP/REST can be applied (i) to study the energetics of the ligand binding without defined poses and showing minor differences in affinities |ΔΔG| ≲ 0.5 kcal/mol and (ii) to collect ligand binding conformational ensembles.
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Affiliation(s)
| | - Xingyu Luo
- School of Systems Biology, George Mason University, Manassas, VA 20110
| | - Audrey Olson
- School of Systems Biology, George Mason University, Manassas, VA 20110
| | - Bryan M. Delfing
- School of Systems Biology, George Mason University, Manassas, VA 20110
| | | | - Kenneth W. Foreman
- Department of Chemistry and Biochemistry, George Mason University, Fairfax, VA 22030
| | - Mikell Paige
- Department of Chemistry and Biochemistry, George Mason University, Fairfax, VA 22030
| | - Kylene Kehn-Hall
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
| | - Dmitri K. Klimov
- School of Systems Biology, George Mason University, Manassas, VA 20110
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7
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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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8
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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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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9
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Rizzuti B. Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2022; 1870:140757. [PMID: 35051666 DOI: 10.1016/j.bbapap.2022.140757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 12/22/2022]
Abstract
Molecular dynamics simulation is the most popular computational technique for investigating the structural and dynamical behaviour of proteins, in search of the molecular basis of their function. Far from being a completely settled field of research, simulations are still evolving to best capture the essential features of the atomic interactions that govern a protein's inner motions. Modern force fields are becoming increasingly accurate in providing a physical description adequate to this purpose, and allow us to model complex biological systems under fairly realistic conditions. Furthermore, the use of accelerated sampling techniques is improving our access to the observation of progressively larger molecular structures, longer time scales, and more hidden functional events. In this review, the basic principles of molecular dynamics simulations and a number of key applications in the area of protein science are summarized, and some of the most important results are discussed. Examples include the study of the structure, dynamics and binding properties of 'difficult' targets, such as intrinsically disordered proteins and membrane receptors, and the investigation of challenging phenomena like hydration-driven processes and protein aggregation. The findings described provide an overall picture of the current state of this research field, and indicate new perspectives on the road ahead to the upcoming future of molecular simulations.
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Affiliation(s)
- Bruno Rizzuti
- CNR-NANOTEC, SS Rende (CS), Department of Physics, University of Calabria, 87036 Rende, Italy; Institute for Biocomputation and Physics of Complex Systems (BIFI), Joint Unit GBsC-CSIC-BIFI, University of Zaragoza, 50018 Zaragoza, Spain.
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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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Neira JL, Rizzuti B, Ortega-Alarcón D, Giudici AM, Abián O, Fárez-Vidal ME, Velázquez-Campoy A. The armadillo-repeat domain of plakophilin 1 binds the C-terminal sterile alpha motif (SAM) of p73. Biochim Biophys Acta Gen Subj 2021; 1865:129914. [PMID: 33872756 DOI: 10.1016/j.bbagen.2021.129914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/13/2021] [Accepted: 04/13/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND Plakophilin 1 (PKP1) is a component of desmosomes, which are key structural components for cell-cell adhesion, and can also be found in other cell locations. The p53, p63 and p73 proteins belong to the p53 family of transcription factors, playing crucial roles in tumour suppression. The α-splice variant of p73 (p73α) has at its C terminus a sterile alpha motif (SAM); such domain, SAMp73, is involved in the interaction with other macromolecules. METHODS We studied the binding of SAMp73 with the armadillo domain of PKP1 (ARM-PKP1) in the absence and the presence of 100 mM NaCl, by using several biophysical techniques, namely fluorescence, far-ultraviolet circular dichroism (CD), nuclear magnetic resonance (NMR), isothermal titration calorimetry (ITC), and molecular docking and simulations. RESULTS Association was observed between the two proteins, with a dissociation constant of ~5 μM measured by ITC and fluorescence in the absence of NaCl. The binding region of SAMp73 involved residues of the so-called "middle-loop-end-helix" binding region (i.e., comprising the third helix, together with the C terminus of the second one, and the N-cap of the fourth), as shown by 15N, 1H- HSQC-NMR spectra. Molecular modelling provided additional information on the possible structure of the binding complex. CONCLUSIONS This newly-observed interaction could have potential therapeutic relevance in the tumour pathways where PKP1 is involved, and under conditions when there is a possible inactivation of p53. GENERAL SIGNIFICANCE The discovery of the binding between SAMp73 and ARM-PKP1 suggests a functional role for their interaction, including the possibility that SAMp73 could assist PKP1 in signalling pathways.
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Affiliation(s)
- José L Neira
- IDIBE, Universidad Miguel Hernández, 03202 Elche, Alicante, Spain; Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Joint Units IQFR-CSIC-BIFI, GBsC-CSIC-BIFI, Universidad de Zaragoza, 50009 Zaragoza, Spain.
| | - Bruno Rizzuti
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Joint Units IQFR-CSIC-BIFI, GBsC-CSIC-BIFI, Universidad de Zaragoza, 50009 Zaragoza, Spain; CNR-NANOTEC, Licryl-UOS Cosenza and CEMIF.Cal, Department of Physics, University of Calabria, 87036 Rende, Italy.
| | - David Ortega-Alarcón
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Joint Units IQFR-CSIC-BIFI, GBsC-CSIC-BIFI, Universidad de Zaragoza, 50009 Zaragoza, Spain
| | | | - Olga Abián
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Joint Units IQFR-CSIC-BIFI, GBsC-CSIC-BIFI, Universidad de Zaragoza, 50009 Zaragoza, Spain; Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain; Instituto Aragonés de Ciencias de la Salud (IACS), 50009 Zaragoza, Spain; Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
| | - María Esther Fárez-Vidal
- Departamento de Bioquímica y Biología Molecular III e Inmunología, Facultad de Medicina, Universidad de Granada, 18016 Granada, Spain; Instituto de Investigación Biomédica IBS, Complejo Hospitalario Universitario de Granada, Universidad de Granada, 18071 Granada, Spain
| | - Adrián Velázquez-Campoy
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Joint Units IQFR-CSIC-BIFI, GBsC-CSIC-BIFI, Universidad de Zaragoza, 50009 Zaragoza, Spain; Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain; Fundacion ARAID, Government of Aragon, 50009 Zaragoza, Spain; Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain; Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
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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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Rizzi V, Bonati L, Ansari N, Parrinello M. The role of water in host-guest interaction. Nat Commun 2021; 12:93. [PMID: 33397926 PMCID: PMC7782548 DOI: 10.1038/s41467-020-20310-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.
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Affiliation(s)
- Valerio Rizzi
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092, Zurich, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland
| | - Luigi Bonati
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland
- Department of Physics, ETH Zurich, 8092, Zurich, Switzerland
| | - Narjes Ansari
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092, Zurich, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092, Zurich, Switzerland.
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland.
- Italian Institute of Technology, Via Morego 30, 16163, Genova, Italy.
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14
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Procacci P. Methodological uncertainties in drug-receptor binding free energy predictions based on classical molecular dynamics. Curr Opin Struct Biol 2020; 67:127-134. [PMID: 33220532 DOI: 10.1016/j.sbi.2020.08.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 08/02/2020] [Accepted: 08/02/2020] [Indexed: 12/13/2022]
Abstract
Computational approaches are becoming an essential tool in modern drug design and discovery, with fast compound triaging using a combination of machine learning and docking techniques followed by molecular dynamics binding free energies assessment using alchemical techniques. The traditional MD-based alchemical free energy perturbation (FEP) method faces severe sampling issues that may limits its reliability in automated workflows. Here we review the major sources of uncertainty in FEP protocols for drug discovery, showing how the sampling problem can be effectively tackled by switching to nonequilibrium alchemical techniques.
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Affiliation(s)
- Piero Procacci
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, dVia della Lastruccia 3, 50019 Sesto Fiorentino, Italy.
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15
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Kumar S, Seth D, Deshpande PA. Molecular dynamics simulations identify the regions of compromised thermostability in SazCA. Proteins 2020; 89:375-388. [PMID: 33146427 DOI: 10.1002/prot.26022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 09/14/2020] [Accepted: 10/16/2020] [Indexed: 11/09/2022]
Abstract
The present study examined the structure and dynamics of the most active and thermostable carbonic anhydrase, SazCA, probed using molecular dynamics simulations. The molecular system was described by widely used biological force-fields (AMBER, CHARMM22, CHARMM36, and OPLS-AA) in conjunction with TIP3P water model. The comparison of molecular dynamics simulation results suggested AMBER to be a suitable choice to describe the structure and dynamics of SazCA. In addition to this, we also addressed the effect of temperature on the stability of SazCA. We performed molecular dynamics simulations at 313, 333, 353, 373, and 393 K to study the relationship between thermostability and flexibility in SazCA. The amino acid residues VAL98, ASN99, GLY100, LYS101, GLU145, and HIS207 were identified as the most flexible residues from root-mean-square fluctuations. The salt bridge analysis showed that ion-pairs ASP113-LYS81, ASP115-LYS81, ASP115-LYS114, GLU144-LYS143, and GLU144-LYS206, were responsible for the compromised thermal stability of SazCA.
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Affiliation(s)
- Shashi Kumar
- Quantum and Molecular Engineering Laboratory, Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Deepak Seth
- Quantum and Molecular Engineering Laboratory, Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Parag Arvind Deshpande
- Quantum and Molecular Engineering Laboratory, Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
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16
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Sora V, Kumar M, Maiani E, Lambrughi M, Tiberti M, Papaleo E. Structure and Dynamics in the ATG8 Family From Experimental to Computational Techniques. Front Cell Dev Biol 2020; 8:420. [PMID: 32587856 PMCID: PMC7297954 DOI: 10.3389/fcell.2020.00420] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 05/06/2020] [Indexed: 12/31/2022] Open
Abstract
Autophagy is a conserved and essential intracellular mechanism for the removal of damaged components. Since autophagy deregulation is linked to different kinds of pathologies, it is fundamental to gain knowledge on the fine molecular and structural details related to the core proteins of the autophagy machinery. Among these, the family of human ATG8 proteins plays a central role in recruiting other proteins to the different membrane structures involved in the autophagic pathway. Several experimental structures are available for the members of the ATG8 family alone or in complex with their different biological partners, including disordered regions of proteins containing a short linear motif called LC3 interacting motif. Recently, the first structural details of the interaction of ATG8 proteins with biological membranes came into light. The availability of structural data for human ATG8 proteins has been paving the way for studies on their structure-function-dynamic relationship using biomolecular simulations. Experimental and computational structural biology can help to address several outstanding questions on the mechanism of human ATG8 proteins, including their specificity toward different interactors, their association with membranes, the heterogeneity of their conformational ensemble, and their regulation by post-translational modifications. We here summarize the main results collected so far and discuss the future perspectives within the field and the knowledge gaps. Our review can serve as a roadmap for future structural and dynamics studies of the ATG8 family members in health and disease.
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Affiliation(s)
- Valentina Sora
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Mukesh Kumar
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Emiliano Maiani
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Lambrughi
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Tiberti
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
- Translational Disease System Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
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17
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Krämer A, Hudson PS, Jones MR, Brooks BR. Multi-phase Boltzmann weighting: accounting for local inhomogeneity in molecular simulations of water-octanol partition coefficients in the SAMPL6 challenge. J Comput Aided Mol Des 2020; 34:471-483. [PMID: 32060677 PMCID: PMC8750956 DOI: 10.1007/s10822-020-00285-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 01/08/2020] [Indexed: 02/08/2023]
Abstract
Accurately computing partition coefficients is a pivotal part of drug discovery. Specifically, octanol-water partition coefficients can provide information into hydrophobicity of drug-like molecules, as well as a de facto representation of membrane permeability. However, one challenge facing the computation of partition coefficients is the need to encapsulate various microscopic environments. These include areas of largely bulk solvent (i.e., either water or octanol) or regions where octanol is saturated with water or areas of higher salt concentration. Also, tautomeric effects require consideration. Thus, we present a Boltzmann weighting approach that incorporates transfer free energies across varying microscopic media, as well as varying tautomeric state, to compute partition coefficients in the SAMPL6 challenge.
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Affiliation(s)
- Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Phillip S Hudson
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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18
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Karlov D, Sosnin S, Fedorov MV, Popov P. graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein-Ligand Complexes. ACS OMEGA 2020; 5:5150-5159. [PMID: 32201802 PMCID: PMC7081425 DOI: 10.1021/acsomega.9b04162] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 02/21/2020] [Indexed: 06/04/2023]
Abstract
In this work, we present graph-convolutional neural networks for the prediction of binding constants of protein-ligand complexes. We derived the model using multi task learning, where the target variables are the dissociation constant (K d), inhibition constant (K i), and half maximal inhibitory concentration (IC50). Being rigorously trained on the PDBbind dataset, the model achieves the Pearson correlation coefficient of 0.87 and the RMSE value of 1.05 in pK units, outperforming recently developed 3D convolutional neural network model K deep.
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Affiliation(s)
- Dmitry
S. Karlov
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
| | - Sergey Sosnin
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
- Skolkovo
Innovation Center,Syntelly LLC, 42 Bolshoy Boulevard, Moscow 143026, Russia
| | - Maxim V. Fedorov
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
- Skolkovo
Innovation Center,Syntelly LLC, 42 Bolshoy Boulevard, Moscow 143026, Russia
- University
of Strathclyde, Physics
John Anderson Building, 107 Rottenrow East, Glasgow UK G4 0NG, U.K.
| | - Petr Popov
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
- Moscow
Institute of Physics and Technology, Dolgoprudny 141701, Russia
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19
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Minh DDL. Alchemical Grid Dock (AlGDock): Binding Free Energy Calculations between Flexible Ligands and Rigid Receptors. J Comput Chem 2020; 41:715-730. [PMID: 31397498 PMCID: PMC7263302 DOI: 10.1002/jcc.26036] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/28/2019] [Accepted: 07/08/2019] [Indexed: 12/14/2022]
Abstract
Alchemical Grid Dock (AlGDock) is open-source software designed to compute the binding potential of mean force-the binding free energy between a flexible ligand and a rigid receptor-for a small organic ligand and a biological macromolecule. Multiple BPMFs can be used to rigorously compute binding affinities between flexible partners. AlGDock uses replica exchange between thermodynamic states at different temperatures and receptor-ligand interaction strengths. Receptor-ligand interaction energies are represented by interpolating precomputed grids. Thermodynamic states are adaptively initialized and adjusted on-the-fly to maintain adequate replica exchange rates. In demonstrative calculations, when the bound ligand is treated as fully solvated, AlGDock estimates BPMFs with a precision within 4 kT in 65% and within 8 kT for 91% of systems. It correctly identifies the native binding pose in 83% of simulations. Performance is sometimes limited by subtle differences in the important configuration space of sampled and targeted thermodynamic states. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
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20
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Menzer WM, Xie B, Minh DDL. On Restraints in End-Point Protein-Ligand Binding Free Energy Calculations. J Comput Chem 2020; 41:573-586. [PMID: 31821590 PMCID: PMC7311925 DOI: 10.1002/jcc.26119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 10/26/2019] [Accepted: 11/08/2019] [Indexed: 12/14/2022]
Abstract
The impact of harmonic restraints on protein heavy atoms and ligand atoms on end-point free energy calculations is systematically characterized for 54 protein-ligand complexes. We observe that stronger restraints reduce the equilibration time and statistical inefficiency, suppress conformational sampling, influence correlation with experiment, and monotonically decrease the estimated loss of entropy upon binding, leading to stronger estimated binding free energies in most systems. A statistical estimator that reweights for the biasing potential and includes data prior to the estimated equilibration time has the highest correlation with experiment. A spring constant of 20 cal mol-1 Å-2 maintains a near-native energy landscape and suppresses artifactual energy minima while minimally limiting thermal fluctuations about the crystal structure. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- William M Menzer
- Department of Biology, Illinois Institute of Technology, Chicago, Illinois, 60616
| | - Bing Xie
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
| | - David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
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21
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Rogers TR, Wang F. Comparing Alchemical Free Energy Estimates to Experimental Values Based on the Ben-Naim Formula: How Much Agreement Can We Expect? J Phys Chem B 2020; 124:840-847. [PMID: 31922746 DOI: 10.1021/acs.jpcb.9b08965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The solvation free energy (SFE) plays a key role in thermodynamics. One well-established method for computing the SFE is through an alchemical transformation. However, experimental SFEs are generally determined according to the Ben-Naim equations relying on vapor pressure or density ratios. It is important to establish whether, or to what extent, typical alchemical-based free energy computations provide results comparable to experimental SFEs. In this work, we mimic experimental measurements by simulating the liquid-vapor coexistence of water without alchemical operations. The SFEs measured through vapor pressure and density ratios are used to validate the SFEs obtained through alchemical transformations. It is shown that proper consideration of the nonideal behavior of the vapor is important to ensure that the alchemical SFEs are consistent with the Ben-Naim SFEs. Alchemical transformations in the vapor phase should be performed in addition to solution phase transformations for strongly interacting solutes, such as those with low boiling temperatures and large second virial coefficients. A formula based on the virial expansion of pressure is proposed to provide a better estimate of the true SFE from the simulated vapor pressures. The proposed formula is also applicable to experimental determinations of SFE when the pressure-based route is used.
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Affiliation(s)
- T Ryan Rogers
- Department of Chemistry and Biochemistry , University of Arkansas , Fayetteville , Arkansas 72703 , United States
| | - Feng Wang
- Department of Chemistry and Biochemistry , University of Arkansas , Fayetteville , Arkansas 72703 , United States
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22
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Procacci P. Accuracy, precision, and efficiency of nonequilibrium alchemical methods for computing free energies of solvation. I. Bidirectional approaches. J Chem Phys 2019; 151:144113. [DOI: 10.1063/1.5120615] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Piero Procacci
- Department of Chemistry, University of Florence, Florence, Italy
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23
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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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24
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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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25
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Procacci P. Solvation free energies via alchemical simulations: let's get honest about sampling, once more. Phys Chem Chem Phys 2019; 21:13826-13834. [PMID: 31211310 DOI: 10.1039/c9cp02808k] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Free energy perturbation (FEP) approaches with stratification have seen widespread and increasing use in computational studies of biologically relevant molecules. However, when the molecular systems are characterized by a complex conformational free energy landscape, the assessment of convergence remains a concern for many practitioners. The sampling problem in FEP has been authoritatively addressed in a recent perspective paper [D. Mobley, J. Comput.-Aided Mol. Des., 2012, 26, 93], incisively entitled "Let's get honest about sampling". Here, I return to the issue of sampling in the determination of the octanol-water partition coefficient for a synthetic precursor of kinase inhibitors that has been included in the recent extension of the SAMPL6 blind challenge of log P coefficients. I will show that even for this simple compound, whose conformational space is essentially dictated by two sp3 rotable bonds connecting rigid planar units, canonical sampling using standard techniques can be surprisingly hard to achieve. I will also show how the conformational sampling problem can be effectively bypassed using unidirectional and bidirectional nonequilibrium work methods, reliably recovering the solvation energy with minimal methodological uncertainty.
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26
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Spiriti J, Subramanian SR, Palli R, Wu M, Zuckerman DM. Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α. PLoS One 2019; 14:e0215694. [PMID: 31013302 PMCID: PMC6478315 DOI: 10.1371/journal.pone.0215694] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/06/2019] [Indexed: 12/17/2022] Open
Abstract
There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves.
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Affiliation(s)
- Justin Spiriti
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, United States of America
| | - Sundar Raman Subramanian
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
| | - Rohith Palli
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
| | - Maria Wu
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
| | - Daniel M. Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, United States of America
- * E-mail:
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27
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Procacci P. Comment on “Statistical efficiency of methods for computing free energy of hydration” [J. Chem. Phys. 149, 144111 (2018)]. J Chem Phys 2019; 150:127101. [DOI: 10.1063/1.5086743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Piero Procacci
- Department of Chemistry, University of Florence, Florence, Italy
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28
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Burley KH, Gill SC, Lim NM, Mobley DL. Enhancing Side Chain Rotamer Sampling Using Nonequilibrium Candidate Monte Carlo. J Chem Theory Comput 2019; 15:1848-1862. [PMID: 30677291 DOI: 10.1021/acs.jctc.8b01018] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Molecular simulations are a valuable tool for studying biomolecular motions and thermodynamics. However, such motions can be slow compared to simulation time scales, yet critical. Specifically, adequate sampling of side chain motions in protein binding pockets is crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations. The time scale of side chain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed nonequilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of side chain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target side chain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine side chain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances side chain sampling, especially in systems where the torsional barrier to rotation is high (≥10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment. Overall, this may provide a promising strategy to selectively improve side chain sampling in molecular simulations.
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Affiliation(s)
- Kalistyn H Burley
- Department of Pharmaceutical Sciences , University of California, Irvine , Irvine , California 92697 , United States
| | - Samuel C Gill
- Department of Chemistry , University of California, Irvine , Irvine , California 92617 , United States
| | - Nathan M Lim
- Department of Pharmaceutical Sciences , University of California, Irvine , Irvine , California 92697 , 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 92617 , United States
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29
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Hudson PS, Boresch S, Rogers DM, Woodcock HL. Accelerating QM/MM Free Energy Computations via Intramolecular Force Matching. J Chem Theory Comput 2018; 14:6327-6335. [PMID: 30300543 PMCID: PMC6314469 DOI: 10.1021/acs.jctc.8b00517] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The calculation of free energy differences between levels of theory has numerous potential pitfalls. Chief among them is the lack of overlap, i.e., ensembles generated at one level of theory (e.g., "low") not being good approximations of ensembles at the other (e.g., "high"). Numerous strategies have been devised to mitigate this issue. However, the most straightforward approach is to ensure that the "low" level ensemble more closely resembles that of the "high". Ideally, this is done without increasing computational cost. Herein, we demonstrate that by reparametrizing classical intramolecular potentials to reproduce high level forces (i.e., force matching) configurational overlap between a "low" (i.e., classical) and "high" (i.e., quantum) level can be significantly improved. This procedure is validated on two test cases and results in vastly improved convergence of free energy simulations.
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Affiliation(s)
- Phillip S Hudson
- Department of Chemistry , University of South Florida , 4202 East Fowler Avenue, CHE205 , Tampa , Florida 33620-5250 , United States
- Laboratory of Computational Biology , National Institutes of Health, National Heart, Lung and Blood Institute , 12 South Drive Rm 3053 , Bethesda , Maryland 20892-5690 , United States
| | - Stefan Boresch
- Faculty of Chemistry, Department of Computational Biological Chemistry , University of Vienna , Währingerstraße 17 , A-1090 Vienna , Austria
| | - David M Rogers
- Department of Chemistry , University of South Florida , 4202 East Fowler Avenue, CHE205 , Tampa , Florida 33620-5250 , United States
| | - H Lee Woodcock
- Department of Chemistry , University of South Florida , 4202 East Fowler Avenue, CHE205 , Tampa , Florida 33620-5250 , United States
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30
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Braun E, Gilmer J, Mayes HB, Mobley DL, Monroe JI, Prasad S, Zuckerman DM. Best Practices for Foundations in Molecular Simulations [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2018; 1:5957. [PMID: 31788666 PMCID: PMC6884151 DOI: 10.33011/livecoms.1.1.5957] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This document provides a starting point for approaching molecular simulations, guiding beginning practitioners to what issues they need to know about before and while starting their first simulations, and why those issues are so critical. This document makes no claims to provide an adequate introduction to the subject on its own. Instead, our goal is to help people know what issues are critical before beginning, and to provide references to good resources on those topics. We also provide a checklist of key issues to consider before and while setting up molecular simulations which may serve as a foundation for other best practices documents.
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Affiliation(s)
| | | | | | | | | | - Samarjeet Prasad
- National Institutes of Health and Johns Hopkins University, Baltimore
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31
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Papaleo E, Camilloni C, Teilum K, Vendruscolo M, Lindorff-Larsen K. Molecular dynamics ensemble refinement of the heterogeneous native state of NCBD using chemical shifts and NOEs. PeerJ 2018; 6:e5125. [PMID: 30013831 PMCID: PMC6035720 DOI: 10.7717/peerj.5125] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 06/08/2018] [Indexed: 01/24/2023] Open
Abstract
Many proteins display complex dynamical properties that are often intimately linked to their biological functions. As the native state of a protein is best described as an ensemble of conformations, it is important to be able to generate models of native state ensembles with high accuracy. Due to limitations in sampling efficiency and force field accuracy it is, however, challenging to obtain accurate ensembles of protein conformations by the use of molecular simulations alone. Here we show that dynamic ensemble refinement, which combines an accurate atomistic force field with commonly available nuclear magnetic resonance (NMR) chemical shifts and NOEs, can provide a detailed and accurate description of the conformational ensemble of the native state of a highly dynamic protein. As both NOEs and chemical shifts are averaged on timescales up to milliseconds, the resulting ensembles reflect the structural heterogeneity that goes beyond that probed, e.g., by NMR relaxation order parameters. We selected the small protein domain NCBD as object of our study since this protein, which has been characterized experimentally in substantial detail, displays a rich and complex dynamical behaviour. In particular, the protein has been described as having a molten-globule like structure, but with a relatively rigid core. Our approach allowed us to describe the conformational dynamics of NCBD in solution, and to probe the structural heterogeneity resulting from both short- and long-timescale dynamics by the calculation of order parameters on different time scales. These results illustrate the usefulness of our approach since they show that NCBD is rather rigid on the nanosecond timescale, but interconverts within a broader ensemble on longer timescales, thus enabling the derivation of a coherent set of conclusions from various NMR experiments on this protein, which could otherwise appear in contradiction with each other.
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Affiliation(s)
- Elena Papaleo
- Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.,Current affiliation: Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Carlo Camilloni
- Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.,Current affiliation: Department of Biosciences, University of Milano, Milano, Italy
| | - Kaare Teilum
- Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | | | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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32
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Aldeghi M, Bluck JP, Biggin PC. Absolute Alchemical Free Energy Calculations for Ligand Binding: A Beginner's Guide. Methods Mol Biol 2018; 1762:199-232. [PMID: 29594774 DOI: 10.1007/978-1-4939-7756-7_11] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Many thermodynamic quantities can be extracted from computer simulations that generate an ensemble of microstates according to the principles of statistical mechanics. Among these quantities is the free energy of binding of a small molecule to a macromolecule, such as a protein. Here, we present an introductory overview of a protocol that allows for the estimation of ligand binding free energies via molecular dynamics simulations. While we focus on the binding of organic molecules to proteins, the approach is in principle transferable to any pair of molecules.
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Affiliation(s)
- Matteo Aldeghi
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, Oxford, UK
- Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Joseph P Bluck
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, Oxford, UK
| | - Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, Oxford, UK.
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33
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Adaptive simulations, towards interactive protein-ligand modeling. Sci Rep 2017; 7:8466. [PMID: 28814780 PMCID: PMC5559483 DOI: 10.1038/s41598-017-08445-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/12/2017] [Indexed: 11/09/2022] Open
Abstract
Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.
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34
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Williams-Noonan BJ, Yuriev E, Chalmers DK. Free Energy Methods in Drug Design: Prospects of “Alchemical Perturbation” in Medicinal Chemistry. J Med Chem 2017; 61:638-649. [DOI: 10.1021/acs.jmedchem.7b00681] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Billy J. Williams-Noonan
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
| | - Elizabeth Yuriev
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
| | - David K. Chalmers
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
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35
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Xie B, Nguyen TH, Minh DDL. Absolute Binding Free Energies between T4 Lysozyme and 141 Small Molecules: Calculations Based on Multiple Rigid Receptor Configurations. J Chem Theory Comput 2017; 13:2930-2944. [PMID: 28430432 PMCID: PMC5612505 DOI: 10.1021/acs.jctc.6b01183] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We demonstrate the feasibility of estimating protein-ligand binding free energies using multiple rigid receptor configurations. On the basis of T4 lysozyme snapshots extracted from six alchemical binding free energy calculations with a flexible receptor, binding free energies were estimated for a total of 141 ligands. For 24 ligands, the calculations reproduced flexible-receptor estimates with a correlation coefficient of 0.90 and a root-mean-square error of 1.59 kcal/mol. The accuracy of calculations based on Poisson-Boltzmann/surface area implicit solvent was comparable to that of previously reported free energy calculations.
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Affiliation(s)
- Bing Xie
- Department of Chemistry, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Trung Hai Nguyen
- Department of Chemistry, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - David D. L. Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, IL 60616, USA
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36
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Matos GDR, Kyu DY, Loeffler HH, Chodera JD, Shirts MR, Mobley DL. Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the FreeSolv database. JOURNAL OF CHEMICAL AND ENGINEERING DATA 2017; 62:1559-1569. [PMID: 29056756 PMCID: PMC5648357 DOI: 10.1021/acs.jced.7b00104] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Solvation free energies can now be calculated precisely from molecular simulations, providing a valuable test of the energy functions underlying these simulations. Here, we briefly review "alchemical" approaches for calculating the solvation free energies of small, neutral organic molecules from molecular simulations, and illustrate by applying them to calculate aqueous solvation free energies (hydration free energies). These approaches use a non-physical pathway to compute free energy differences from a simulation or set of simulations and appear to be a particularly robust and general-purpose approach for this task. We also present an update (version 0.5) to our FreeSolv database of experimental and calculated hydration free energies of neutral compounds and provide input files in formats for several simulation packages. This revision to FreeSolv provides calculated values generated with a single protocol and software version, rather than the heterogeneous protocols used in the prior version of the database. We also further update the database to provide calculated enthalpies and entropies of hydration and some experimental enthalpies and entropies, as well as electrostatic and nonpolar components of solvation free energies.
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Affiliation(s)
- Guilherme Duarte Ramos Matos
- Department of Chemistry, University of California, Irvine, Department of Pharmaceutical Sciences, University of California, Irvine, Scientific Computing Department, STFC, UK, Computational and Systems Biology Program, Sloan Kettering Institute, Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, and Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine
| | - Daisy Y Kyu
- Department of Chemistry, University of California, Irvine, Department of Pharmaceutical Sciences, University of California, Irvine, Scientific Computing Department, STFC, UK, Computational and Systems Biology Program, Sloan Kettering Institute, Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, and Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine
| | - Hannes H Loeffler
- Department of Chemistry, University of California, Irvine, Department of Pharmaceutical Sciences, University of California, Irvine, Scientific Computing Department, STFC, UK, Computational and Systems Biology Program, Sloan Kettering Institute, Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, and Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine
| | - John D Chodera
- Department of Chemistry, University of California, Irvine, Department of Pharmaceutical Sciences, University of California, Irvine, Scientific Computing Department, STFC, UK, Computational and Systems Biology Program, Sloan Kettering Institute, Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, and Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine
| | - Michael R Shirts
- Department of Chemistry, University of California, Irvine, Department of Pharmaceutical Sciences, University of California, Irvine, Scientific Computing Department, STFC, UK, Computational and Systems Biology Program, Sloan Kettering Institute, Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, and Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine
| | - David L Mobley
- Department of Chemistry, University of California, Irvine, Department of Pharmaceutical Sciences, University of California, Irvine, Scientific Computing Department, STFC, UK, Computational and Systems Biology Program, Sloan Kettering Institute, Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, and Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine
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37
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Pal RK, Haider K, Kaur D, Flynn W, Xia J, Levy RM, Taran T, Wickstrom L, Kurtzman T, Gallicchio E. A combined treatment of hydration and dynamical effects for the modeling of host-guest binding thermodynamics: the SAMPL5 blinded challenge. J Comput Aided Mol Des 2017; 31:29-44. [PMID: 27696239 PMCID: PMC5477994 DOI: 10.1007/s10822-016-9956-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 08/25/2016] [Indexed: 01/02/2023]
Abstract
As part of the SAMPL5 blinded experiment, we computed the absolute binding free energies of 22 host-guest complexes employing a novel approach based on the BEDAM single-decoupling alchemical free energy protocol with parallel replica exchange conformational sampling and the AGBNP2 implicit solvation model specifically customized to treat the effect of water displacement as modeled by the Hydration Site Analysis method with explicit solvation. Initial predictions were affected by the lack of treatment of ionic charge screening, which is very significant for these highly charged hosts, and resulted in poor relative ranking of negatively versus positively charged guests. Binding free energies obtained with Debye-Hückel treatment of salt effects were in good agreement with experimental measurements. Water displacement effects contributed favorably and very significantly to the observed binding affinities; without it, the modeling predictions would have grossly underestimated binding. The work validates the implicit/explicit solvation approach employed here and it shows that comprehensive physical models can be effective at predicting binding affinities of molecular complexes requiring accurate treatment of conformational dynamics and hydration.
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Affiliation(s)
- Rajat Kumar Pal
- Department of Chemistry, Brooklyn College, 2900 Bedford Avenue, Brooklyn, New York, 11210, USA
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY, 10016, USA
| | - Kamran Haider
- Department of Chemistry, Lehman College, The City University of New York, 250 Bedford Park Blvd. West, Bronx, New York, NY, 10468, USA
| | - Divya Kaur
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, NY, 10016, USA
| | - William Flynn
- Center for Biophysics and Computational Biology, Institute of Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia, PA, USA
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, 08854, USA
| | - Junchao Xia
- Center for Biophysics and Computational Biology, Institute of Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia, PA, USA
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Institute of Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia, PA, USA
| | - Tetiana Taran
- Borough of Manhattan Community College, Department of Science, The City University of New York, 199 Chambers Street, New York, NY, 10007, USA
| | - Lauren Wickstrom
- Borough of Manhattan Community College, Department of Science, The City University of New York, 199 Chambers Street, New York, NY, 10007, USA
| | - Tom Kurtzman
- Department of Chemistry, Lehman College, The City University of New York, 250 Bedford Park Blvd. West, Bronx, New York, NY, 10468, USA
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY, 10016, USA
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, NY, 10016, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, 2900 Bedford Avenue, Brooklyn, New York, 11210, USA.
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY, 10016, USA.
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, NY, 10016, USA.
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38
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Adelman JL, Grabe M. Simulating Current-Voltage Relationships for a Narrow Ion Channel Using the Weighted Ensemble Method. J Chem Theory Comput 2016; 11:1907-18. [PMID: 26392816 DOI: 10.1021/ct501134s] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Ion channels are responsible for a myriad of fundamental biological processes via their role in controlling the flow of ions through water-filled membrane-spanning pores in response to environmental cues. Molecular simulation has played an important role in elucidating the mechanism of ion conduction, but connecting atomistically detailed structural models of the protein to electrophysiological measurements remains a broad challenge due to the computational cost of reaching the necessary time scales. Here, we introduce an enhanced sampling method for simulating the conduction properties of narrow ion channels using the Weighted ensemble (WE) sampling approach. We demonstrate the application of this method to calculate the current–voltage relationship as well as the nonequilibrium ion distribution at steady-state of a simple model ion channel. By direct comparisons with long brute force simulations, we show that the WE simulations rigorously reproduce the correct long-time scale kinetics of the system and are capable of determining these quantities using significantly less aggregate simulation time under conditions where permeation events are rare.
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39
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All-atom/coarse-grained hybrid predictions of distribution coefficients in SAMPL5. J Comput Aided Mol Des 2016; 30:969-976. [PMID: 27460060 PMCID: PMC5206257 DOI: 10.1007/s10822-016-9926-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 07/16/2016] [Indexed: 12/14/2022]
Abstract
We present blind predictions submitted to the SAMPL5 challenge on calculating distribution coefficients. The predictions were based on estimating the solvation free energies in water and cyclohexane of the 53 compounds in the challenge. These free energies were computed using alchemical free energy simulations based on a hybrid all-atom/coarse-grained model. The compounds were treated with the general Amber force field, whereas the solvent molecules were treated with the Elba coarse-grained model. Considering the simplicity of the solvent model and that we approximate the distribution coefficient with the partition coefficient of the neutral species, the predictions are of good accuracy. The correlation coefficient, R is 0.64, 82 % of the predictions have the correct sign and the mean absolute deviation is 1.8 log units. This is on a par with or better than the other simulation-based predictions in the challenge. We present an analysis of the deviations to experiments and compare the predictions to another submission that used all-atom solvent.
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40
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Yilmazer ND, Korth M. Recent Progress in Treating Protein-Ligand Interactions with Quantum-Mechanical Methods. Int J Mol Sci 2016; 17:ijms17050742. [PMID: 27196893 PMCID: PMC4881564 DOI: 10.3390/ijms17050742] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 04/18/2016] [Accepted: 05/03/2016] [Indexed: 11/16/2022] Open
Abstract
We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.
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Affiliation(s)
- Nusret Duygu Yilmazer
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
| | - Martin Korth
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
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41
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Su PC, Johnson ME. Evaluating thermodynamic integration performance of the new amber molecular dynamics package and assess potential halogen bonds of enoyl-ACP reductase (FabI) benzimidazole inhibitors. J Comput Chem 2016; 37:836-47. [PMID: 26666582 PMCID: PMC4769659 DOI: 10.1002/jcc.24274] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Revised: 10/23/2015] [Accepted: 11/16/2015] [Indexed: 12/17/2022]
Abstract
Thermodynamic integration (TI) can provide accurate binding free energy insights in a lead optimization program, but its high computational expense has limited its usage. In the effort of developing an efficient and accurate TI protocol for FabI inhibitors lead optimization program, we carefully compared TI with different Amber molecular dynamics (MD) engines (sander and pmemd), MD simulation lengths, the number of intermediate states and transformation steps, and the Lennard-Jones and Coulomb Softcore potentials parameters in the one-step TI, using eleven benzimidazole inhibitors in complex with Francisella tularensis enoyl acyl reductase (FtFabI). To our knowledge, this is the first study to extensively test the new AMBER MD engine, pmemd, on TI and compare the parameters of the Softcore potentials in the one-step TI in a protein-ligand binding system. The best performing model, the one-step pmemd TI, using 6 intermediate states and 1 ns MD simulations, provides better agreement with experimental results (RMSD = 0.52 kcal/mol) than the best performing implicit solvent method, QM/MM-GBSA from our previous study (RMSD = 3.00 kcal/mol), while maintaining similar efficiency. Briefly, we show the optimized TI protocol to be highly accurate and affordable for the FtFabI system. This approach can be implemented in a larger scale benzimidazole scaffold lead optimization against FtFabI. Lastly, the TI results here also provide structure-activity relationship insights, and suggest the parahalogen in benzimidazole compounds might form a weak halogen bond with FabI, which is a well-known halogen bond favoring enzyme.
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Affiliation(s)
- Pin-Chih Su
- Center for Pharmaceutical Biotechnology, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, U.S.A., 60607
| | - Michael E. Johnson
- Center for Pharmaceutical Biotechnology, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, U.S.A., 60607
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42
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Ley K, Christofferson A, Penna M, Winkler D, Maclaughlin S, Yarovsky I. Surface-water Interface Induces Conformational Changes Critical for Protein Adsorption: Implications for Monolayer Formation of EAS Hydrophobin. Front Mol Biosci 2015; 2:64. [PMID: 26636091 PMCID: PMC4644811 DOI: 10.3389/fmolb.2015.00064] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 10/29/2015] [Indexed: 11/13/2022] Open
Abstract
The class I hydrophobin EAS is part of a family of small, amphiphilic fungal proteins best known for their ability to self-assemble into stable monolayers that modify the hydrophobicity of a surface to facilitate further microbial growth. These proteins have attracted increasing attention for industrial and biomedical applications, with the aim of designing surfaces that have the potential to maintain their clean state by resisting non-specific protein binding. To gain a better understanding of this process, we have employed all-atom molecular dynamics to study initial stages of the spontaneous adsorption of monomeric EAS hydrophobin on fully hydroxylated silica, a commonly used industrial and biomedical substrate. Particular interest has been paid to the Cys3-Cys4 loop, which has been shown to exhibit disruptive behavior in solution, and the Cys7-Cys8 loop, which is believed to be involved in the aggregation of EAS hydrophobin at interfaces. Specific and water mediated interactions with the surface were also analyzed. We have identified two possible binding motifs, one which allows unfolding of the Cys7-Cys8 loop due to the surfactant-like behavior of the Cys3-Cys4 loop, and another which has limited unfolding due to the Cys3-Cys4 loop remaining disordered in solution. We have also identified intermittent interactions with water which mediate the protein adsorption to the surface, as well as longer lasting interactions which control the diffusion of water around the adsorption site. These results have shown that EAS behaves in a similar way at the air-water and surface-water interfaces, and have also highlighted the need for hydrophilic ligand functionalization of the silica surface in order to prevent the adsorption of EAS hydrophobin.
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Affiliation(s)
- Kamron Ley
- Health Innovations Research Institute and School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University Melbourne, VIC, Australia
| | - Andrew Christofferson
- Health Innovations Research Institute and School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University Melbourne, VIC, Australia
| | - Matthew Penna
- Health Innovations Research Institute and School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University Melbourne, VIC, Australia ; Australian Research Council (ARC) Research Hub for Australian Steel Manufacturing Wollongong, NSW, Australia
| | - Dave Winkler
- CSIRO, Manufacturing Flagship Clayton, VIC, Australia ; Institute of Pharmaceutical Science, Monash University Parkville, VIC, Australia ; Institute for Molecular Science, Latrobe University Bundoora, VIC, Australia
| | - Shane Maclaughlin
- Australian Research Council (ARC) Research Hub for Australian Steel Manufacturing Wollongong, NSW, Australia ; BlueScope Steel Research Laboratories Port Kembla, NSW, Australia
| | - Irene Yarovsky
- Health Innovations Research Institute and School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University Melbourne, VIC, Australia ; Australian Research Council (ARC) Research Hub for Australian Steel Manufacturing Wollongong, NSW, Australia
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Ain QU, Aleksandrova A, Roessler FD, Ballester PJ. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2015; 5:405-424. [PMID: 27110292 PMCID: PMC4832270 DOI: 10.1002/wcms.1225] [Citation(s) in RCA: 190] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 07/17/2015] [Accepted: 07/18/2015] [Indexed: 12/29/2022]
Abstract
Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Qurrat Ul Ain
- Department of Chemistry, Centre for Molecular Informatics University of Cambridge Cambridge UK
| | | | - Florian D Roessler
- Department of Chemistry, Centre for Molecular Informatics University of Cambridge Cambridge UK
| | - Pedro J Ballester
- Cancer Research Center of Marseille, (INSERM U1068, Institut Paoli-Calmettes, Aix-Marseille Université, CNRS UMR7258) Marseille France
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ENCORE: Software for Quantitative Ensemble Comparison. PLoS Comput Biol 2015; 11:e1004415. [PMID: 26505632 PMCID: PMC4624683 DOI: 10.1371/journal.pcbi.1004415] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 06/24/2015] [Indexed: 12/15/2022] Open
Abstract
There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large trajectory files.
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Martín-García F, Papaleo E, Gomez-Puertas P, Boomsma W, Lindorff-Larsen K. Comparing molecular dynamics force fields in the essential subspace. PLoS One 2015; 10:e0121114. [PMID: 25811178 PMCID: PMC4374674 DOI: 10.1371/journal.pone.0121114] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 02/10/2015] [Indexed: 12/11/2022] Open
Abstract
The continued development and utility of molecular dynamics simulations requires improvements in both the physical models used (force fields) and in our ability to sample the Boltzmann distribution of these models. Recent developments in both areas have made available multi-microsecond simulations of two proteins, ubiquitin and Protein G, using a number of different force fields. Although these force fields mostly share a common mathematical form, they differ in their parameters and in the philosophy by which these were derived, and previous analyses showed varying levels of agreement with experimental NMR data. To complement the comparison to experiments, we have performed a structural analysis of and comparison between these simulations, thereby providing insight into the relationship between force-field parameterization, the resulting ensemble of conformations and the agreement with experiments. In particular, our results show that, at a coarse level, many of the motional properties are preserved across several, though not all, force fields. At a finer level of detail, however, there are distinct differences in both the structure and dynamics of the two proteins, which can, together with comparison with experimental data, help to select force fields for simulations of proteins. A noteworthy observation is that force fields that have been reparameterized and improved to provide a more accurate energetic description of the balance between helical and coil structures are difficult to distinguish from their "unbalanced" counterparts in these simulations. This observation implies that simulations of stable, folded proteins, even those reaching 10 microseconds in length, may provide relatively little information that can be used to modify torsion parameters to achieve an accurate balance between different secondary structural elements.
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Affiliation(s)
- Fernando Martín-García
- Molecular Modelling Group, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), C/Nicolás Cabrera 1, Cantoblanco, Madrid, Spain
- Biomol-Informatics SL, Parque Científico de Madrid, Cantoblanco, Madrid, Spain
| | - Elena Papaleo
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Paulino Gomez-Puertas
- Molecular Modelling Group, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), C/Nicolás Cabrera 1, Cantoblanco, Madrid, Spain
| | - Wouter Boomsma
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
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BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge. J Comput Aided Mol Des 2015; 29:315-25. [PMID: 25726024 DOI: 10.1007/s10822-014-9795-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Accepted: 09/05/2014] [Indexed: 12/14/2022]
Abstract
The binding energy distribution analysis method (BEDAM) protocol has been employed as part of the SAMPL4 blind challenge to predict the binding free energies of a set of octa-acid host-guest complexes. The resulting predictions were consistently judged as some of the most accurate predictions in this category of the SAMPL4 challenge in terms of quantitative accuracy and statistical correlation relative to the experimental values, which were not known at the time the predictions were made. The work has been conducted as part of a hands-on graduate class laboratory session. Collectively the students, aided by automated setup and analysis tools, performed the bulk of the calculations and the numerical and structural analysis. The success of the experiment confirms the reliability of the BEDAM methodology and it shows that physics-based atomistic binding free energy estimation models, when properly streamlined and automated, can be successfully employed by non-specialists.
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Lee HC, Hsu WC, Liu AL, Hsu CJ, Sun YC. Using thermodynamic integration MD simulation to compute relative protein–ligand binding free energy of a GSK3β kinase inhibitor and its analogs. J Mol Graph Model 2014; 51:37-49. [DOI: 10.1016/j.jmgm.2014.04.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Revised: 04/20/2014] [Accepted: 04/22/2014] [Indexed: 01/15/2023]
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Jämbeck JPM, Lyubartsev AP. Update to the General Amber Force Field for Small Solutes with an Emphasis on Free Energies of Hydration. J Phys Chem B 2014; 118:3793-804. [DOI: 10.1021/jp4111234] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Joakim P. M. Jämbeck
- Division of Physical Chemistry,
Arrhenius Laboratory, Stockholm University, Stockholm SE-10691, Sweden
| | - Alexander P. Lyubartsev
- Division of Physical Chemistry,
Arrhenius Laboratory, Stockholm University, Stockholm SE-10691, Sweden
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Abstract
By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.
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Affiliation(s)
- W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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50
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Converging free energies of binding in cucurbit[7]uril and octa-acid host-guest systems from SAMPL4 using expanded ensemble simulations. J Comput Aided Mol Des 2014; 28:401-15. [PMID: 24610238 DOI: 10.1007/s10822-014-9716-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 01/21/2014] [Indexed: 10/25/2022]
Abstract
Molecular containers such as cucurbit[7]uril (CB7) and the octa-acid (OA) host are ideal simplified model test systems for optimizing and analyzing methods for computing free energies of binding intended for use with biologically relevant protein-ligand complexes. To this end, we have performed initially blind free energy calculations to determine the free energies of binding for ligands of both the CB7 and OA hosts. A subset of the selected guest molecules were those included in the SAMPL4 prediction challenge. Using expanded ensemble simulations in the dimension of coupling host-guest intermolecular interactions, we are able to show that our estimates in most cases can be demonstrated to fully converge and that the errors in our estimates are due almost entirely to the assigned force field parameters and the choice of environmental conditions used to model experiment. We confirm the convergence through the use of alternative simulation methodologies and thermodynamic pathways, analyzing sampled conformations, and directly observing changes of the free energy with respect to simulation time. Our results demonstrate the benefits of enhanced sampling of multiple local free energy minima made possible by the use of expanded ensemble molecular dynamics and may indicate the presence of significant problems with current transferable force fields for organic molecules when used for calculating binding affinities, especially in non-protein chemistries.
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