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Avallone N, Huppert S, Depondt P, Andriambariarijaona L, Datchi F, Ninet S, Plé T, Spezia R, Finocchi F. Orientational Disorder Drives Site Disorder in Plastic Ammonia Hemihydrate. PHYSICAL REVIEW LETTERS 2024; 133:106102. [PMID: 39303235 DOI: 10.1103/physrevlett.133.106102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 07/24/2024] [Indexed: 09/22/2024]
Abstract
In the 2-10 GPa pressure range, ammonia hemihydrate H_{2}O:(NH_{3})_{2} (AHH) is a molecular solid in which intermolecular interactions are ruled by distinct types of hydrogen bonds. Upon heating, the low-temperature ordered P2_{1}/c crystal (AHH-II) transits to a bcc phase (AHH-pbcc) where each site is randomly occupied by water or ammonia. In addition to the site disorder, experiments suggest that AHH-pbcc is a plastic solid, but the physical origin and mechanisms at play for the rotational and site disordering remain unknown. Using large-scale (∼10^{5} atoms) and long-time (>10 ns) simulations, we show that, as temperature rises above the transition line, orientational disorder sets in, breaking the strongest hydrogen bonds that provide the largest contribution to the cohesion of the ordered AHH-II phase and enabling the molecules to migrate from a crystal site to a neighboring one. This generates a plastic molecular alloy with site disorder while the solid state is overall maintained until melting at a higher temperature. The case of high (P,T) plastic ammonia hemihydrate can be extended to other water-ammonia alloys where a similar interplay between distinct hydrogen bonds occurs.
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2
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Takaba K, Friedman AJ, Cavender CE, Behara PK, Pulido I, Henry MM, MacDermott-Opeskin H, Iacovella CR, Nagle AM, Payne AM, Shirts MR, Mobley DL, Chodera JD, Wang Y. Machine-learned molecular mechanics force fields from large-scale quantum chemical data. Chem Sci 2024; 15:12861-12878. [PMID: 39148808 PMCID: PMC11322960 DOI: 10.1039/d4sc00690a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 06/17/2024] [Indexed: 08/17/2024] Open
Abstract
The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.
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Affiliation(s)
- Kenichiro Takaba
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
- Pharmaceuticals Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation Shizuoka 410-2321 Japan
| | - Anika J Friedman
- Department of Chemical and Biological Engineering, University of Colorado Boulder Boulder CO 80309 USA
| | - Chapin E Cavender
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego 9500 Gilman Drive La Jolla CA 92093 USA
| | - Pavan Kumar Behara
- Center for Neurotherapeutics, Department of Pathology and Laboratory Medicine, University of California Irvine CA 92697 USA
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
| | - Michael M Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
| | | | - Christopher R Iacovella
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
| | - Arnav M Nagle
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
- Department of Bioengineering, University of California, Berkeley Berkeley CA 94720 USA
| | - Alexander Matthew Payne
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center New York 10065 USA
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder Boulder CO 80309 USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California Irvine California 92697 USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
| | - Yuanqing Wang
- Simons Center for Computational Physical Chemistry and Center for Data Science, New York University New York NY 10004 USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA
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3
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Angelo M, Zhang W, Vilseck JZ, Aoki ST. In silico λ-dynamics predicts protein binding specificities to modified RNAs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577511. [PMID: 38328125 PMCID: PMC10849657 DOI: 10.1101/2024.01.26.577511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.
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Affiliation(s)
- Murphy Angelo
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
| | - Wen Zhang
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA
| | - Jonah Z. Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Scott T. Aoki
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA
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4
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Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MM, Hu F, Huang J, Krämer A, Michel J, Mitchell JA, Pande VS, Rodrigues JPGLM, Rodriguez-Guerra J, Simmonett AC, Singh S, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, De Fabritiis G, Markland TE. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. J Phys Chem B 2024; 128:109-116. [PMID: 38154096 PMCID: PMC10846090 DOI: 10.1021/acs.jpcb.3c06662] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Raimondas Galvelis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Raúl P. Peláez
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Charlles R. A. Abreu
- Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil
- Redesign Science Inc., 180 Varick St., New York, NY 10014, USA
| | - Stephen E. Farr
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, NY, USA
- Ph.D. Program in Chemistry and Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY, USA
| | - Anton Gorenko
- Stream HPC, Koningin Wilhelminaplein 1 - 40601, 1062 HG Amsterdam, Netherlands
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Frank Hu
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Andreas Krämer
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Joshua A. Mitchell
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, CA 95616, USA
| | - Vijay S. Pande
- Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, CA 94025, USA
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - João PGLM Rodrigues
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - Jaime Rodriguez-Guerra
- Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany
| | - Andrew C. Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Jason Swails
- Entos Inc., 9310 Athena Circle, La Jolla, CA 92037, USA
| | - Philip Turner
- College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Yuanqing Wang
- Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, NY 10004, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Gianni De Fabritiis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
- ICREA, Passeig Lluis Companys 23, 08010, Barcelona, Spain
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5
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dos Santos Nascimento IJ, Santana Gomes JN, de Oliveira Viana J, de Medeiros e Silva YMS, Barbosa EG, de Moura RO. The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors. Mini Rev Med Chem 2024; 24:1125-1146. [PMID: 37680157 PMCID: PMC11337241 DOI: 10.2174/1389557523666230901152257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 06/15/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023]
Abstract
A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.
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Affiliation(s)
- Igor José dos Santos Nascimento
- Department of Pharmacy, Cesmac University Center, Maceió, 57051-160, Brazil
- Department of Pharmacy, Drug Development and Synthesis Laboratory, State University of Paraíba, Campina Grande, 58429-500, Brazil
- Post-graduate Program in Pharmaceutical Sciences, State University of Paraíba, Campina Grande, 58429-500, Brazil
| | - Joilly Nilce Santana Gomes
- Department of Pharmacy, Drug Development and Synthesis Laboratory, State University of Paraíba, Campina Grande, 58429-500, Brazil
| | - Jéssika de Oliveira Viana
- Post-graduate Program in Bioinformatics, Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Yvnni Maria Sales de Medeiros e Silva
- Department of Pharmacy, Drug Development and Synthesis Laboratory, State University of Paraíba, Campina Grande, 58429-500, Brazil
- Post-graduate Program in Pharmaceutical Sciences, State University of Paraíba, Campina Grande, 58429-500, Brazil
| | - Euzébio Guimarães Barbosa
- Post-graduate Program in Bioinformatics, Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil
- Post-graduate Program in Pharmaceutical Sciences, Faculty of Pharmacy, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ricardo Olimpio de Moura
- Department of Pharmacy, Drug Development and Synthesis Laboratory, State University of Paraíba, Campina Grande, 58429-500, Brazil
- Post-graduate Program in Pharmaceutical Sciences, State University of Paraíba, Campina Grande, 58429-500, Brazil
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6
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Fass J, York F, Wittmann M, Kaus J, Zhao Y. Local Resampling Trick for Focused Molecular Dynamics. J Chem Theory Comput 2023; 19:6139-6150. [PMID: 37706456 DOI: 10.1021/acs.jctc.3c00539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
We describe a method that focuses sampling effort on a user-defined selection of a large system, which can lead to substantial decreases in computational effort by speeding up the calculation of nonbonded interactions. A naive approach can lead to incorrect sampling if the selection depends on the configuration in a way that is not accounted for. We avoid this pitfall by introducing appropriate auxiliary variables. This results in an implementation that is closely related to "configurational freezing" and "elastic barrier dynamical freezing." We implement the method and validate that it can be used to supplement conventional molecular dynamics in free energy calculations (absolute hydration and relative binding).
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Affiliation(s)
- Joshua Fass
- Computation, Relay Therapeutics, Cambridge, Massachusetts 02139, United States
| | - Forrest York
- Computation, Relay Therapeutics, Cambridge, Massachusetts 02139, United States
| | - Matthew Wittmann
- Computation, Relay Therapeutics, Cambridge, Massachusetts 02139, United States
| | - Joseph Kaus
- Computation, Relay Therapeutics, Cambridge, Massachusetts 02139, United States
| | - Yutong Zhao
- Computation, Relay Therapeutics, Cambridge, Massachusetts 02139, United States
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7
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Naleem N, Abreu CRA, Warmuz K, Tong M, Kirmizialtin S, Tuckerman ME. An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions. J Chem Phys 2023; 159:034102. [PMID: 37458344 DOI: 10.1063/5.0147597] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/23/2023] [Indexed: 07/20/2023] Open
Abstract
Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the "feature importance" approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.
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Affiliation(s)
- Nawavi Naleem
- Chemistry Program, Science Division, New York University, Abu Dhabi, UAE
| | - Charlles R A Abreu
- Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, 21941-909 Rio de Janeiro, RJ, Brazil
| | - Krzysztof Warmuz
- Computer Science Program, Science Division, New York University, Abu Dhabi, UAE
| | - Muchen Tong
- Department of Chemistry, New York University (NYU), New York, New York 10003, USA
| | - Serdal Kirmizialtin
- Chemistry Program, Science Division, New York University, Abu Dhabi, UAE
- Department of Chemistry, New York University (NYU), New York, New York 10003, USA
- Center for Smart Engineering Materials, New York University, Abu Dhabi, UAE
| | - Mark E Tuckerman
- Department of Chemistry, New York University (NYU), New York, New York 10003, USA
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Rd. North, Shanghai 200062, China
- Simons Center for Computational Physical Chemistry at New York University, New York, New York 10003, USA
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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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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9
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Zhang J, Zhang H, Qin Z, Kang Y, Hong X, Hou T. Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials. J Chem Inf Model 2023; 63:1133-1142. [PMID: 36791039 DOI: 10.1021/acs.jcim.2c01497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Direct trajectory calculations have become increasingly popular in recent computational chemistry investigations. However, the exorbitant computational cost of ab initio trajectory calculations usually limits its application in mechanistic explorations. Recently, machine learning-based potential energy surface (ML-PES) provides a powerful strategy to circumvent the heavy computational cost and meanwhile maintain the required accuracy. Despite the appealing potential, constructing a robust ML-PES is still challenging since the training set of the PES should cover a broad enough configuration space. In this work, we demonstrate that when the concerned properties could be collected by the localized sampling of the configuration space, quasiclassical trajectory (QCT) calculations can be invoked to efficiently obtain locally accurate ML-PESs. We prove our concept with two model reactions: methyl migration of i-pentane cation and dimerization of cyclopentadiene. We found that the locally accurate ML-PESs are sufficiently robust for reproducing the static and dynamic features of the reactions, including the time-resolved free energy and entropy changes, and time gaps.
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Affiliation(s)
- Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zhixin Qin
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, Zhejiang, China.,Beijing National Laboratory for Molecular Sciences, North First Street No. 2, Zhongguancun, Beijing 100190, China.,Key Laboratory of Precise Synthesis of Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Laboratory of Computer-aided Design & Computer Graphics, Zhejiang University, Hangzhou 310058, Zhejiang, China
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10
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Liang J, Tan P, Hong L, Jin S, Xu Z, Li L. A random batch Ewald method for charged particles in the isothermal-isobaric ensemble. J Chem Phys 2022; 157:144102. [PMID: 36243529 DOI: 10.1063/5.0107140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We develop an accurate, highly efficient, and scalable random batch Ewald (RBE) method to conduct molecular dynamics simulations in the isothermal-isobaric ensemble (the NPT ensemble) for charged particles in a periodic box. After discretizing the Langevin equations of motion derived using suitable Lagrangians, the RBE method builds the mini-batch strategy into the Fourier space in the Ewald summation for the pressure and forces such that the computational cost is reduced to O(N) per time step. We implement the method in the Large-scale Atomic/Molecular Massively Parallel Simulator package and report accurate simulation results for both dynamical quantities and statistics for equilibrium for typical systems including all-atom bulk water and a semi-isotropic membrane system. Numerical simulations on massive supercomputing cluster are also performed to show promising central processing unit efficiency of the RBE.
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Affiliation(s)
- Jiuyang Liang
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Pan Tan
- School of Physics and Astronomy and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Liang Hong
- School of Physics and Astronomy and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Shi Jin
- School of Mathematical Sciences, Institute of Natural Sciences and MoE-LSC, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhenli Xu
- School of Mathematical Sciences, Institute of Natural Sciences and MoE-LSC, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lei Li
- School of Mathematical Sciences, Institute of Natural Sciences and MoE-LSC, Shanghai Jiao Tong University, Shanghai 200240, China
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11
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Kieninger S, Keller BG. GROMACS Stochastic Dynamics and BAOAB Are Equivalent Configurational Sampling Algorithms. J Chem Theory Comput 2022; 18:5792-5798. [DOI: 10.1021/acs.jctc.2c00585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Stefanie Kieninger
- Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Arnimallee 22, D-14195 Berlin, Germany
| | - Bettina G. Keller
- Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Arnimallee 22, D-14195 Berlin, Germany
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12
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Barhaghi MS, Crawford B, Schwing G, Hardy DJ, Stone JE, Schwiebert L, Potoff J, Tajkhorshid E. py-MCMD: Python Software for Performing Hybrid Monte Carlo/Molecular Dynamics Simulations with GOMC and NAMD. J Chem Theory Comput 2022; 18:4983-4994. [PMID: 35621307 PMCID: PMC9760104 DOI: 10.1021/acs.jctc.1c00911] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
py-MCMD, an open-source Python software, provides a robust workflow layer that manages communication of relevant system information between the simulation engines NAMD and GOMC and generates coherent thermodynamic properties and trajectories for analysis. To validate the workflow and highlight its capabilities, hybrid Monte Carlo/molecular dynamics (MC/MD) simulations are performed for SPC/E water in the isobaric-isothermal (NPT) and grand canonical (GC) ensembles as well as with Gibbs ensemble Monte Carlo (GEMC). The hybrid MC/MD approach shows close agreement with reference MC simulations and has a computational efficiency that is 2 to 136 times greater than traditional Monte Carlo simulations. MC/MD simulations performed for water in a graphene slit pore illustrate significant gains in sampling efficiency when the coupled-decoupled configurational-bias MC (CD-CBMC) algorithm is used compared with simulations using a single unbiased random trial position. Simulations using CD-CBMC reach equilibrium with 25 times fewer cycles than simulations using a single unbiased random trial position, with a small increase in computational cost. In a more challenging application, hybrid grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations are used to hydrate a buried binding pocket in bovine pancreatic trypsin inhibitor. Water occupancies produced by GCMC/MD simulations are in close agreement with crystallographically identified positions, and GCMC/MD simulations have a computational efficiency that is 5 times better than MD simulations. py-MCMD is available on GitHub at https://github.com/GOMC-WSU/py-MCMD.
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Affiliation(s)
- Mohammad Soroush Barhaghi
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Brad Crawford
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Gregory Schwing
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - David J Hardy
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - John E Stone
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Loren Schwiebert
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Jeffrey Potoff
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Emad Tajkhorshid
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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13
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Cox S, White AD. Symmetric Molecular Dynamics. J Chem Theory Comput 2022; 18:4077-4081. [PMID: 35699649 PMCID: PMC9281392 DOI: 10.1021/acs.jctc.2c00401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We derive a formulation of molecular dynamics that generates only symmetric configurations. We implement it for all 2D planar and 3D space groups. An atlas of 2D Lennard-Jones crystals under all planar groups is created with symmetric molecular dynamics.
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Affiliation(s)
- Sam Cox
- Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States
| | - Andrew D. White
- Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States
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14
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Maffeo C, Chou HY, Aksimentiev A. Single-molecule biophysics experiments in silico: Toward a physical model of a replisome. iScience 2022; 25:104264. [PMID: 35521518 PMCID: PMC9062759 DOI: 10.1016/j.isci.2022.104264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/23/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022] Open
Abstract
The interpretation of single-molecule experiments is frequently aided by computational modeling of biomolecular dynamics. The growth of computing power and ongoing validation of computational models suggest that it soon may be possible to replace some experiments outright with computational mimics. Here, we offer a blueprint for performing single-molecule studies in silico using a DNA-binding protein as a test bed. We demonstrate how atomistic simulations, typically limited to sub-millisecond durations and zeptoliter volumes, can guide development of a coarse-grained model for use in simulations that mimic single-molecule experiments. We apply the model to recapitulate, in silico, force-extension characterization of protein binding to single-stranded DNA and protein and DNA replacement assays, providing a detailed portrait of the underlying mechanics. Finally, we use the model to simulate the trombone loop of a replication fork, a large complex of proteins and DNA. Coarse-grained model derived from all-atom simulation recapitulates experiments Model reproduces the elastic response to force and exchange dynamics Model reveals structure of intermediate states usually inaccessible to experiment Model applied to viral replisome with trombone loop containing tens of SSB proteins
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Affiliation(s)
- Christopher Maffeo
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 W Green St, Urbana, 61801 IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N Matthews Avenue, Urbana, 61801 IL, USA
| | - Han-Yi Chou
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 W Green St, Urbana, 61801 IL, USA
| | - Aleksei Aksimentiev
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 W Green St, Urbana, 61801 IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N Matthews Avenue, Urbana, 61801 IL, USA
- Corresponding author
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15
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Graham MM, Thiery AH, Beskos A. Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models. J R Stat Soc Series B Stat Methodol 2022. [DOI: 10.1111/rssb.12497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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16
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Hervø-Hansen S, Heyda J, Lund M, Matubayasi N. Anion-cation contrast of small molecule solvation in salt solutions. Phys Chem Chem Phys 2022; 24:3238-3249. [PMID: 35044392 PMCID: PMC8809138 DOI: 10.1039/d1cp04129k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/24/2021] [Indexed: 11/21/2022]
Abstract
The contributions from anions and cations from salt are inseparable in their perturbation of molecular systems by experimental and computational methods, rendering it difficult to dissect the effects exerted by the anions and cations individually. Here we investigate the solvation of a small molecule, caffeine, and its perturbation by monovalent salts from various parts of the Hofmeister series. Using molecular dynamics and the energy-representation theory of solvation, we estimate the solvation free energy of caffeine and decompose it into the contributions from anions, cations, and water. We also decompose the contributions arising from the solute-solvent and solute-ions interactions and that from excluded volume, enabling us to pin-point the mechanism of salt. Anions and cations revealed high contrast in their perturbation of caffeine solvation, with the cations salting-in caffeine via binding to the polar ketone groups, while the anions were found to be salting-out via perturbations of water. In agreement with previous findings, the perturbation by salt is mostly anion dependent, with the magnitude of the excluded-volume effect found to be the governing mechanism. The free-energy decomposition as conducted in the present work can be useful to understand ion-specific effects and the associated Hofmeister series.
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Affiliation(s)
- Stefan Hervø-Hansen
- Division of Theoretical Chemistry, Department of Chemistry, Lund University, Lund SE 221 00, Sweden.
| | - Jan Heyda
- Department of Physical Chemistry, University of Chemistry and Technology, Prague CZ-16628, Czech Republic.
| | - Mikael Lund
- Division of Theoretical Chemistry, Department of Chemistry, Lund University, Lund SE 221 00, Sweden.
- Lund Institute for Advanced Neutron and X-ray Science (LINXS), Lund University, Lund, Sweden
| | - Nobuyuki Matubayasi
- Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka 560-8531, Japan.
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17
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Sun Z, Kalhor P, Xu Y, Liu J. Extensive numerical tests of leapfrog integrator in middle thermostat scheme in molecular simulations. CHINESE J CHEM PHYS 2021. [DOI: 10.1063/1674-0068/cjcp2111242] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Zhaoxi Sun
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Institute of Theoretical and Computational Chemistry, Peking University, Beijing 100871, China
| | - Payam Kalhor
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Institute of Theoretical and Computational Chemistry, Peking University, Beijing 100871, China
| | - Yang Xu
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Institute of Theoretical and Computational Chemistry, Peking University, Beijing 100871, China
| | - Jian Liu
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Institute of Theoretical and Computational Chemistry, Peking University, Beijing 100871, China
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18
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Hayes RL, Buckner J, Brooks CL. BLaDE: A Basic Lambda Dynamics Engine for GPU-Accelerated Molecular Dynamics Free Energy Calculations. J Chem Theory Comput 2021; 17:6799-6807. [PMID: 34709046 DOI: 10.1021/acs.jctc.1c00833] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There is an accelerating interest in practical applications of alchemical free energy methods to problems in protein design, constant pH simulations, and especially computer-aided drug design. In the present paper, we describe a basic lambda dynamics engine (BLaDE) that enables alchemical free energy simulations, including multisite λ dynamics (MSλD) simulations, on graphical processor units (GPUs). We find that BLaDE is 5 to 8 times faster than the current GPU implementation of MSλD-based free energy calculations in CHARMM. We also demonstrate that BLaDE running standard molecular dynamics attains a performance competitive with and sometimes exceeding that of the highly optimized OpenMM GPU code. BLaDE is available as a standalone program and through an API in CHARMM.
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Affiliation(s)
- Ryan L Hayes
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Joshua Buckner
- 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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19
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Pinski FJ. A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space. ENTROPY 2021; 23:e23050499. [PMID: 33922040 PMCID: PMC8143484 DOI: 10.3390/e23050499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 11/16/2022]
Abstract
To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, hybrid Monte Carlo on Hilbert spaces (Beskos, et al. Stoch. Proc. Applic. 2011), that provides finite-dimensional approximations of measures π, which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the algorithm described in this article lies in the choice of this operator. This new choice defines a Markov Chain Monte Carlo (MCMC) method that is well defined on the Hilbert space itself. As before, the algorithm described herein uses an enlarged phase space Π having the target π as a marginal, together with a Hamiltonian flow that preserves Π. In the previous work, the authors explored a method where the phase space π was augmented with Brownian bridges. With this new choice, π is augmented by Ornstein–Uhlenbeck (OU) bridges. The covariance of Brownian bridges grows with its length, which has negative effects on the acceptance rate in the MCMC method. This contrasts with the covariance of OU bridges, which is independent of the path length. The ingredients of the new algorithm include the definition of the mass operator, the equations for the Hamiltonian flow, the (approximate) numerical integration of the evolution equations, and finally, the Metropolis–Hastings acceptance rule. Taken together, these constitute a robust method for sampling the target distribution in an almost dimension-free manner. The behavior of this novel algorithm is demonstrated by computer experiments for a particle moving in two dimensions, between two free-energy basins separated by an entropic barrier.
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Affiliation(s)
- Francis J Pinski
- Department of Physics, University of Cincinnati, Cincinnati, OH 45221, USA
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20
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Holmes-Cerfon M. Simulating sticky particles: A Monte Carlo method to sample a stratification. J Chem Phys 2020; 153:164112. [PMID: 33138386 DOI: 10.1063/5.0019550] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions.
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Affiliation(s)
- Miranda Holmes-Cerfon
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
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21
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Rossi K, Jurásková V, Wischert R, Garel L, Corminbœuf C, Ceriotti M. Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH 3SO 3H and H 2O 2 in Phenol. J Chem Theory Comput 2020; 16:5139-5149. [PMID: 32567854 DOI: 10.1021/acs.jctc.0c00362] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network (NN) potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time-step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant noncovalent interactions in the mixture, also considering nuclear quantum effects.
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Affiliation(s)
- Kevin Rossi
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Veronika Jurásková
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Raphael Wischert
- Eco-Efficient Products and Processes Laboratory, Solvay, RIC Shanghai, Shanghai 201108, China
| | - Laurent Garel
- Aroma Performance Laboratory, Solvay, RIC Lyon, 69190 Saint-Fons, France
| | - Clémence Corminbœuf
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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22
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Mey ASJS, Allen BK, Macdonald HEB, Chodera JD, Hahn DF, Kuhn M, Michel J, Mobley DL, Naden LN, Prasad S, Rizzi A, Scheen J, Shirts MR, Tresadern G, Xu H. Best Practices for Alchemical Free Energy Calculations [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2020; 2:18378. [PMID: 34458687 PMCID: PMC8388617 DOI: 10.33011/livecoms.2.1.18378] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.
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Affiliation(s)
- Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Maximilian Kuhn
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
- Cresset, Cambridgeshire, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, Irvine, USA
| | - Levi N. Naden
- Molecular Sciences Software Institute, Blacksburg VA, USA
| | | | - Andrea Rizzi
- Silicon Therapeutics, Boston, MA, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Jenke Scheen
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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23
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Samala N, Agmon N. Thermally Induced Hydrogen-Bond Rearrangements in Small Water Clusters and the Persistent Water Tetramer. ACS OMEGA 2019; 4:22581-22590. [PMID: 31909342 PMCID: PMC6941388 DOI: 10.1021/acsomega.9b03326] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 11/26/2019] [Indexed: 06/10/2023]
Abstract
Small water clusters absorb heat and catalyze pivotal atmospheric reactions. Yet, experiments produced conflicting results on water cluster distribution under atmospheric conditions. Additionally, it is unclear which "phase transitions" such clusters exhibit, at what temperatures, and what are their underlying molecular mechanisms. We find that logarithmically small tails in the radial probability densities of (H2O) n clusters (n = 2 - 6) provide direct testimony for such transitions. Using the best available water potential (MB-pol), an advanced thermostating algorithm (g-BAOAB), and sufficiently long trajectories, we map the "bifurcation", "melting", and (hitherto unexplored) "vaporization" transitions, finding that both melting and vaporization proceed via a "monomer on a ring" conformer, exhibiting huge distance fluctuations at the vaporization temperatures (T v). T v may play a role in determining the atmospheric cluster size distribution such that the dimer and tetramer, with their exceptionally low/high T v values, are under/over-represented in these distributions, as indeed observed in nondestructive mass spectrometric measurements.
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24
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Samala NR, Agmon N. Temperature Dependence of Intramolecular Vibrational Bands in Small Water Clusters. J Phys Chem B 2019; 123:9428-9442. [PMID: 31553613 DOI: 10.1021/acs.jpcb.9b07777] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Cyclic water clusters are pivotal for understanding atmospheric reactions as well as liquid water, yet the temperature (T) dependence of their dynamics and spectroscopy is poorly studied. The development of highly accurate water potentials, such as MB-pol, partly rectifies this. It remains to account for the quantum nuclear effects (NQE), because quantum nuclear dynamics become increasingly inaccurate at low temperatures. From a practical point of view, we find that NQE can be accounted for simply by subtracting a constant from the frequencies obtained from the velocity autocorrelation functions (VACF) of classical nuclear dynamics, resulting in unprecedented agreement with experiment, mostly within 5 cm-1. We have performed classical simulations of (H2O)n clusters (n = 2-5) from 20 K and up to their melting temperature, calculating both all-atom and partial VACF, thus generating the temperature dependence of the vibrational frequencies (IR and Raman bands). Focusing on the hydrogen-bonded (HBed) OH stretch and HOH bend, we find opposing T dependencies. The HBed OH modes blue shift linearly with T, attributed to ring expansion rather than any specific conformational change. The lowest-frequency Raman concerted mode is predicted to show the largest such shift. In contrast, the HOH bend undergoes a red-shift, with the highest frequency concerted band undergoing the largest red-shift. These results can be explained by a coupled-oscillator model for n hydrogen atoms on a ring, constrained to move either tangentially (stretch) or perpendicularly (bend) to the ring. With increasing temperature and weakening of HBs, the intrinsic force constant increases (stretch) or remains constant (bend), while the nearest-neighbor coupling constant decreases, and this results in the interesting behavior revealed herein. T-dependent Raman studies are required for testing some of these predictions.
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Affiliation(s)
- Nagaprasad Reddy Samala
- The Fritz Haber Research Center, Institute of Chemistry , The Hebrew University of Jerusalem , Jerusalem 91904 , Israel
| | - Noam Agmon
- The Fritz Haber Research Center, Institute of Chemistry , The Hebrew University of Jerusalem , Jerusalem 91904 , Israel
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25
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Trenins G, Willatt MJ, Althorpe SC. Path-integral dynamics of water using curvilinear centroids. J Chem Phys 2019. [DOI: 10.1063/1.5100587] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- George Trenins
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Michael J. Willatt
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Stuart C. Althorpe
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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26
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Zhang Z, Liu X, Yan K, Tuckerman ME, Liu J. Unified Efficient Thermostat Scheme for the Canonical Ensemble with Holonomic or Isokinetic Constraints via Molecular Dynamics. J Phys Chem A 2019; 123:6056-6079. [PMID: 31117592 DOI: 10.1021/acs.jpca.9b02771] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We have recently proposed a new unified theoretical scheme (the "middle" scheme) for thermostat algorithms for efficient and accurate configurational sampling of the canonical ensemble. In this paper, we extend the "middle" scheme to molecular dynamics algorithms for configurational sampling in systems subject to constraints. Holonomic constraints and isokinetic constraints are used for demonstration. Numerical examples indicate that the "middle" scheme presents a promising approach to calculate configuration-dependent thermodynamic properties and their thermal fluctuations.
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Affiliation(s)
- Zhijun Zhang
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China
| | - Xinzijian Liu
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China
| | - Kangyu Yan
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China
| | - Mark E Tuckerman
- Department of Chemistry , New York University , New York , New York 10003 , United States.,Courant Institute of Mathematical Sciences , New York University , New York , New York 10012 , United States.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai , 3663 Zhongshan Road North , Shanghai 200062 , China
| | - Jian Liu
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China
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27
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Minuesa G, Albanese SK, Xie W, Kazansky Y, Worroll D, Chow A, Schurer A, Park SM, Rotsides CZ, Taggart J, Rizzi A, Naden LN, Chou T, Gourkanti S, Cappel D, Passarelli MC, Fairchild L, Adura C, Glickman JF, Schulman J, Famulare C, Patel M, Eibl JK, Ross GM, Bhattacharya S, Tan DS, Leslie CS, Beuming T, Patel DJ, Goldgur Y, Chodera JD, Kharas MG. Small-molecule targeting of MUSASHI RNA-binding activity in acute myeloid leukemia. Nat Commun 2019; 10:2691. [PMID: 31217428 PMCID: PMC6584500 DOI: 10.1038/s41467-019-10523-3] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 05/16/2019] [Indexed: 12/30/2022] Open
Abstract
The MUSASHI (MSI) family of RNA binding proteins (MSI1 and MSI2) contribute to a wide spectrum of cancers including acute myeloid leukemia. We find that the small molecule Ro 08-2750 (Ro) binds directly and selectively to MSI2 and competes for its RNA binding in biochemical assays. Ro treatment in mouse and human myeloid leukemia cells results in an increase in differentiation and apoptosis, inhibition of known MSI-targets, and a shared global gene expression signature similar to shRNA depletion of MSI2. Ro demonstrates in vivo inhibition of c-MYC and reduces disease burden in a murine AML leukemia model. Thus, we identify a small molecule that targets MSI's oncogenic activity. Our study provides a framework for targeting RNA binding proteins in cancer.
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Affiliation(s)
- Gerard Minuesa
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Steven K Albanese
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Wei Xie
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yaniv Kazansky
- Weill Cornell Medical College, Tri-Institutional MD-PhD Program, Rockefeller University and Sloan Kettering Institute, New York, NY, 10065, USA
| | - Daniel Worroll
- Department of Pharmacology, Weill Cornell Graduate School of Medical Sciences, New York, NY, 10065, USA
| | - Arthur Chow
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Alexandra Schurer
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Sun-Mi Park
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Christina Z Rotsides
- Chemical Biology Program, Sloan Kettering Institute and Tri-Institutional Research Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - James Taggart
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, NY, 10065, USA
| | - Levi N Naden
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Timothy Chou
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Saroj Gourkanti
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | | | - Maria C Passarelli
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Weill Cornell Medical College, Tri-Institutional MD-PhD Program, Rockefeller University and Sloan Kettering Institute, New York, NY, 10065, USA
| | - Lauren Fairchild
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, NY, 10065, USA
| | - Carolina Adura
- High-Throughput and Spectroscopy Resource Center, The Rockefeller University, New York, NY, 10065, USA
| | - J Fraser Glickman
- High-Throughput and Spectroscopy Resource Center, The Rockefeller University, New York, NY, 10065, USA
| | - Jessica Schulman
- Hematologic Oncology Tissue Bank, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Christopher Famulare
- Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Minal Patel
- Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joseph K Eibl
- Northern Ontario School of Medicine, Sudbury, ON, P3E 2C6, Canada
| | - Gregory M Ross
- Northern Ontario School of Medicine, Sudbury, ON, P3E 2C6, Canada
| | | | - Derek S Tan
- Chemical Biology Program, Sloan Kettering Institute and Tri-Institutional Research Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Christina S Leslie
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Thijs Beuming
- Schrödinger, Inc., 120 West 45th Street, New York, NY, 10036, USA
| | - Dinshaw J Patel
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yehuda Goldgur
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Michael G Kharas
- Molecular Pharmacology Program, Experimental Therapeutics Center and Center for Stem Cell Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.
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Célerse F, Lagardère L, Derat E, Piquemal JP. Massively Parallel Implementation of Steered Molecular Dynamics in Tinker-HP: Comparisons of Polarizable and Non-Polarizable Simulations of Realistic Systems. J Chem Theory Comput 2019; 15:3694-3709. [DOI: 10.1021/acs.jctc.9b00199] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Frédéric Célerse
- Laboratoire de Chimie Théorique, UMR 7616 CNRS, Sorbonne Université, 75005 Paris, France
- Institut Parisien de Chimie Moléculaire, UMR 8232 CNRS, Sorbonne Université, 75005 Paris, France
| | - Louis Lagardère
- Institut des Sciences du Calcul et des Données, Sorbonne Université, 75005 Paris, France
- Institut Parisien de Chimie Physique et Théorique, FR 2622 CNRS, Sorbonne Université, 75005 Paris, France
- Laboratoire de Chimie théorique, UMR 7616 CNRS, Sorbonne Université, 75005 Paris, France
| | - Etienne Derat
- Institut Parisien de Chimie Moléculaire, UMR 8232 CNRS, Sorbonne Université, 75005 Paris, France
| | - Jean-Philip Piquemal
- Laboratoire de Chimie Théorique, UMR 7616 CNRS, Sorbonne Université, 75005 Paris, France
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Institut Universitaire de France, 75005 Paris, France
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29
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Di Pasquale N, Hudson T, Icardi M. Systematic derivation of hybrid coarse-grained models. Phys Rev E 2019; 99:013303. [PMID: 30780282 DOI: 10.1103/physreve.99.013303] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Indexed: 06/09/2023]
Abstract
Molecular dynamics represents a key enabling technology for applications ranging from biology to the development of new materials. However, many real-world applications remain inaccessible to fully resolved simulations due to their unsustainable computational costs and must therefore rely on semiempirical coarse-grained models. Significant efforts have been devoted in the last decade towards improving the predictivity of these coarse-grained models and providing a rigorous justification of their use, through a combination of theoretical studies and data-driven approaches. One of the most promising research efforts is the (re)discovery of the Mori-Zwanzig projection as a generic, yet systematic, theoretical tool for deriving coarse-grained models. Despite its clean mathematical formulation and generality, there are still many open questions about its applicability and assumptions. In this work, we propose a detailed derivation of a hybrid multiscale system, generalizing and further investigating the approach developed in Español [Europhys. Lett. 88, 40008 (2009)10.1209/0295-5075/88/40008]. Issues such as the general coexistence of atoms (fully resolved degrees of freedom) and beads (larger coarse-grained units), the role of the fine-to-coarse mapping chosen, and the approximation of effective potentials are discussed. The theoretical discussion is supported by numerical simulations of a monodimensional nonlinear periodic benchmark system with an open-source parallel Julia code, easily extensible to arbitrary potential models and fine-to-coarse mapping functions. The results presented highlight the importance of introducing, in the macroscopic model, nonconstant fluctuating and dissipative terms, given by the Mori-Zwanzig approach, to correctly reproduce the reference fine-grained results, without requiring ad hoc calibration of interaction potentials and thermostats.
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Affiliation(s)
- Nicodemo Di Pasquale
- Department of Mathematics, University of Leicester, University Road, Leicester LE1 7RH, United Kingdom
| | - Thomas Hudson
- Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Matteo Icardi
- School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
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30
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Bornn L, Shephard N, Solgi R. Moment conditions and Bayesian non-parametrics. J R Stat Soc Series B Stat Methodol 2018. [DOI: 10.1111/rssb.12294] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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31
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Davidchack RL, Ouldridge TE, Tretyakov MV. Geometric integrator for Langevin systems with quaternion-based rotational degrees of freedom and hydrodynamic interactions. J Chem Phys 2018; 147:224103. [PMID: 29246069 DOI: 10.1063/1.4999771] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
We introduce new Langevin-type equations describing the rotational and translational motion of rigid bodies interacting through conservative and non-conservative forces and hydrodynamic coupling. In the absence of non-conservative forces, the Langevin-type equations sample from the canonical ensemble. The rotational degrees of freedom are described using quaternions, the lengths of which are exactly preserved by the stochastic dynamics. For the proposed Langevin-type equations, we construct a weak 2nd order geometric integrator that preserves the main geometric features of the continuous dynamics. The integrator uses Verlet-type splitting for the deterministic part of Langevin equations appropriately combined with an exactly integrated Ornstein-Uhlenbeck process. Numerical experiments are presented to illustrate both the new Langevin model and the numerical method for it, as well as to demonstrate how inertia and the coupling of rotational and translational motion can introduce qualitatively distinct behaviours.
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Affiliation(s)
- R L Davidchack
- Department of Mathematics, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - T E Ouldridge
- Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - M V Tretyakov
- School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
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32
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Zhang Z, Liu X, Chen Z, Zheng H, Yan K, Liu J. A unified thermostat scheme for efficient configurational sampling for classical/quantum canonical ensembles via molecular dynamics. J Chem Phys 2018; 147:034109. [PMID: 28734283 DOI: 10.1063/1.4991621] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We show a unified second-order scheme for constructing simple, robust, and accurate algorithms for typical thermostats for configurational sampling for the canonical ensemble. When Langevin dynamics is used, the scheme leads to the BAOAB algorithm that has been recently investigated. We show that the scheme is also useful for other types of thermostats, such as the Andersen thermostat and Nosé-Hoover chain, regardless of whether the thermostat is deterministic or stochastic. In addition to analytical analysis, two 1-dimensional models and three typical real molecular systems that range from the gas phase, clusters, to the condensed phase are used in numerical examples for demonstration. Accuracy may be increased by an order of magnitude for estimating coordinate-dependent properties in molecular dynamics (when the same time interval is used), irrespective of which type of thermostat is applied. The scheme is especially useful for path integral molecular dynamics because it consistently improves the efficiency for evaluating all thermodynamic properties for any type of thermostat.
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Affiliation(s)
- Zhijun Zhang
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Xinzijian Liu
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Zifei Chen
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Haifeng Zheng
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Kangyu Yan
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jian Liu
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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33
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Fass J, Sivak DA, Crooks GE, Beauchamp KA, Leimkuhler B, Chodera JD. Quantifying Configuration-Sampling Error in Langevin Simulations of Complex Molecular Systems. ENTROPY 2018; 20. [PMID: 30393452 PMCID: PMC6208357 DOI: 10.3390/e20050318] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
While Langevin integrators are popular in the study of equilibrium properties of complex systems, it is challenging to estimate the timestep-induced discretization error: the degree to which the sampled phase-space or configuration-space probability density departs from the desired target density due to the use of a finite integration timestep. Sivak et al., introduced a convenient approach to approximating a natural measure of error between the sampled density and the target equilibrium density, the Kullback-Leibler (KL) divergence, in phase space, but did not specifically address the issue of configuration-space properties, which are much more commonly of interest in molecular simulations. Here, we introduce a variant of this near-equilibrium estimator capable of measuring the error in the configuration-space marginal density, validating it against a complex but exact nested Monte Carlo estimator to show that it reproduces the KL divergence with high fidelity. To illustrate its utility, we employ this new near-equilibrium estimator to assess a claim that a recently proposed Langevin integrator introduces extremely small configuration-space density errors up to the stability limit at no extra computational expense. Finally, we show how this approach to quantifying sampling bias can be applied to a wide variety of stochastic integrators by following a straightforward procedure to compute the appropriate shadow work, and describe how it can be extended to quantify the error in arbitrary marginal or conditional distributions of interest.
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Affiliation(s)
- Josh Fass
- Tri-Institutional PhD Program in Computational Biology & Medicine, New York, NY 10065, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - David A. Sivak
- Department of Physics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | | | | | - Benedict Leimkuhler
- School of Mathematics and Maxwell Institute of Mathematical Sciences, James Clerk Maxwell Building, Kings Buildings, University of Edinburgh, Edinburgh EH9 3FD, UK
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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34
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35
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Radak BK, Chipot C, Suh D, Jo S, Jiang W, Phillips JC, Schulten K, Roux B. Constant-pH Molecular Dynamics Simulations for Large Biomolecular Systems. J Chem Theory Comput 2017; 13:5933-5944. [PMID: 29111720 DOI: 10.1021/acs.jctc.7b00875] [Citation(s) in RCA: 123] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
An increasingly important endeavor is to develop computational strategies that enable molecular dynamics (MD) simulations of biomolecular systems with spontaneous changes in protonation states under conditions of constant pH. The present work describes our efforts to implement the powerful constant-pH MD simulation method, based on a hybrid nonequilibrium MD/Monte Carlo (neMD/MC) technique within the highly scalable program NAMD. The constant-pH hybrid neMD/MC method has several appealing features; it samples the correct semigrand canonical ensemble rigorously, the computational cost increases linearly with the number of titratable sites, and it is applicable to explicit solvent simulations. The present implementation of the constant-pH hybrid neMD/MC in NAMD is designed to handle a wide range of biomolecular systems with no constraints on the choice of force field. Furthermore, the sampling efficiency can be adaptively improved on-the-fly by adjusting algorithmic parameters during the simulation. Illustrative examples emphasizing medium- and large-scale applications on next-generation supercomputing architectures are provided.
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Affiliation(s)
- Brian K Radak
- Leadership Computing Facility, Argonne National Laboratory , Argonne, Illinois 60439-8643, United States
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche No. 7565, Université de Lorraine, Université de Lorraine , B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France.,Department of Physics, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801-2325, United States
| | - Donghyuk Suh
- Department of Chemistry, University of Chicago , Chicago, Illinois 60637-1454, United States
| | - Sunhwan Jo
- Leadership Computing Facility, Argonne National Laboratory , Argonne, Illinois 60439-8643, United States
| | - Wei Jiang
- Leadership Computing Facility, Argonne National Laboratory , Argonne, Illinois 60439-8643, United States
| | - James C Phillips
- Theoretical and Computational Biophysics Group, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801-2325, United States
| | - Klaus Schulten
- Department of Physics, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801-2325, United States.,Theoretical and Computational Biophysics Group, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801-2325, United States
| | - Benoît Roux
- Department of Chemistry, University of Chicago , Chicago, Illinois 60637-1454, United States.,Department of Biochemistry and Molecular Biology, University of Chicago , Chicago, Illinois 60637-1454, United States.,Center for Nanoscale Materials, Argonne National Laboratory , Argonne, Illinois 60439-8643, United States
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36
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Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang LP, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 2017; 13:e1005659. [PMID: 28746339 PMCID: PMC5549999 DOI: 10.1371/journal.pcbi.1005659] [Citation(s) in RCA: 1364] [Impact Index Per Article: 194.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 08/09/2017] [Accepted: 06/27/2017] [Indexed: 01/22/2023] Open
Abstract
OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, California, United States of America
| | - Jason Swails
- Department of Chemistry and Chemical Biology and BioMaPS Institute, Rutgers University, Piscataway, New Jersey, United States of America
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Robert T McGibbon
- Department of Chemistry, Stanford University, Stanford, California, United States of America
| | - Yutong Zhao
- Department of Chemistry, Stanford University, Stanford, California, United States of America
| | - Kyle A Beauchamp
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Lee-Ping Wang
- Department of Chemistry, University of California, Davis, Davis, California, United States of America
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Matthew P Harrigan
- Department of Chemistry, Stanford University, Stanford, California, United States of America
| | - Chaya D Stern
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.,Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Rafal P Wiewiora
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.,Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Vijay S Pande
- Department of Chemistry, Stanford University, Stanford, California, United States of America.,Department of Computer Science, Stanford University, Stanford, California, United States of America
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37
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Graham MM, Storkey AJ. Asymptotically exact inference in differentiable generative models. Electron J Stat 2017. [DOI: 10.1214/17-ejs1340si] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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