1
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Hwang W, Austin SL, Blondel A, Boittier ED, Boresch S, Buck M, Buckner J, Caflisch A, Chang HT, Cheng X, Choi YK, Chu JW, Crowley MF, Cui Q, Damjanovic A, Deng Y, Devereux M, Ding X, Feig MF, Gao J, Glowacki DR, Gonzales JE, Hamaneh MB, Harder ED, Hayes RL, Huang J, Huang Y, Hudson PS, Im W, Islam SM, Jiang W, Jones MR, Käser S, Kearns FL, Kern NR, Klauda JB, Lazaridis T, Lee J, Lemkul JA, Liu X, Luo Y, MacKerell AD, Major DT, Meuwly M, Nam K, Nilsson L, Ovchinnikov V, Paci E, Park S, Pastor RW, Pittman AR, Post CB, Prasad S, Pu J, Qi Y, Rathinavelan T, Roe DR, Roux B, Rowley CN, Shen J, Simmonett AC, Sodt AJ, Töpfer K, Upadhyay M, van der Vaart A, Vazquez-Salazar LI, Venable RM, Warrensford LC, Woodcock HL, Wu Y, Brooks CL, Brooks BR, Karplus M. CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed. J Phys Chem B 2024; 128:9976-10042. [PMID: 39303207 PMCID: PMC11492285 DOI: 10.1021/acs.jpcb.4c04100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/15/2024] [Accepted: 08/22/2024] [Indexed: 09/22/2024]
Abstract
Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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Affiliation(s)
- Wonmuk Hwang
- Department
of Biomedical Engineering, Texas A&M
University, College
Station, Texas 77843, United States
- Department
of Materials Science and Engineering, Texas
A&M University, College Station, Texas 77843, United States
- Department
of Physics and Astronomy, Texas A&M
University, College Station, Texas 77843, United States
- Center for
AI and Natural Sciences, Korea Institute
for Advanced Study, Seoul 02455, Republic
of Korea
| | - Steven L. Austin
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Arnaud Blondel
- Institut
Pasteur, Université Paris Cité, CNRS UMR3825, Structural
Bioinformatics Unit, 28 rue du Dr. Roux F-75015 Paris, France
| | - Eric D. Boittier
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Stefan Boresch
- Faculty of
Chemistry, Department of Computational Biological Chemistry, University of Vienna, Wahringerstrasse 17, 1090 Vienna, Austria
| | - Matthias Buck
- Department
of Physiology and Biophysics, Case Western
Reserve University, School of Medicine, Cleveland, Ohio 44106, United States
| | - Joshua Buckner
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Amedeo Caflisch
- Department
of Biochemistry, University of Zürich, CH-8057 Zürich, Switzerland
| | - Hao-Ting Chang
- Institute
of Bioinformatics and Systems Biology, National
Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
| | - Xi Cheng
- Shanghai
Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yeol Kyo Choi
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jhih-Wei Chu
- Institute
of Bioinformatics and Systems Biology, Department of Biological Science
and Technology, Institute of Molecular Medicine and Bioengineering,
and Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung
University, Hsinchu 30010, Taiwan,
ROC
| | - Michael F. Crowley
- Renewable
Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Qiang Cui
- Department
of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department
of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department
of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
| | - Ana Damjanovic
- Department
of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department
of Physics and Astronomy, Johns Hopkins
University, Baltimore, Maryland 21218, United States
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Yuqing Deng
- Shanghai
R&D Center, DP Technology, Ltd., Shanghai 201210, China
| | - Mike Devereux
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Xinqiang Ding
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Michael F. Feig
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
| | - Jiali Gao
- School
of Chemical Biology & Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- Institute
of Systems and Physical Biology, Shenzhen
Bay Laboratory, Shenzhen, Guangdong 518055, China
- Department
of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - David R. Glowacki
- CiTIUS
Centro Singular de Investigación en Tecnoloxías Intelixentes
da USC, 15705 Santiago de Compostela, Spain
| | - James E. Gonzales
- Department
of Biomedical Engineering, Texas A&M
University, College
Station, Texas 77843, United States
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Mehdi Bagerhi Hamaneh
- Department
of Physiology and Biophysics, Case Western
Reserve University, School of Medicine, Cleveland, Ohio 44106, United States
| | | | - Ryan L. Hayes
- Department
of Chemical and Biomolecular Engineering, University of California, Irvine, Irvine, California 92697, United States
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
| | - Jing Huang
- Key Laboratory
of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Yandong Huang
- College
of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Phillip S. Hudson
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
- Medicine
Design, Pfizer Inc., Cambridge, Massachusetts 02139, United States
| | - Wonpil Im
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Shahidul M. Islam
- Department
of Chemistry, Delaware State University, Dover, Delaware 19901, United States
| | - Wei Jiang
- Computational
Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - Michael R. Jones
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Silvan Käser
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Fiona L. Kearns
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Nathan R. Kern
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jeffery B. Klauda
- Department
of Chemical and Biomolecular Engineering, Institute for Physical Science
and Technology, Biophysics Program, University
of Maryland, College Park, Maryland 20742, United States
| | - Themis Lazaridis
- Department
of Chemistry, City College of New York, New York, New York 10031, United States
| | - Jinhyuk Lee
- Disease
Target Structure Research Center, Korea
Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
- Department
of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology, Daejeon 34141, Republic of Korea
| | - Justin A. Lemkul
- Department
of Biochemistry, Virginia Polytechnic Institute
and State University, Blacksburg, Virginia 24061, United States
| | - Xiaorong Liu
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Yun Luo
- Department
of Biotechnology and Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, California 91766, United States
| | - Alexander D. MacKerell
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Dan T. Major
- Department
of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
| | - Markus Meuwly
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
- Department
of Chemistry, Brown University, Providence, Rhode Island 02912, United States
| | - Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Lennart Nilsson
- Karolinska
Institutet, Department of Biosciences and
Nutrition, SE-14183 Huddinge, Sweden
| | - Victor Ovchinnikov
- Harvard
University, Department of Chemistry
and Chemical Biology, Cambridge, Massachusetts 02138, United States
| | - Emanuele Paci
- Dipartimento
di Fisica e Astronomia, Universitá
di Bologna, Bologna 40127, Italy
| | - Soohyung Park
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Richard W. Pastor
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Amanda R. Pittman
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Carol Beth Post
- Borch Department
of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Samarjeet Prasad
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Jingzhi Pu
- Department
of Chemistry and Chemical Biology, Indiana
University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Yifei Qi
- School
of Pharmacy, Fudan University, Shanghai 201203, China
| | | | - Daniel R. Roe
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Benoit Roux
- Department
of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | | | - Jana Shen
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Andrew C. Simmonett
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Alexander J. Sodt
- Eunice
Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Kai Töpfer
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Meenu Upadhyay
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Arjan van der Vaart
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | | | - Richard M. Venable
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Luke C. Warrensford
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - H. Lee Woodcock
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Yujin Wu
- 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
| | - Bernard R. Brooks
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Martin Karplus
- Harvard
University, Department of Chemistry
and Chemical Biology, Cambridge, Massachusetts 02138, United States
- Laboratoire
de Chimie Biophysique, ISIS, Université
de Strasbourg, 67000 Strasbourg, France
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2
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Aho N, Groenhof G, Buslaev P. Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path? J Chem Theory Comput 2024. [PMID: 39039621 DOI: 10.1021/acs.jctc.4c00134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.
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Affiliation(s)
- Noora Aho
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014 Jyväskylä, Finland
- Theoretical Physics and Center for Biophysics, Saarland University, 66123 Saarbrücken, Germany
| | - Gerrit Groenhof
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Pavel Buslaev
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014 Jyväskylä, Finland
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3
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Borgmans S, Rogge SMJ, Vanduyfhuys L, Van Speybroeck V. OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking. J Chem Theory Comput 2023; 19:9032-9048. [PMID: 38084847 PMCID: PMC10753773 DOI: 10.1021/acs.jctc.3c01028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/27/2023]
Abstract
While free energy surfaces are the crux of our understanding of many chemical and biological processes, their accuracy is generally unknown. Moreover, many developments to improve their accuracy are often complicated, limiting their general use. Luckily, several tools and guidelines are already in place to identify these shortcomings, but they are typically lacking in flexibility or fail to systematically determine how to improve the accuracy of the free energy calculation. To overcome these limitations, this work introduces OGRe, a Python package for optimal grid refinement in an arbitrary number of dimensions. OGRe is based on three metrics that gauge the confinement, consistency, and overlap of each simulation in a series of umbrella sampling (US) simulations, an enhanced sampling technique ubiquitously adopted to construct free energy surfaces for hindered processes. As these three metrics are fundamentally linked to the accuracy of the weighted histogram analysis method adopted to generate free energy surfaces from US simulations, they facilitate the systematic construction of accurate free energy profiles, where each metric is driven by a specific umbrella parameter. This allows for the derivation of a consistent and optimal collection of umbrellas for each simulation, largely independent of the initial values, thereby dramatically increasing the ease-of-use toward accurate free energy surfaces. As such, OGRe is particularly suited to determine complex free energy surfaces with large activation barriers and shallow minima, which underpin many physical and chemical transformations and hence to further our fundamental understanding of these processes.
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Affiliation(s)
- Sander Borgmans
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, 9052 Zwijnaarde, Belgium
| | - Sven M. J. Rogge
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, 9052 Zwijnaarde, Belgium
| | - Louis Vanduyfhuys
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, 9052 Zwijnaarde, Belgium
| | - Veronique Van Speybroeck
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, 9052 Zwijnaarde, Belgium
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4
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Bajpai S, Petkov BK, Tong M, Abreu CRA, Nair NN, Tuckerman ME. An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package. J Comput Chem 2023; 44:2166-2183. [PMID: 37464902 DOI: 10.1002/jcc.27182] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 07/20/2023]
Abstract
Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. In this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.
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Affiliation(s)
- Shitanshu Bajpai
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
| | - Brian K Petkov
- Department of Chemistry, New York University (NYU), New York, New York, USA
| | - Muchen Tong
- Department of Chemistry, New York University (NYU), New York, New York, USA
| | - Charlles R A Abreu
- Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
| | - Mark E Tuckerman
- Department of Chemistry, New York University (NYU), New York, New York, USA
- Courant Institute of Mathematical Sciences, New York University (NYU), New York, New York, USA
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China
- Simons Center for Computational Physical Chemistry, New York University, New York, New York, USA
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5
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Ganesan V, Priya MH. Probing the Conformational Preference to β-Strand during Peptide Self-Assembly. J Phys Chem B 2023. [PMID: 37364023 DOI: 10.1021/acs.jpcb.3c02327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Alanine-rich tetrapeptides like A3K dominantly exist as polyproline II helices in dilute aqueous solutions. However, during self-assembly, based on the free energy calculation in implicit solvent for various peptide conformations, only the peptides in the β-strand conformation can be packed closely. This necessitates the conformational transition to the β-strand commonly observed during peptide self-assembly such as in amyloid fibril formation. In fact, the closest interpeptide distance of 4.8 Å is consistent with the interstrand distance determined from the X-ray diffraction pattern of many amyloid fibrils. The position of free energy minimum obtained from implicit solvent calculation matches exactly with the explicit solvent simulation through umbrella sampling when the peptide conformations are restrained, demonstrating the applicability of the former for rapid screening of peptide configurations favorable for self-assembly. The barrier in the free energy profile in the presence of water arises out of the entropic restriction on the interstitial water molecules while satisfying the hydrogen bonding of both the peptides by forming water mediated hydrogen bond bridge. Further, the high energy barrier observed for the β-strand suggests that peptides initially tend to self-assemble in the polyproline II structure to mitigate the desolvation energy cost; the transition to the β-strand would happen only in the later stage after crossing the barrier. The umbrella sampling simulations with peptides allowed to change conformations, relative to each other, confirm the dynamic conformational transition during the course of the self-assembly supporting the "dock and lock" mechanism suggested for amyloid fibrillar growth.
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Affiliation(s)
- Vidhya Ganesan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, India
| | - M Hamsa Priya
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, India
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6
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Jou IA, Yoo AS, Dionne EV, Brady JW. Potential of mean force conformational energy maps for disaccharide linkages of the Burkholderia multivorans exopolysaccharide C1576 in aqueous solution. Carbohydr Res 2023; 524:108741. [PMID: 36716692 PMCID: PMC9974804 DOI: 10.1016/j.carres.2023.108741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/30/2022] [Accepted: 01/09/2023] [Indexed: 01/19/2023]
Abstract
Potential of Mean Force Ramachandran energy maps in aqueous solution have been prepared for all of the glycosidic linkages found in the C1576 exopolysaccharide from the biofilms of the bacterial species Burkholderia multivorans, a member of the Burkholderia cepacian complex that was isolated from a cystic fibrosis patient. C1576 is a rhamnomannan with a tetrasaccharide repeat unit. In general, for the four linkage types in this polymer, hydration did not produce dramatic changes in the Ramachandran energy surfaces, with the 3-methyl-α-d-rhamnopyranose-(1→3)-α-d-rhamnopyranose case exhibiting the greatest hydration change, with the global minimum energy conformation shifting by more than 80° in ψ. However, hydration did reduce the rigidity of all the linkages, increasing the overall flexibility of this polysaccharide.
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Affiliation(s)
- Ining A Jou
- Department of Food Science, Cornell University, Ithaca, NY, 14853, USA
| | - Andrew S Yoo
- Department of Food Science, Cornell University, Ithaca, NY, 14853, USA
| | - Elyssa V Dionne
- Department of Food Science, Cornell University, Ithaca, NY, 14853, USA
| | - John W Brady
- Department of Food Science, Cornell University, Ithaca, NY, 14853, USA.
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7
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Enhancing sampling with free-energy calculations. Curr Opin Struct Biol 2022; 77:102497. [PMID: 36410221 DOI: 10.1016/j.sbi.2022.102497] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/19/2022]
Abstract
In recent years, considerable progress has been made to enhance sampling and help address biological questions, including, but not limited to conformational transitions in biomolecules and protein-ligand reversible binding, hitherto intractable by brute-force computer simulations. Many of these advances result from the development of a palette of methods aimed at exploring rare events through reliable free-energy calculations. The advent of new, often conceptually related methods has also rendered difficult the choice of the best suited option for a given problem. Here, we focus on geometrical transformations and algorithms designed to enhance sampling along adequately chosen progress variables, tracing their theoretical foundations, and showing how they are connected and can be blended together for improved performance.
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8
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Roy R, Poddar S, Kar P. Comparison of the conformational dynamics of an N-glycan in implicit and explicit solvents. Carbohydr Res 2022; 522:108700. [DOI: 10.1016/j.carres.2022.108700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 11/28/2022]
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9
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Giese TJ, Zeng J, York DM. Multireference Generalization of the Weighted Thermodynamic Perturbation Method. J Phys Chem A 2022; 126:8519-8533. [PMID: 36301936 PMCID: PMC9771595 DOI: 10.1021/acs.jpca.2c06201] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We describe the generalized weighted thermodynamic perturbation (gwTP) method for estimating the free energy surface of an expensive "high-level" potential energy function from the umbrella sampling performed with multiple inexpensive "low-level" reference potentials. The gwTP method is a generalization of the weighted thermodynamic perturbation (wTP) method developed by Li and co-workers [J. Chem. Theory Comput. 2018, 14, 5583-5596] that uses a single "low-level" reference potential. The gwTP method offers new possibilities in model design whereby the sampling generated from several low-level potentials may be combined (e.g., specific reaction parameter models that might have variable accuracy at different stages of a multistep reaction). The gwTP method is especially well suited for use with machine learning potentials (MLPs) that are trained against computationally expensive ab initio quantum mechanical/molecular mechanical (QM/MM) energies and forces using active learning procedures that naturally produce multiple distinct neural network potentials. Simulations can be performed with greater sampling using the fast MLPs and then corrected to the ab initio level using gwTP. The capabilities of the gwTP method are demonstrated by creating reference potentials based on the MNDO/d and DFTB2/MIO semiempirical models supplemented with the "range-corrected deep potential" (DPRc). The DPRc parameters are trained to ab initio QM/MM data, and the potentials are used to calculate the free energy surface of stepwise mechanisms for nonenzymatic RNA 2'-O-transesterification model reactions. The extended sampling made possible by the reference potentials allows one to identify unequilibrated portions of the simulations that are not always evident from the short time scale commonly used with ab initio QM/MM potentials. We show that the reference potential approach can yield more accurate ab initio free energy predictions than the wTP method or what can be reasonably afforded from explicit ab initio QM/MM sampling.
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Affiliation(s)
- Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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10
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Ahmad K, Rizzi A, Capelli R, Mandelli D, Lyu W, Carloni P. Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective. Front Mol Biosci 2022; 9:899805. [PMID: 35755817 PMCID: PMC9216551 DOI: 10.3389/fmolb.2022.899805] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/09/2022] [Indexed: 12/12/2022] Open
Abstract
The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.
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Affiliation(s)
- Katya Ahmad
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
| | - Andrea Rizzi
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
- Atomistic Simulations, Istituto Italiano di Tecnologia, Genova, Italy
| | - Riccardo Capelli
- Department of Applied Science and Technology (DISAT), Politecnico di Torino, Torino, Italy
| | - Davide Mandelli
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
| | - Wenping Lyu
- Warshel Institute for Computational Biology, School of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, China
| | - Paolo Carloni
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, Jülich, Germany
- Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich, Jülich, Germany
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11
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Lu H, Martí J. Predicting the conformational variability of oncogenic GTP-bound G12D mutated KRas-4B proteins at zwitterionic model cell membranes. NANOSCALE 2022; 14:3148-3158. [PMID: 35142321 DOI: 10.1039/d1nr07622a] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
KRas proteins are the largest family of mutated Ras isoforms, participating in a wide variety of cancers. Due to their importance, large effort is being carried out on drug development by small-molecule inhibitors. However, understanding protein conformational variability remains a challenge in drug discovery. In the case of the Ras family, their multiple conformational states can affect the binding of potential drug inhibitors. To overcome this challenge, we propose a computational framework based on combined all-atom Molecular Dynamics and Metadynamics simulations in order to accurately access conformational variants of the target protein. We tested the methodology using a G12D mutated GTP bound oncogenic KRas-4B protein located at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. Two main orientations of KRas-4B at the anionic membrane have been determined. The corresponding torsional angles are taken as reliable reaction coordinates so that free-energy landscapes are obtained by well-tempered metadynamics simulations, revealing local and global minima of the free-energy hypersurface and unveiling reactive paths of the system between the two preferential orientations. We have observed that GTP-binding to KRas-4B has huge influence on the stabilisation of the protein and it can potentially help to open Switch I/II druggable pockets, lowering energy barriers between stable states and resulting in cumulative conformers of KRas-4B. This may highlight new opportunities for targeting the unique meta-stable states through the design of new efficient drugs.
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Affiliation(s)
- Huixia Lu
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, China.
| | - Jordi Martí
- Department of Physics, Polytechnical University of Catalonia-Barcelona Tech, B5-209 Northern Campus, Jordi Girona 1-3, 08034 Barcelona, Catalonia, Spain.
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12
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Masand VH, Sk MF, Kar P, Rastija V, Zaki MEA. Identification of Food Compounds as inhibitors of SARS-CoV-2 main protease using molecular docking and molecular dynamics simulations. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2021; 217:104394. [PMID: 34312571 PMCID: PMC8295492 DOI: 10.1016/j.chemolab.2021.104394] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/22/2021] [Accepted: 07/18/2021] [Indexed: 05/27/2023]
Abstract
SARS-CoV-2 has rapidly emerged as a global pandemic with high infection rate. At present, there is no drug available for this deadly disease. Recently, Mpro (Main Protease) enzyme has been identified as essential proteins for the survival of this virus. In the present work, Lipinski's rules and molecular docking have been performed to identify plausible inhibitors of Mpro using food compounds. For virtual screening, a database of food compounds was downloaded and then filtered using Lipinski's rule of five. Then, molecular docking was accomplished to identify hits using Mpro protein as the target enzyme. This led to identification of a Spermidine derivative as a hit. In the next step, Spermidine derivatives were collected from PubMed and screened for their binding with Mpro protein. In addition, molecular dynamic simulations (200 ns) were executed to get additional information. Some of the compounds are found to have strong affinity for Mpro, therefore these hits could be used to develop a therapeutic agent for SARS-CoV-2.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, 444 602, India
| | - Md Fulbabu Sk
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Khandwa Road, MP, 453552, India
| | - Parimal Kar
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Khandwa Road, MP, 453552, India
| | - Vesna Rastija
- Department of Agroecology and Environmental Protection, Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
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13
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Smith Z, Tiwary P. Making High-Dimensional Molecular Distribution Functions Tractable through Belief Propagation on Factor Graphs. J Phys Chem B 2021; 125:11150-11158. [PMID: 34586819 DOI: 10.1021/acs.jpcb.1c05717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics (MD) simulations provide a wealth of high-dimensional data at all-atom and femtosecond resolution but deciphering mechanistic information from this data is an ongoing challenge in physical chemistry and biophysics. Theoretically speaking, joint probabilities of the equilibrium distribution contain all thermodynamic information, but they prove increasingly difficult to compute and interpret as the dimensionality increases. Here, inspired by tools in probabilistic graphical modeling, we develop a factor graph trained through belief propagation that helps factorize the joint probability into an approximate tractable form that can be easily visualized and used. We validate the study through the analysis of the conformational dynamics of two small peptides with five and nine residues. Our validations include testing the conditional dependency predictions through an intervention scheme inspired by Judea Pearl. Second, we directly use the belief propagation-based approximate probability distribution as a high-dimensional static bias for enhanced sampling, where we achieve spontaneous back-and-forth motion between metastable states that is up to 350 times faster than unbiased MD. We believe this work opens up useful ways to thinking about and dealing with high-dimensional molecular simulations.
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Affiliation(s)
- Zachary Smith
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
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14
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Pal A, Pal S, Verma S, Shiga M, Nair NN. Mean force based temperature accelerated sliced sampling: Efficient reconstruction of high dimensional free energy landscapes. J Comput Chem 2021; 42:1996-2003. [DOI: 10.1002/jcc.26727] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/28/2021] [Accepted: 07/21/2021] [Indexed: 12/14/2022]
Affiliation(s)
- Asit Pal
- Department of Chemistry Indian Institute of Technology Kanpur Kanpur India
| | - Subhendu Pal
- Department of Chemistry Indian Institute of Technology Kanpur Kanpur India
| | - Shivani Verma
- Department of Chemistry Indian Institute of Technology Kanpur Kanpur India
| | - Motoyuki Shiga
- Center for Computational Science and E‐Systems Japan Atomic Energy Agency Chiba Japan
| | - Nisanth N. Nair
- Department of Chemistry Indian Institute of Technology Kanpur Kanpur India
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15
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Giese TJ, Ekesan Ş, York DM. Extension of the Variational Free Energy Profile and Multistate Bennett Acceptance Ratio Methods for High-Dimensional Potential of Mean Force Profile Analysis. J Phys Chem A 2021; 125:4216-4232. [PMID: 33784093 DOI: 10.1021/acs.jpca.1c00736] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
We redevelop the variational free energy profile (vFEP) method using a cardinal B-spline basis to extend the method for analyzing free energy surfaces (FESs) involving three or more reaction coordinates. We also implemented software for evaluating high-dimensional profiles based on the multistate Bennett acceptance ratio (MBAR) method which constructs an unbiased probability density from global reweighting of the observed samples. The MBAR method takes advantage of a fast algorithm for solving the unbinned weighted histogram (UWHAM)/MBAR equations which replaces the solution of simultaneous equations with a nonlinear optimization of a convex function. We make use of cardinal B-splines and multiquadric radial basis functions to obtain smooth, differentiable MBAR profiles in arbitrary high dimensions. The cardinal B-spline vFEP and MBAR methods are compared using three example systems that examine 1D, 2D, and 3D profiles. Both methods are found to be useful and produce nearly indistinguishable results. The vFEP method is found to be 150 times faster than MBAR when applied to periodic 2D profiles, but the MBAR method is 4.5 times faster than vFEP when evaluating unbounded 3D profiles. In agreement with previous comparisons, we find the vFEP method produces superior FESs when the overlap between umbrella window simulations decreases. Finally, the associative reaction mechanism of hammerhead ribozyme is characterized using 3D, 4D, and 6D profiles, and the higher-dimensional profiles are found to have smaller reaction barriers by as much as 1.5 kcal/mol. The methods presented here have been implemented into the FE-ToolKit software package along with new methods for network-wide free energy analysis in drug discovery.
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Affiliation(s)
- Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| | - Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
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16
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17
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Martí J, Lu H. Microscopic Interactions of Melatonin, Serotonin and Tryptophan with Zwitterionic Phospholipid Membranes. Int J Mol Sci 2021; 22:2842. [PMID: 33799606 PMCID: PMC8001758 DOI: 10.3390/ijms22062842] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/15/2022] Open
Abstract
The interactions at the atomic level between small molecules and the main components of cellular plasma membranes are crucial for elucidating the mechanisms allowing for the entrance of such small species inside the cell. We have performed molecular dynamics and metadynamics simulations of tryptophan, serotonin, and melatonin at the interface of zwitterionic phospholipid bilayers. In this work, we will review recent computer simulation developments and report microscopic properties, such as the area per lipid and thickness of the membranes, atomic radial distribution functions, angular orientations, and free energy landscapes of small molecule binding to the membrane. Cholesterol affects the behaviour of the small molecules, which are mainly buried in the interfacial regions. We have observed a competition between the binding of small molecules to phospholipids and cholesterol through lipidic hydrogen-bonds. Free energy barriers that are associated to translational and orientational changes of melatonin have been found to be between 10-20 kJ/mol for distances of 1 nm between melatonin and the center of the membrane. Corresponding barriers for tryptophan and serotonin that are obtained from reversible work methods are of the order of 10 kJ/mol and reveal strong hydrogen bonding between such species and specific phospholipid sites. The diffusion of tryptophan and melatonin is of the order of 10-7 cm2/s for the cholesterol-free and cholesterol-rich setups.
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Affiliation(s)
- Jordi Martí
- Department of Physics, Technical University of Catalonia-Barcelona Tech, 08034 Barcelona, Spain
| | - Huixia Lu
- School of Pharmacy, Shanghai Jiaotong University, Shanghai 200240, China;
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18
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Lu H, Martí J. Long-lasting Salt Bridges Provide the Anchoring Mechanism of Oncogenic Kirsten Rat Sarcoma Proteins at Cell Membranes. J Phys Chem Lett 2020; 11:9938-9945. [PMID: 33170712 DOI: 10.1021/acs.jpclett.0c02809] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
RAS proteins work as GDP-GTP binary switches and regulate cytoplasmic signaling networks that are able to control several cellular processes, playing an essential role in signal transduction pathways involved in cell growth, differentiation, and survival, so that overacting RAS signaling can lead to cancer. One of the hardest challenges to face is the design of mutation-selective therapeutic strategies. In this work, a G12D-mutated farnesylated GTP-bound Kirsten RAt sarcoma (KRAS) protein has been simulated at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. A specific long-lasting salt bridge connection between farnesyl and the hypervariable region of the protein has been identified as the main mechanism responsible for the binding of oncogenic farnesylated KRAS-4B to the cell membrane. Free-energy landscapes allowed us to characterize local and global minima of KRAS-4B binding to the cell membrane, revealing the main pathways between anchored and released states.
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Affiliation(s)
- Huixia Lu
- Department of Physics, Technical University of Catalonia-Barcelona Tech, B4-B5 Northern Campus, Barcelona, Catalonia, Spain
| | - Jordi Martí
- Department of Physics, Technical University of Catalonia-Barcelona Tech, B4-B5 Northern Campus, Barcelona, Catalonia, Spain
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19
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Wang W. Physisorbed State Regulates the Dissociation Mechanism of H 2O on Ni(100). J Phys Chem A 2020; 124:8724-8732. [PMID: 33045831 DOI: 10.1021/acs.jpca.0c06130] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Water dissociation is a key step in many industrial catalytic processes. The dissociation of H2O on a rigid Ni(100) surface was investigated by the quantum instanton method with a full-dimensional potential energy surface. The calculated free-energy barrier maps showed that the free-energy barrier varied dramatically with the surface site. The free-energy well map demonstrated that the physisorption well of H2O was existent at all of the surface sites, and H2O could be dissociated by both the direct and steady-state processes. The calculated direct dissociation rate constants at different surface sites decreased rapidly in the order transition state (TS) > bridge > top > hollow. The steady-state dissociation rate constants had the same trend as that of the direct process but the steady-state dissociation rate constant at the top site became the largest at high temperatures. The direct dissociation rate constants were always larger than those of the steady-state process at a given temperature. The calculated kinetic isotope effects for the direct and steady-state processes were extremely large at low temperatures, which was caused by the zero-point energy correction and remarkable quantum tunneling. From low temperature to high temperature, H2O would undergo stable molecular adsorption at the top site, steady-state dissociation at the TS site, direct rupture at the TS site, and direct decomposition at the impact site.
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Affiliation(s)
- Wenji Wang
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, Xianyang 712100, Shaanxi Province, P. R. China
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20
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Kasahara K, Terazawa H, Itaya H, Goto S, Nakamura H, Takahashi T, Higo J. myPresto/omegagene 2020: a molecular dynamics simulation engine for virtual-system coupled sampling. Biophys Physicobiol 2020; 17:140-146. [PMID: 33240741 PMCID: PMC7671739 DOI: 10.2142/biophysico.bsj-2020013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/10/2020] [Indexed: 12/03/2022] Open
Abstract
The molecular dynamics (MD) method is a promising approach for investigating the molecular mechanisms of microscopic phenomena. In particular, generalized ensemble MD methods can efficiently explore the conformational space with a rugged free-energy surface. However, the implementation and acquisition of technical knowledge for each generalized ensemble MD method are not straightforward for end-users. Here, we present a new version of the myPresto/omegagene software, which is an MD simulation engine tailored for a series of generalized ensemble methods, which are virtual-system coupled multicanonical MD (V-McMD), virtual-system coupled adaptive umbrella sampling (V-AUS), and virtual-system coupled canonical MD (VcMD). This program has been applied in several studies analyzing free-energy landscapes of a variety of molecular systems with all-atom simulations. The updated version provides new functionality for coarse-grained simulations powered by the hydrophobicity scale method. The software package includes a step-by-step tutorial document for enhanced conformational sampling of the poly-glutamine (poly-Q) oligomer expressed as a one-bead per residue model. The myPresto/omegagene software is freely available at the following URL: https://github.com/kotakasahara/omegagene under the Apache2 license.
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Affiliation(s)
- Kota Kasahara
- College of Life Sciences, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
| | - Hiroki Terazawa
- Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
| | - Hayato Itaya
- Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
| | - Satoshi Goto
- Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Takuya Takahashi
- College of Life Sciences, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
| | - Junichi Higo
- Graduate School of Simulation Studies, University of Hyogo, Kobe, Hyogo 650-0047, Japan
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21
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Li C, Swanson JMJ. Understanding and Tracking the Excess Proton in Ab Initio Simulations; Insights from IR Spectra. J Phys Chem B 2020; 124:5696-5708. [PMID: 32515957 PMCID: PMC7448536 DOI: 10.1021/acs.jpcb.0c03615] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Proton transport in aqueous media is ubiquitously important in chemical and biological processes. Although ab initio molecular dynamics (AIMD) simulations have made great progress in characterizing proton transport, there has been a long-standing challenge in defining and tracking the excess proton, or more properly, the center of excess charge (CEC) created when a hydrogen nucleus distorts the electron distributions of water molecules in a delocalized and highly dynamic nature. Yet, defining (and biasing) such a CEC is essential when combining AIMD with enhanced sampling methods to calculate the relevant macroscopic properties via free-energy landscapes, which is the standard practice for most processes of interest. Several CEC formulas have been proposed and used, but none have yet been systematically tested or rigorously derived. In this paper, we show that the CEC can be used as a computational tool to disentangle IR features of the solvated excess proton from its surrounding solvent, and in turn, how correlating the features in the excess charge spectrum with the behavior of CEC in simulations enables a systematic evaluation of various CEC definitions. We present a new definition of CEC and show how it overcomes the limitations of those currently available both from a spectroscopic point of view and from a practical perspective of performance in enhanced sampling simulations.
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Affiliation(s)
- Chenghan Li
- Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, United States
| | - Jessica M. J. Swanson
- Department of Chemistry, Biological Chemistry Program, and Center for Cell and Genome Science, The University of Utah, Salt Lake City, Utah 84112, United States
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22
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Wang DD, Zhu M, Yan H. Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions. Brief Bioinform 2020; 22:5860693. [PMID: 32591817 DOI: 10.1093/bib/bbaa107] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/20/2020] [Accepted: 05/05/2020] [Indexed: 12/18/2022] Open
Abstract
Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.
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Affiliation(s)
- Debby D Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology
| | - Mengxu Zhu
- Department of Electrical Engineering, City University of Hong Kong
| | - Hong Yan
- College of Science and Engineering, City University of Hong Kong
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23
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Cellular absorption of small molecules: free energy landscapes of melatonin binding at phospholipid membranes. Sci Rep 2020; 10:9235. [PMID: 32513935 PMCID: PMC7280225 DOI: 10.1038/s41598-020-65753-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 05/05/2020] [Indexed: 12/28/2022] Open
Abstract
Free energy calculations are essential to unveil mechanisms at the atomic scale such as binding of small solutes and their translocation across cell membranes, eventually producing cellular absorption. Melatonin regulates biological rhythms and is directly related to carcinogenesis and neurodegenerative disorders. Free energy landscapes obtained from well-tempered metadynamics simulations precisely describe the characteristics of melatonin binding to specific sites in the membrane and reveal the role of cholesterol in free energy barrier crossing. A specific molecular torsional angle and the distance between melatonin and the center of the membrane along the normal to the membrane Z-axis have been considered as suitable reaction coordinates. Free energy barriers between two particular orientations of the molecular structure (folded and extended) have been found to be of about 18 kJ/mol for z-distances of about 1–2 nm. The ability of cholesterol to expel melatonin out of the internal regions of the membrane towards the interface and the external solvent is explained from a free energy perspective. The calculations reported here offer detailed free energy landscapes of melatonin embedded in model cell membranes and reveal microscopic information on its transition between free energy minima, including the location of relevant transition states, and provide clues on the role of cholesterol in the cellular absorption of small molecules.
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24
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Miao M, Fu H, Zhang H, Shao X, Chipot C, Cai W. Avoiding non-equilibrium effects in adaptive biasing force calculations. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1775222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Mengyao Miao
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, People’s Republic of China
| | - Haohao Fu
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, People’s Republic of China
| | - Hong Zhang
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, People’s Republic of China
| | - Xueguang Shao
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, People’s Republic of China
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, People’s Republic of China
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana−Champaign, Vandœuvre-lès-Nancy cedex, France
- Department of Physics, University of Illinois at Urbana−Champaign, 1110 West Green Street, Urbana, IL 61801, USA
| | - Wensheng Cai
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, People’s Republic of China
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25
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Hahn DF, Zarotiadis RA, Hünenberger PH. The Conveyor Belt Umbrella Sampling (CBUS) Scheme: Principle and Application to the Calculation of the Absolute Binding Free Energies of Alkali Cations to Crown Ethers. J Chem Theory Comput 2020; 16:2474-2493. [DOI: 10.1021/acs.jctc.9b00998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- David F. Hahn
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Rhiannon A. Zarotiadis
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Philippe H. Hünenberger
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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26
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Liao Q. Enhanced sampling and free energy calculations for protein simulations. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 170:177-213. [PMID: 32145945 DOI: 10.1016/bs.pmbts.2020.01.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.
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Affiliation(s)
- Qinghua Liao
- Science for Life Laboratory, Department of Chemistry-BMC, Uppsala University, Uppsala, Sweden.
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27
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Limongelli V. Ligand binding free energy and kinetics calculation in 2020. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1455] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science – Center for Computational Medicine in Cardiology Università della Svizzera italiana (USI) Lugano Switzerland
- Department of Pharmacy University of Naples “Federico II” Naples Italy
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28
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Noé F. Machine Learning for Molecular Dynamics on Long Timescales. MACHINE LEARNING MEETS QUANTUM PHYSICS 2020. [DOI: 10.1007/978-3-030-40245-7_16] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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29
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Okamoto Y. Protein structure predictions by enhanced conformational sampling methods. Biophys Physicobiol 2019; 16:344-366. [PMID: 31984190 PMCID: PMC6976031 DOI: 10.2142/biophysico.16.0_344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 08/07/2019] [Indexed: 12/01/2022] Open
Abstract
In this Special Festschrift Issue for the celebration of Professor Nobuhiro Gō's 80th birthday, we review enhanced conformational sampling methods for protein structure predictions. We present several generalized-ensemble algorithms such as multicanonical algorithm, replica-exchange method, etc. and parallel Monte Carlo or molecular dynamics method with genetic crossover. Examples of the results of these methods applied to the predictions of protein tertiary structures are also presented.
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Affiliation(s)
- Yuko Okamoto
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
- Structural Biology Research Center, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
- Center for Computational Science, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
- Information Technology Center, Nagoya University, Nagoya, Aichi 464-8601, Japan
- JST-CREST, Nagoya, Aichi 464-8602, Japan
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Hayami T, Higo J, Nakamura H, Kasahara K. Multidimensional virtual-system coupled canonical molecular dynamics to compute free-energy landscapes of peptide multimer assembly. J Comput Chem 2019; 40:2453-2463. [PMID: 31282023 DOI: 10.1002/jcc.26020] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 05/26/2019] [Accepted: 06/17/2019] [Indexed: 11/05/2022]
Abstract
An enhanced-sampling method termed multidimensional virtual-system coupled canonical molecular dynamics (mD-VcMD) method is developed. In many cases, generalized-ensemble methods realizing enhanced sampling, for example, adaptive umbrella sampling, apply an effective potential, which is derived from temporarily assumed canonical distribution as a function of one or more arbitrarily defined reaction coordinates. However, it is not straightforward to estimate the appropriate canonical distribution, especially for cases applying multiple reaction coordinates. The current method, mD-VcMD, does not rely on the form of the canonical distribution. Therefore, it is practically useful to explore a high-dimensional reaction-coordinate space. In this article, formulation of mD-VcMD and its evaluation with the simple molecular models consisting of three or four alanine peptides are presented. We confirmed that mD-VcMD efficiently searched 2D and 3D reaction-coordinate spaces defined as interpeptide distances. Direct comparisons with results of long-term canonical MD simulations revealed that mD-VcMD produces correct canonical ensembles. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Tomonori Hayami
- Institute for Protein Research, Osaka University, 3-2 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Junichi Higo
- Institute for Protein Research, Osaka University, 3-2 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, 3-2 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan
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31
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Yeh IC, Lenhart JL, Orlicki JA, Rinderspacher BC. Molecular Dynamics Simulation Study of Adsorption of Bioinspired Oligomers on Alumina Surfaces. J Phys Chem B 2019; 123:7024-7035. [PMID: 31313924 DOI: 10.1021/acs.jpcb.9b04473] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The adsorption of small oligomers on a model metal oxide surface was studied with atomistically detailed molecular dynamics simulations. The oligomers consisted of two different repeat units: a maleimide, which contains a catechol functional group as in the dopamine residue found in marine adhesive proteins, and a methyl acrylate. A hydroxylated alumina surface was used as the model metal oxide surface. Adsorption interactions were investigated in aqueous as well as anhydrous conditions. In anhydrous conditions, the model oligomers displayed strong adsorption interactions with the surface. However, in aqueous conditions, the adsorption interactions were significantly weakened because of the competition with the water molecules for adsorption sites near the surface. Catechol functional groups in the model oligomers were found to play an important role in adsorption interactions with the alumina surface via hydrogen bonds. However, diverse adsorption properties were observed depending on compositions and sequences of two different repeat units and self-aggregations, indicating that the hydrogen bonding capability of catechol groups is not the sole factor determining adsorption properties.
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Affiliation(s)
- In-Chul Yeh
- Polymers Branch, Materials & Manufacturing Science Division , U.S. Army Research Laboratory , Aberdeen Proving Ground , Maryland 21005 , United States
| | - Joseph L Lenhart
- Polymers Branch, Materials & Manufacturing Science Division , U.S. Army Research Laboratory , Aberdeen Proving Ground , Maryland 21005 , United States
| | - Joshua A Orlicki
- Polymers Branch, Materials & Manufacturing Science Division , U.S. Army Research Laboratory , Aberdeen Proving Ground , Maryland 21005 , United States
| | - B Christopher Rinderspacher
- Polymers Branch, Materials & Manufacturing Science Division , U.S. Army Research Laboratory , Aberdeen Proving Ground , Maryland 21005 , United States
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32
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Hahn DF, Milić JV, Hünenberger PH. Vase
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Kite
Equilibrium of Resorcin[4]arene Cavitands Investigated Using Molecular Dynamics Simulations with Ball‐and‐Stick Local Elevation Umbrella Sampling. Helv Chim Acta 2019. [DOI: 10.1002/hlca.201900060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- David F. Hahn
- Laboratory of Physical Chemistry, Department of Chemistry and Applied BiosciencesETH Zürich Vladimir-Prelog-Weg 2 CH-8093 Zürich Switzerland
| | - Jovana V. Milić
- Laboratory of Photonics and InterfacesÉcole Polytechnique Fédérale de Lausanne, EPFL SB ISIC LPI, Station 6 CH-1015 Lausanne Switzerland
| | - Philippe H. Hünenberger
- Laboratory of Physical Chemistry, Department of Chemistry and Applied BiosciencesETH Zürich Vladimir-Prelog-Weg 2 CH-8093 Zürich Switzerland
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33
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Boubeta FM, Contestín García RM, Lorenzo EN, Boechi L, Estrin D, Sued M, Arrar M. Lessons learned about steered molecular dynamics simulations and free energy calculations. Chem Biol Drug Des 2019; 93:1129-1138. [PMID: 30793836 DOI: 10.1111/cbdd.13485] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 12/18/2018] [Accepted: 12/22/2018] [Indexed: 01/30/2023]
Abstract
The calculation of free energy profiles is central in understanding differential enzymatic activity, for instance, involving chemical reactions that require QM-MM tools, ligand migration, and conformational rearrangements that can be modeled using classical potentials. The use of steered molecular dynamics (sMD) together with the Jarzynski equality is a popular approach in calculating free energy profiles. Here, we first briefly review the application of the Jarzynski equality to sMD simulations, then revisit the so-called stiff-spring approximation and the consequent expectation of Gaussian work distributions and, finally, reiterate the practical utility of the second-order cumulant expansion, as it coincides with the parametric maximum-likelihood estimator in this scenario. We illustrate this procedure using simulations of CO, both in aqueous solution and in a carbon nanotube as a model system for biologically relevant nanoheterogeneous environments. We conclude the use of the second-order cumulant expansion permits the use of faster pulling velocities in sMD simulations, without introducing bias due to large dispersion in the non-equilibrium work distribution.
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Affiliation(s)
- Fernando Martín Boubeta
- CONICET-Facultad de Ciencias Exactas y Naturales, Instituto de Química-Física de los Materiales, Medio Ambiente y Energía, Universidad de Buenos Aires, Buenos Aires, Argentina.,Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Rocío María Contestín García
- CONICET-Facultad de Ciencias Exactas y Naturales, Instituto de Química-Física de los Materiales, Medio Ambiente y Energía, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Ezequiel Norberto Lorenzo
- CONICET-Facultad de Ciencias Exactas y Naturales, Instituto de Química-Física de los Materiales, Medio Ambiente y Energía, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Leonardo Boechi
- CONICET-Facultad de Ciencias Exactas y Naturales, Instituto de Cálculo, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Dario Estrin
- CONICET-Facultad de Ciencias Exactas y Naturales, Instituto de Química-Física de los Materiales, Medio Ambiente y Energía, Universidad de Buenos Aires, Buenos Aires, Argentina.,Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mariela Sued
- CONICET-Facultad de Ciencias Exactas y Naturales, Instituto de Cálculo, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mehrnoosh Arrar
- CONICET-Facultad de Ciencias Exactas y Naturales, Instituto de Química-Física de los Materiales, Medio Ambiente y Energía, Universidad de Buenos Aires, Buenos Aires, Argentina.,Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
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Abstract
Our current knowledge on the unique roles of RNA in cells makes it vital to investigate the properties of RNA systems using computational methods because of the potential pharmaceutical applications. With the continuous advancement of computer technology, it is now possible to study RNA folding. Molecular mechanics calculations are useful in discovering the structural and thermodynamic properties of RNA systems. Yet, the predictions depend on the quality of the RNA force field, which is a set of parameters describing the potential energy of the system. Torsional parameters are one of the terms in a force field that can be revised using physics-based approaches. This chapter focuses on improvements provided by revisions of torsional parameters of the AMBER (Assisted Model Building with Energy Refinement) RNA force field. The theory behind torsional revisions and re-parameterization of several RNA torsions is briefly described. Applications of the revised torsional parameters to study RNA nucleosides, single-stranded RNA tetramers, and RNA repeat expansions are described in detail. It is concluded that RNA force fields require constant revisions and should be benchmarked against diverse RNA systems such as single strands and internal loops in order to test their qualities.
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Lamim Ribeiro JM, Tiwary P. Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE. J Chem Theory Comput 2018; 15:708-719. [PMID: 30525598 DOI: 10.1021/acs.jctc.8b00869] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
In this work, we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 2018, 149, 072301) for investigating ligand-protein unbinding mechanisms and calculating absolute binding free energies, Δ Gb, when plagued with difficult to sample rare events. In order to do so, we introduce a simple but powerful extension to RAVE that allows learning a reaction coordinate expressed as a piecewise function that is linear over all intervals. Such an approach allows us to retain the physical interpretation of a RAVE-derived reaction coordinate while making the method more applicable to a wider range of complex biophysical problems. As we will demonstrate, using as our test-case the slow dissociation of benzene from the L99A variant of lysozyme, the RAVE extension led to observing an unbinding event in 100% of the independent all-atom MD simulations, all within 3-50 ns for a process that takes on an average close to few hundred milliseconds, which reflects a 7 orders of magnitude acceleration relative to straightforward MD. Furthermore, we will show that without the use of time-dependent biasing, clear back-and-forth movement between metastable intermediates was achieved during the various simulations, demonstrating the caliber of the RAVE-derived piecewise reaction coordinate and bias potential, which together drive efficient and accurate sampling of the ligand-protein dissociation event. Last, we report the results for Δ Gb, which via very short MD simulations, can form a strict lower-bound that is ∼2-3 kcal/mol off from experiments. We believe that RAVE, together with its multidimensional extension that we introduce here, will be a useful tool for simulating the slow unbinding process of practical ligand-protein complexes in an automated manner with minimal use of human intuition.
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Affiliation(s)
- João Marcelo Lamim Ribeiro
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology , University of Maryland , Maryland , College Park 20742 , United States
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology , University of Maryland , Maryland , College Park 20742 , United States
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36
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Chen W, Ferguson AL. Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration. J Comput Chem 2018; 39:2079-2102. [PMID: 30368832 DOI: 10.1002/jcc.25520] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 06/14/2018] [Indexed: 01/08/2023]
Abstract
Macromolecular and biomolecular folding landscapes typically contain high free energy barriers that impede efficient sampling of configurational space by standard molecular dynamics simulation. Biased sampling can artificially drive the simulation along prespecified collective variables (CVs), but success depends critically on the availability of good CVs associated with the important collective dynamical motions. Nonlinear machine learning techniques can identify such CVs but typically do not furnish an explicit relationship with the atomic coordinates necessary to perform biased sampling. In this work, we employ auto-associative artificial neural networks ("autoencoders") to learn nonlinear CVs that are explicit and differentiable functions of the atomic coordinates. Our approach offers substantial speedups in exploration of configurational space, and is distinguished from existing approaches by its capacity to simultaneously discover and directly accelerate along data-driven CVs. We demonstrate the approach in simulations of alanine dipeptide and Trp-cage, and have developed an open-source and freely available implementation within OpenMM. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Wei Chen
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois, 61801
| | - Andrew L Ferguson
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois, 61801.,Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, 1304 W Green Street, Urbana, Illinois, 61801.,Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, Illinois, 61801
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37
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Kasson PM, Jha S. Adaptive ensemble simulations of biomolecules. Curr Opin Struct Biol 2018; 52:87-94. [PMID: 30265901 DOI: 10.1016/j.sbi.2018.09.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/06/2018] [Accepted: 09/11/2018] [Indexed: 12/23/2022]
Abstract
Recent advances in both theory and computational power have created opportunities to simulate biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble simulations are now widely used to compute a number of individual simulation trajectories and analyze statistics across them. Adaptive ensemble simulations offer a further level of sophistication and flexibility by enabling high-level algorithms to control simulations-based on intermediate results. We review some of the adaptive ensemble algorithms and software infrastructure currently in use and outline where the complexities of implementing adaptive simulation have limited algorithmic innovation to date. We describe an adaptive ensemble API to overcome some of these barriers and more flexibly and simply express adaptive simulation algorithms to help realize the power of this type of simulation.
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Affiliation(s)
- Peter M Kasson
- Departments of Molecular Physiology and of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, United States; Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala 75146, Sweden.
| | - Shantenu Jha
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, United States; Center for Data-Driven Discovery, Brookhaven National Laboratory, Upton, NY 11793, United States.
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38
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Higo J, Kasahara K, Nakamura H. Multi-dimensional virtual system introduced to enhance canonical sampling. J Chem Phys 2018; 147:134102. [PMID: 28987097 DOI: 10.1063/1.4986129] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
When an important process of a molecular system occurs via a combination of two or more rare events, which occur almost independently to one another, computational sampling for the important process is difficult. Here, to sample such a process effectively, we developed a new method, named the "multi-dimensional Virtual-system coupled Monte Carlo (multi-dimensional-VcMC)" method, where the system interacts with a virtual system expressed by two or more virtual coordinates. Each virtual coordinate controls sampling along a reaction coordinate. By setting multiple reaction coordinates to be related to the corresponding rare events, sampling of the important process can be enhanced. An advantage of multi-dimensional-VcMC is its simplicity: Namely, the conformation moves widely in the multi-dimensional reaction coordinate space without knowledge of canonical distribution functions of the system. To examine the effectiveness of the algorithm, we introduced a toy model where two molecules (receptor and its ligand) bind and unbind to each other. The receptor has a deep binding pocket, to which the ligand enters for binding. Furthermore, a gate is set at the entrance of the pocket, and the gate is usually closed. Thus, the molecular binding takes place via the two events: ligand approach to the pocket and gate opening. In two-dimensional (2D)-VcMC, the two molecules exhibited repeated binding and unbinding, and an equilibrated distribution was obtained as expected. A conventional canonical simulation, which was 200 times longer than 2D-VcMC, failed in sampling the binding/unbinding effectively. The current method is applicable to various biological systems.
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Affiliation(s)
- Junichi Higo
- Institute for Protein Research, Osaka University, 3-2 Yamada-oka, Suita, Osaka 565-0871, Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, 3-2 Yamada-oka, Suita, Osaka 565-0871, Japan
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39
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Sidky H, Whitmer JK. Learning free energy landscapes using artificial neural networks. J Chem Phys 2018; 148:104111. [DOI: 10.1063/1.5018708] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Hythem Sidky
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Jonathan K. Whitmer
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA
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40
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Hayami T, Kasahara K, Nakamura H, Higo J. Molecular dynamics coupled with a virtual system for effective conformational sampling. J Comput Chem 2018; 39:1291-1299. [PMID: 29464736 DOI: 10.1002/jcc.25196] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 11/12/2022]
Abstract
An enhanced conformational sampling method is proposed: virtual-system coupled canonical molecular dynamics (VcMD). Although VcMD enhances sampling along a reaction coordinate, this method is free from estimation of a canonical distribution function along the reaction coordinate. This method introduces a virtual system that does not necessarily obey a physical law. To enhance sampling the virtual system couples with a molecular system to be studied. Resultant snapshots produce a canonical ensemble. This method was applied to a system consisting of two short peptides in an explicit solvent. Conventional molecular dynamics simulation, which is ten times longer than VcMD, was performed along with adaptive umbrella sampling. Free-energy landscapes computed from the three simulations mutually converged well. The VcMD provided quicker association/dissociation motions of peptides than the conventional molecular dynamics did. The VcMD method is applicable to various complicated systems because of its methodological simplicity. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Tomonori Hayami
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Junichi Higo
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
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41
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Abstract
The minimum energy pathway contains important information describing the transition between two states on a potential energy surface (PES). Chain-of-states methods were developed to efficiently calculate minimum energy pathways connecting two stable states. In the chain-of-states framework, a series of structures are generated and optimized to represent the minimum energy pathway connecting two states. However, multiple pathways may exist connecting two existing states and should be identified to obtain a full view of the transitions. Therefore, we developed an enhanced sampling method, named as the direct pathway dynamics sampling (DPDS) method, to facilitate exploration of a PES for multiple pathways connecting two stable states as well as addition minima and their associated transition pathways. In the DPDS method, molecular dynamics simulations are carried out on the targeting PES within a chain-of-states framework to directly sample the transition pathway space. The simulations of DPDS could be regulated by two parameters controlling distance among states along the pathway and smoothness of the pathway. One advantage of the chain-of-states framework is that no specific reaction coordinates are necessary to generate the reaction pathway, because such information is implicitly represented by the structures along the pathway. The chain-of-states setup in a DPDS method greatly enhances the sufficient sampling in high-energy space between two end states, such as transition states. By removing the constraint on the end states of the pathway, DPDS will also sample pathways connecting minima on a PES in addition to the end points of the starting pathway. This feature makes DPDS an ideal method to directly explore transition pathway space. Three examples demonstrate the efficiency of DPDS methods in sampling the high-energy area important for reactions on the PES.
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Affiliation(s)
- Hongyu Zhou
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University , Dallas, Texas 75275, United States of America
| | - Peng Tao
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University , Dallas, Texas 75275, United States of America
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42
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Zhang C, Drake JA, Ma J, Pettitt BM. Optimal updating magnitude in adaptive flat-distribution sampling. J Chem Phys 2017; 147:174105. [PMID: 29117700 PMCID: PMC5669982 DOI: 10.1063/1.5008618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 10/08/2017] [Indexed: 11/14/2022] Open
Abstract
We present a study on the optimization of the updating magnitude for a class of free energy methods based on flat-distribution sampling, including the Wang-Landau (WL) algorithm and metadynamics. These methods rely on adaptive construction of a bias potential that offsets the potential of mean force by histogram-based updates. The convergence of the bias potential can be improved by decreasing the updating magnitude with an optimal schedule. We show that while the asymptotically optimal schedule for the single-bin updating scheme (commonly used in the WL algorithm) is given by the known inverse-time formula, that for the Gaussian updating scheme (commonly used in metadynamics) is often more complex. We further show that the single-bin updating scheme is optimal for very long simulations, and it can be generalized to a class of bandpass updating schemes that are similarly optimal. These bandpass updating schemes target only a few long-range distribution modes and their optimal schedule is also given by the inverse-time formula. Constructed from orthogonal polynomials, the bandpass updating schemes generalize the WL and Langfeld-Lucini-Rago algorithms as an automatic parameter tuning scheme for umbrella sampling.
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Affiliation(s)
- Cheng Zhang
- Sealy Center for Structural Biology and Molecular Biophysics, The University of Texas Medical Branch, Galveston, Texas 77555-0304, USA
| | - Justin A Drake
- Sealy Center for Structural Biology and Molecular Biophysics, The University of Texas Medical Branch, Galveston, Texas 77555-0304, USA
| | - Jianpeng Ma
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - B Montgomery Pettitt
- Sealy Center for Structural Biology and Molecular Biophysics, The University of Texas Medical Branch, Galveston, Texas 77555-0304, USA
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43
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Zhang BW, Deng N, Tan Z, Levy RM. Stratified UWHAM and Its Stochastic Approximation for Multicanonical Simulations Which Are Far from Equilibrium. J Chem Theory Comput 2017; 13:4660-4674. [PMID: 28902500 PMCID: PMC5897113 DOI: 10.1021/acs.jctc.7b00651] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We describe a new analysis tool called Stratified unbinned Weighted Histogram Analysis Method (Stratified-UWHAM), which can be used to compute free energies and expectations from a multicanonical ensemble when a subset of the parallel simulations is far from being equilibrated because of barriers between free energy basins which are only rarely (or never) crossed at some states. The Stratified-UWHAM equations can be obtained in the form of UWHAM equations but with an expanded set of states. We also provide a stochastic solver, Stratified RE-SWHAM, for Stratified-UWHAM to remove its computational bottleneck. Stratified-UWHAM and Stratified RE-SWHAM are applied to study three test topics: the free energy landscape of alanine dipeptide, the binding affinity of a host-guest binding complex, and path sampling for a two-dimensional double well potential. The examples show that when some of the parallel simulations are only locally equilibrated, the estimates of free energies and equilibrium distributions provided by the conventional UWHAM (or MBAR) solutions exhibit considerable biases, but the estimates provided by Stratified-UWHAM and Stratified RE-SWHAM agree with the benchmark very well. Lastly, we discuss features of the Stratified-UWHAM approach which is based on coarse-graining in relation to two other maximum likelihood-based methods which were proposed recently, that also coarse-grain the multicanonical data.
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Khanjari N, Eslami H, Müller-Plathe F. Adaptive-numerical-bias metadynamics. J Comput Chem 2017; 38:2721-2729. [DOI: 10.1002/jcc.25066] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 08/07/2017] [Accepted: 09/01/2017] [Indexed: 12/18/2022]
Affiliation(s)
- Neda Khanjari
- Department of Chemistry; College of Sciences, Persian Gulf University; Boushehr 75168 Iran
| | - Hossein Eslami
- Department of Chemistry; College of Sciences, Persian Gulf University; Boushehr 75168 Iran
| | - Florian Müller-Plathe
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Str. 8; Darmstadt 64287 Germany
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Xu Y, Cheng S, Sussman JL, Silman I, Jiang H. Computational Studies on Acetylcholinesterases. Molecules 2017; 22:molecules22081324. [PMID: 28796192 PMCID: PMC6152020 DOI: 10.3390/molecules22081324] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 08/07/2017] [Accepted: 08/07/2017] [Indexed: 01/18/2023] Open
Abstract
Functions of biomolecules, in particular enzymes, are usually modulated by structural fluctuations. This is especially the case in a gated diffusion-controlled reaction catalyzed by an enzyme such as acetylcholinesterase. The catalytic triad of acetylcholinesterase is located at the bottom of a long and narrow gorge, but it catalyzes the extremely rapid hydrolysis of the neurotransmitter, acetylcholine, with a reaction rate close to the diffusion-controlled limit. Computational modeling and simulation have produced considerable advances in exploring the dynamical and conformational properties of biomolecules, not only aiding in interpreting the experimental data, but also providing insights into the internal motions of the biomolecule at the atomic level. Given the remarkably high catalytic efficiency and the importance of acetylcholinesterase in drug development, great efforts have been made to understand the dynamics associated with its functions by use of various computational methods. Here, we present a comprehensive overview of recent computational studies on acetylcholinesterase, expanding our views of the enzyme from a microstate of a single structure to conformational ensembles, strengthening our understanding of the integration of structure, dynamics and function associated with the enzyme, and promoting the structure-based and/or mechanism-based design of new inhibitors for it.
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Affiliation(s)
- Yechun Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai 201203, China.
| | - Shanmei Cheng
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai 201203, China.
| | - Joel L Sussman
- Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot 76100, Israel.
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Israel Silman
- Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Hualiang Jiang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai 201203, China.
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Wang W, Zhao Y. The dissociation and recombination rates of CH 4 through the Ni(111) surface: The effect of lattice motion. J Chem Phys 2017; 147:044703. [PMID: 28764359 DOI: 10.1063/1.4995299] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Methane dissociation is a prototypical system for the study of surface reaction dynamics. The dissociation and recombination rates of CH4 through the Ni(111) surface are calculated by using the quantum instanton method with an analytical potential energy surface. The Ni(111) lattice is treated rigidly, classically, and quantum mechanically so as to reveal the effect of lattice motion. The results demonstrate that it is the lateral displacements rather than the upward and downward movements of the surface nickel atoms that affect the rates a lot. Compared with the rigid lattice, the classical relaxation of the lattice can increase the rates by lowering the free energy barriers. For instance, at 300 K, the dissociation and recombination rates with the classical lattice exceed the ones with the rigid lattice by 6 and 10 orders of magnitude, respectively. Compared with the classical lattice, the quantum delocalization rather than the zero-point energy of the Ni atoms further enhances the rates by widening the reaction path. For instance, the dissociation rate with the quantum lattice is about 10 times larger than that with the classical lattice at 300 K. On the rigid lattice, due to the zero-point energy difference between CH4 and CD4, the kinetic isotope effects are larger than 1 for the dissociation process, while they are smaller than 1 for the recombination process. The increasing kinetic isotope effect with decreasing temperature demonstrates that the quantum tunneling effect is remarkable for the dissociation process.
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Affiliation(s)
- Wenji Wang
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, 712100 Shaanxi Province, People's Republic of China
| | - Yi Zhao
- State Key Laboratory for Physical Chemistry of Solid Surfaces and Fujian Provincial Key Lab of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China
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Bekker GJ, Kamiya N, Araki M, Fukuda I, Okuno Y, Nakamura H. Accurate Prediction of Complex Structure and Affinity for a Flexible Protein Receptor and Its Inhibitor. J Chem Theory Comput 2017; 13:2389-2399. [DOI: 10.1021/acs.jctc.6b01127] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Gert-Jan Bekker
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Narutoshi Kamiya
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Simulation Studies, University of Hyogo, 7-1-28 Minatojima
Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Mitsugu Araki
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Medicine, Kyoto University, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Ikuo Fukuda
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yasushi Okuno
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Medicine, Kyoto University, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Haruki Nakamura
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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Karandashev K, Vaníček J. Accelerating equilibrium isotope effect calculations. I. Stochastic thermodynamic integration with respect to mass. J Chem Phys 2017. [DOI: 10.1063/1.4981260] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Konstantin Karandashev
- Laboratory of Theoretical Physical Chemistry, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Jiří Vaníček
- Laboratory of Theoretical Physical Chemistry, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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Ferguson AL. BayesWHAM: A Bayesian approach for free energy estimation, reweighting, and uncertainty quantification in the weighted histogram analysis method. J Comput Chem 2017; 38:1583-1605. [PMID: 28475830 DOI: 10.1002/jcc.24800] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 11/27/2016] [Accepted: 03/19/2017] [Indexed: 01/18/2023]
Abstract
The weighted histogram analysis method (WHAM) is a powerful approach to estimate molecular free energy surfaces (FES) from biased simulation data. Bayesian reformulations of WHAM are valuable in proving statistically optimal use of the data and providing a transparent means to incorporate regularizing priors and estimate statistical uncertainties. In this work, we develop a fully Bayesian treatment of WHAM to generate statistically optimal FES estimates in any number of biasing dimensions under arbitrary choices of the Bayes prior. Rigorous uncertainty estimates are generated by Metropolis-Hastings sampling from the Bayes posterior. We also report a means to project the FES and its uncertainties into arbitrary auxiliary order parameters beyond those in which biased sampling was conducted. We demonstrate the approaches in applications of alanine dipeptide and the unthreading of a synthetic mimic of the astexin-3 lasso peptide. Open-source MATLAB and Python implementations of our codes are available for free public download. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Andrew L Ferguson
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801.,Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
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Abstract
Millisecond-scale conformational transitions represent a seminal challenge for traditional molecular dynamics simulations, even with the help of high-end supercomputer architectures. Such events are particularly relevant to the study of molecular motors-proteins or abiological constructs that convert chemical energy into mechanical work. Here, we present a hybrid-simulation scheme combining an array of methods including elastic network models, transition path sampling, and advanced free-energy methods, possibly in conjunction with generalized-ensemble schemes to deliver a viable option for capturing the millisecond-scale motor steps of biological motors. The methodology is already implemented in large measure in popular molecular dynamics programs, and it can leverage the massively parallel capabilities of petascale computers. The applicability of the hybrid method is demonstrated with two examples, namely cyclodextrin-based motors and V-type ATPases.
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Affiliation(s)
- Abhishek Singharoy
- Theoretical and Computational Biophysics Group, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, Illinois 61801
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7565, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois 61801
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