1
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Wang J, Miao Y. Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides. J Chem Theory Comput 2024; 20:5829-5841. [PMID: 39002136 DOI: 10.1021/acs.jctc.4c00502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2024]
Abstract
Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.
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Affiliation(s)
- Jinan Wang
- Computational Medicine Program and Department of Pharmacology, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Yinglong Miao
- Computational Medicine Program and Department of Pharmacology, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, United States
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2
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Wang J, Miao Y. Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592668. [PMID: 38766067 PMCID: PMC11100592 DOI: 10.1101/2024.05.06.592668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.
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Affiliation(s)
- Jinan Wang
- Computational Medicine Program and Department of Pharmacology, University of North Carolina – Chapel Hill, Chapel Hill, North Carolina, USA 27599
| | - Yinglong Miao
- Computational Medicine Program and Department of Pharmacology, University of North Carolina – Chapel Hill, Chapel Hill, North Carolina, USA 27599
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3
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Fu H, Chipot C, Shao X, Cai W. Standard Binding Free-Energy Calculations: How Far Are We from Automation? J Phys Chem B 2023; 127:10459-10468. [PMID: 37824848 DOI: 10.1021/acs.jpcb.3c04370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.
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Affiliation(s)
- Haohao Fu
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Christophe Chipot
- Laboratoire International Associé CNRS and University of Illinois at Urbana-Champaign, UMR no. 7019, Université de Lorraine, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
- Department of Chemistry, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Hawai'i at Ma̅noa, 2545 McCarthy Mall, Honolulu, Hawaii 96822, United States
| | - Xueguang Shao
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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4
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Conflitti P, Raniolo S, Limongelli V. Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness. J Chem Theory Comput 2023; 19:6047-6061. [PMID: 37656199 PMCID: PMC10536999 DOI: 10.1021/acs.jctc.3c00641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 09/02/2023]
Abstract
Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.
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Affiliation(s)
- Paolo Conflitti
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Stefano Raniolo
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Vittorio Limongelli
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
- Department
of Pharmacy, University of Naples “Federico
II”, 80131 Naples, Italy
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5
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Ray D, Parrinello M. Kinetics from Metadynamics: Principles, Applications, and Outlook. J Chem Theory Comput 2023; 19:5649-5670. [PMID: 37585703 DOI: 10.1021/acs.jctc.3c00660] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.
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Affiliation(s)
- Dhiman Ray
- Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Michele Parrinello
- Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
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6
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Wang J, Do HN, Koirala K, Miao Y. Predicting Biomolecular Binding Kinetics: A Review. J Chem Theory Comput 2023; 19:2135-2148. [PMID: 36989090 DOI: 10.1021/acs.jctc.2c01085] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Hung N Do
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Kushal Koirala
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
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7
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Dutta P, Sengupta N. Efficient Interrogation of the Kinetic Barriers Demarcating Catalytic States of a Tyrosine Kinase with Optimal Physical Descriptors and Mixture Models. Chemphyschem 2023; 24:e202200595. [PMID: 36394126 DOI: 10.1002/cphc.202200595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/16/2022] [Accepted: 11/16/2022] [Indexed: 11/18/2022]
Abstract
Computer simulations are increasingly used to access thermo-kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate-limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.
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Affiliation(s)
- Pallab Dutta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, 741246, India
| | - Neelanjana Sengupta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, 741246, India
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8
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Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket. J Chem Theory Comput 2023; 19:733-745. [PMID: 36706316 DOI: 10.1021/acs.jctc.2c01194] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.
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9
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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10
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Rydzewski J, Chen M, Ghosh TK, Valsson O. Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations. J Chem Theory Comput 2022; 18:7179-7192. [PMID: 36367826 DOI: 10.1021/acs.jctc.2c00873] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Enhanced sampling methods are indispensable in computational chemistry and physics, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space of dynamical systems due to the sampling problem. A class of such enhanced sampling methods works by identifying a few slow degrees of freedom, termed collective variables (CVs), and enhancing the sampling along these CVs. Selecting CVs to analyze and drive the sampling is not trivial and often relies on chemical intuition. Despite routinely circumventing this issue using manifold learning to estimate CVs directly from standard simulations, such methods cannot provide mappings to a low-dimensional manifold from enhanced sampling simulations, as the geometry and density of the learned manifold are biased. Here, we address this crucial issue and provide a general reweighting framework based on anisotropic diffusion maps for manifold learning that takes into account that the learning data set is sampled from a biased probability distribution. We consider manifold learning methods based on constructing a Markov chain describing transition probabilities between high-dimensional samples. We show that our framework reverts the biasing effect, yielding CVs that correctly describe the equilibrium density. This advancement enables the construction of low-dimensional CVs using manifold learning directly from the data generated by enhanced sampling simulations. We call our framework reweighted manifold learning. We show that it can be used in many manifold learning techniques on data from both standard and enhanced sampling simulations.
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Affiliation(s)
- Jakub Rydzewski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Toruń, Poland
| | - Ming Chen
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Tushar K Ghosh
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Omar Valsson
- Department of Chemistry, University of North Texas, Denton, Texas 76201, United States
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11
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Palacio-Rodriguez K, Vroylandt H, Stelzl LS, Pietrucci F, Hummer G, Cossio P. Transition Rates and Efficiency of Collective Variables from Time-Dependent Biased Simulations. J Phys Chem Lett 2022; 13:7490-7496. [PMID: 35939819 DOI: 10.1021/acs.jpclett.2c01807] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Simulations with adaptive time-dependent bias enable an efficient exploration of the conformational space of a system. However, the dynamic information is altered by the bias. Infrequent metadynamics recovers the transition rate of crossing a barrier, if the collective variables are ideal and there is no bias deposition near the transition state. Unfortunately, these conditions are not always fulfilled. To overcome these limitations, and inspired by single-molecule force spectroscopy, we use Kramers' theory for calculating the barrier-crossing rate when a time-dependent bias is added to the system. We assess the efficiency of collective variables parameter by measuring how efficiently the bias accelerates the transitions. We present approximate analytical expressions of the survival probability, reproducing the barrier-crossing time statistics and enabling the extraction of the unbiased transition rate even for challenging cases. We explore the limits of our method and provide convergence criteria to assess its validity.
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Affiliation(s)
- Karen Palacio-Rodriguez
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d'Histoire Naturelle, CNRS UMR 7590, 75005 Paris, France
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia, 050010 Medellín, Colombia
| | - Hadrien Vroylandt
- Institut des sciences du calcul et des données, Sorbonne Université, 75005 Paris, France
| | - Lukas S Stelzl
- Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany
- KOMET 1, Institute of Physics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
- Institute of Molecular Biology, 55128 Mainz, Germany
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
| | - Fabio Pietrucci
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d'Histoire Naturelle, CNRS UMR 7590, 75005 Paris, France
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
- Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Pilar Cossio
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia, 050010 Medellín, Colombia
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
- Center for Computational Mathematics, Flatiron Institute, 10010 New York, United States
- Center for Computational Biology, Flatiron Institute, 10010 New York, United States
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12
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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: 3] [Impact Index Per Article: 1.5] [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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13
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Elder RM, Saylor DM. Predicting Solute Diffusivity in Polymers Using Time-Temperature Superposition. J Phys Chem B 2022; 126:3768-3777. [PMID: 35583328 DOI: 10.1021/acs.jpcb.2c00057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We demonstrate a novel application of the time-temperature superposition (TTS) principle to predict solute diffusivity D in glassy polymers using atomistic molecular dynamics simulations. Our TTS approach incorporates the Debye-Waller factor ⟨u2⟩, a measure of solute caging, along with concepts from thermodynamic scaling methods, allowing us to balance contributions to the dynamics from temperature and ⟨u2⟩ using adjustable parameters. Our approach rescales the solute mean-squared displacement curves at several temperatures into a master curve that approximates the diffusive dynamics at a reference temperature, effectively extending the simulation time scale from nanoseconds to seconds and beyond. With a set of "universal" parameters, this TTS approach predicts D with reasonable accuracy in a broad range of polymer/solute systems. Using TTS greatly reduces the computational cost compared to standard MD simulations. Thus, our method offers a means to rapidly and routinely provide order-of-magnitude estimates of D using simulations.
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Affiliation(s)
- Robert M Elder
- Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20903, United States
| | - David M Saylor
- Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20903, United States
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14
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Russi M, Cavalieri G, Marson D, Laurini E, Pricl S. Binding of the B-Raf Inhibitors Dabrafenib and Vemurafenib to Human Serum Albumin: A Biophysical and Molecular Simulation Study. Mol Pharm 2022; 19:1619-1634. [PMID: 35436118 DOI: 10.1021/acs.molpharmaceut.2c00100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug binding to human serum albumin (HSA) significantly affects in vivo drug transport and biological activity. To gain insight into the binding mechanism of the two B-Raf tyrosine kinase inhibitors dabrafenib and vemurafenib to HSA, in this work, we adopted a combined strategy based on fluorescence spectroscopy, isothermal titration calorimetry (ITC), circular dichroism (CD), and molecular simulations. Both anticancer drugs are found to bind spontaneously and with a 1:1 stoichiometry within the same binding pocket, located in Sudlow's site II (subdomain IIIA) of the protein with comparable affinity and without substantially perturbing the protein secondary structure. However, the nature of each drug-protein interactions is distinct: whereas the formation of the dabrafenib/HSA complex is more entropically driven, the formation of the alternative vemurafenib/HSA assembly is prevalently enthalpic in nature. Kinetic analysis also indicates that the association rate is similar for the two drugs, whereas the residence time of vemurafenib within the HSA binding pocket is somewhat higher than that determined for the alternative B-Raf inhibitor.
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Affiliation(s)
- Maria Russi
- Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy
| | - Gabriele Cavalieri
- Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy
| | - Domenico Marson
- Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy
| | - Erik Laurini
- Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy
| | - Sabrina Pricl
- Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy.,Department of General Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, ul. Pomorska 141/143, 90-236 Łódź, Poland
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15
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Peña Ccoa WJ, Hocky GM. Assessing models of force-dependent unbinding rates via infrequent metadynamics. J Chem Phys 2022; 156:125102. [PMID: 35364872 PMCID: PMC8957391 DOI: 10.1063/5.0081078] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Protein–ligand interactions are crucial for a wide range of physiological processes. Many cellular functions result in these non-covalent “bonds” being mechanically strained, and this can be integral to proper cellular function. Broadly, two classes of force dependence have been observed—slip bonds, where the unbinding rate increases, and catch bonds, where the unbinding rate decreases. Despite much theoretical work, we cannot predict for which protein–ligand pairs, pulling coordinates, and forces a particular rate dependence will appear. Here, we assess the ability of MD simulations combined with enhanced sampling techniques to probe the force dependence of unbinding rates. We show that the infrequent metadynamics technique correctly produces both catch and slip bonding kinetics for model potentials. We then apply it to the well-studied case of a buckyball in a hydrophobic cavity, which appears to exhibit an ideal slip bond. Finally, we compute the force-dependent unbinding rate of biotin–streptavidin. Here, the complex nature of the unbinding process causes the infrequent metadynamics method to begin to break down due to the presence of unbinding intermediates, despite the use of a previously optimized sampling coordinate. Allowing for this limitation, a combination of kinetic and free energy computations predicts an overall slip bond for larger forces consistent with prior experimental results although there are substantial deviations at small forces that require further investigation. This work demonstrates the promise of predicting force-dependent unbinding rates using enhanced sampling MD techniques while also revealing the methodological barriers that must be overcome to tackle more complex targets in the future.
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Affiliation(s)
| | - Glen M. Hocky
- Department of Chemistry, New York University, New York, New York 10003, USA
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16
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Mokkath JH, Muhammed MM, Chamkha AJ. Free Energy Surfaces and Barriers for Vacancy Diffusion on Al(100), Al(110), Al(111) Reconstructed Surfaces. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 12:76. [PMID: 35010027 PMCID: PMC8746563 DOI: 10.3390/nano12010076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/16/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Metadynamics is a popular enhanced sampling method based on the recurrent application of a history-dependent adaptive bias potential that is a function of a selected number of appropriately chosen collective variables. In this work, using metadynamics simulations, we performed a computational study for the diffusion of vacancies on three different Al surfaces [reconstructed Al(100), Al(110), and Al(111) surfaces]. We explored the free energy landscape of diffusion and estimated the barriers associated with this process on each surface. It is found that the surfaces are unique regarding vacancy diffusion. More specically, the reconstructed Al(110) surface presents four metastable states on the free energy surface having sizable and connected passage-ways with an energy barrier of height 0.55 eV. On the other hand, the reconstructed Al(100)/Al(111) surfaces exhibit two/three metastable states, respectively, with an energy barrier of height 0.33 eV. The findings in this study can help to understand surface vacancy diffusion in technologically relevant Al surfaces.
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Affiliation(s)
- Junais Habeeb Mokkath
- Quantum Nanophotonics Simulations Lab, Department of Physics, Kuwait College of Science and Technology, Doha Area, 7th Ring Road, Kuwait City P.O. Box 27235, Kuwait
| | - Mufasila Mumthaz Muhammed
- School of Engineering & Computing, American International University, Saad Al Abdullah-East of Naseem, Block 3, Kuwait;
| | - Ali J. Chamkha
- Faculty of Engineering, Kuwait College of Science and Technology, Doha 35004, Kuwait;
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17
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Wang X. Conformational Fluctuations in GTP-Bound K-Ras: A Metadynamics Perspective with Harmonic Linear Discriminant Analysis. J Chem Inf Model 2021; 61:5212-5222. [PMID: 34570515 DOI: 10.1021/acs.jcim.1c00844] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Biomacromolecules often undergo significant conformational rearrangements during function. In proteins, these motions typically consist in nontrivial, concerted rearrangement of multiple flexible regions. Mechanistic, thermodynamics, and kinetic predictions can be obtained via molecular dynamics simulations, provided that the simulation time is at least comparable to the relevant time scale of the process of interest. Because of the substantial computational cost, however, plain MD simulations often have difficulty in obtaining sufficient statistics for converged estimates, requiring the use of more-advanced techniques. Central in many enhanced sampling methods is the definition of a small set of relevant degrees of freedom (collective variables) that are able to describe the transitions between different metastable states of the system. The harmonic linear discriminant analysis (HLDA) has been shown to be useful for constructing low-dimensional collective variables in various complex systems. Here, we apply HLDA to study the free-energy landscape of a monomeric protein around its native state. More precisely, we study the K-Ras protein bound to GTP, focusing on two flexible loops and on the region associated with oncogenic mutations. We perform microsecond-long biased simulations on the wild type and on G12C, G12D, G12 V mutants, describe the resulting free-energy landscapes, and compare our predictions with previous experimental and computational studies. The fast interconversion between open and closed macroscopic states and their similar thermodynamic stabilities are observed. The mutation-induced effects include the alternations of the relative stabilities of different conformational states and the introduction of many microscopic metastable states. Together, our results demonstrate the applicability of the HLDA-based protocol for the conformational sampling of multiple flexible regions in folded proteins.
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Affiliation(s)
- Xiaohui Wang
- Beijing National Laboratory for Molecular Sciences, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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18
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Kamenik AS, Singh I, Lak P, Balius TE, Liedl KR, Shoichet BK. Energy penalties enhance flexible receptor docking in a model cavity. Proc Natl Acad Sci U S A 2021; 118:e2106195118. [PMID: 34475217 PMCID: PMC8433570 DOI: 10.1073/pnas.2106195118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 07/27/2021] [Indexed: 11/18/2022] Open
Abstract
Protein flexibility remains a major challenge in library docking because of difficulties in sampling conformational ensembles with accurate probabilities. Here, we use the model cavity site of T4 lysozyme L99A to test flexible receptor docking with energy penalties from molecular dynamics (MD) simulations. Crystallography with larger and smaller ligands indicates that this cavity can adopt three major conformations: open, intermediate, and closed. Since smaller ligands typically bind better to the cavity site, we anticipate an energy penalty for the cavity opening. To estimate its magnitude, we calculate conformational preferences from MD simulations. We find that including a penalty term is essential for retrospective ligand enrichment; otherwise, high-energy states dominate the docking. We then prospectively docked a library of over 900,000 compounds for new molecules binding to each conformational state. Absent a penalty term, the open conformation dominated the docking results; inclusion of this term led to a balanced sampling of ligands against each state. High ranked molecules were experimentally tested by Tm upshift and X-ray crystallography. From 33 selected molecules, we identified 18 ligands and determined 13 crystal structures. Most interesting were those bound to the open cavity, where the buried site opens to bulk solvent. Here, highly unusual ligands for this cavity had been predicted, including large ligands with polar tails; these were confirmed both by binding and by crystallography. In docking, incorporating protein flexibility with thermodynamic weightings may thus access new ligand chemotypes. The MD approach to accessing and, crucially, weighting such alternative states may find general applicability.
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Affiliation(s)
- Anna S Kamenik
- Institute of General, Inorganic, and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, 6020 Innsbruck, Austria
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Isha Singh
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Parnian Lak
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Trent E Balius
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Klaus R Liedl
- Institute of General, Inorganic, and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, 6020 Innsbruck, Austria;
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
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19
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Nunes-Alves A, Kokh DB, Wade RC. Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from τRAMD simulations. Curr Res Struct Biol 2021; 3:106-111. [PMID: 34235490 PMCID: PMC8244441 DOI: 10.1016/j.crstbi.2021.04.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/20/2021] [Accepted: 04/25/2021] [Indexed: 01/14/2023] Open
Abstract
The protein-ligand residence time, τ, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict τ, computational methods must overcome the problem that τ often exceeds the timescales accessible to conventional molecular dynamics (MD) simulation. Here, we apply the τ-Random Acceleration Molecular Dynamics (τRAMD) method to a set of kinetically characterized complexes of T4 lysozyme mutants with small, engineered binding cavities. τRAMD yields relative ligand dissociation rates in good accordance with experiments across this diverse set of complexes that differ with regard to measurement temperature, ligand identity, protein mutation and binding cavity. τRAMD thereby allows a comprehensive characterization of the ligand egress routes and determinants of τ. Although ligand dissociation by multiple egress routes is observed, we find that egress via the predominant route determines the value of τ. We also find that the presence of a greater number of metastable states along egress pathways leads to slower protein-ligand dissociation. These physical insights could be exploited in the rational optimization of the kinetic properties of drug candidates. Relative residence times are computed for T4 lysozyme mutant-ligand complexes. τ-Random Acceleration Molecular Dynamics provide efficient sampling of unbinding. Computed dissociation rates show good agreement with all available measured values. Ligand egress via the predominant route determines the value of the residence time. The presence of metastable states along egress pathways slows down dissociation.
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Affiliation(s)
- Ariane Nunes-Alves
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120, Heidelberg, Germany
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120, Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany
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20
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Ludwig J, Smith J, Pfaendtner J. Analyzing the Long Time-Scale Dynamics of Uremic Toxins Bound to Sudlow Site II in Human Serum Albumin. J Phys Chem B 2021; 125:2910-2920. [PMID: 33715376 DOI: 10.1021/acs.jpcb.1c00221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein bound uremic toxins (PBUTs), a series of chemicals that remain a challenge for removal strategies used on patients suffering with chronic kidney disease, could be strong candidates for MD study in order to better understand the interactions and time scales associated with binding mode transitions. Currently, traditional dialysis methods cannot satisfactorily remove PBUTs from the bloodstream. This is at least partly due to these toxin's high level of affinity for protein binding sites, particularly the prominent human serum albumin (HSA) and two of its drug binding sites (Sudlow site I and II). We investigate the dynamics of binding site transitions and interactions by MD simulations targeting four well-known toxins: indoxyl sulfate (IS), p-cresyl sulfate (PCS), indole-3-acetic acid (IAA), and hippurate acid (HIP). Long-time scale dynamics are obtained by the use of time-structure independent component analysis (tICA) for dimensionality reduction followed by spectral analysis of a Markov state model (MSM) scored using the generalized matrix Rayleigh quotient (GMRQ). Our results add new insights to prior findings related to the key role of charge-pairing in governing toxin-protein interactions. We find that IAA, the bulkiest hydrophobic toxin studied, observes the slowest process of at least 3 times slower than the smaller, less hydrophobic toxins. In general, we find that the processes slower than 15 ns are correlated with a transition from dominantly hydrophobic interactions deep in the binding pocket to a gain in hydrogen bonding partners near the mouth of the pocket. Our results indicate that aromatic residues such as PHE play a part in a type of toxin stabilization akin to π-stacking. In conclusion, this work presents mechanistic descriptions of interactions/transitions for a set of important PBUTs that bind Sudlow site II on time scales relevant to the underlying binding kinetics of most interest.
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Affiliation(s)
- James Ludwig
- Department of Chemistry, University of Washington, Seattle, Washington 98195-1700, United States
| | - Josh Smith
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195-1750, United States
| | - Jim Pfaendtner
- Department of Chemistry, University of Washington, Seattle, Washington 98195-1700, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195-1750, United States
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21
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D'Agostino M, Motta S, Romagnoli A, Orlando P, Tiano L, La Teana A, Di Marino D. Insights Into the Binding Mechanism of GC7 to Deoxyhypusine Synthase in Sulfolobus solfataricus: A Thermophilic Model for the Design of New Hypusination Inhibitors. Front Chem 2021; 8:609942. [PMID: 33392152 PMCID: PMC7773846 DOI: 10.3389/fchem.2020.609942] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 11/16/2020] [Indexed: 12/18/2022] Open
Abstract
Translation factor 5A (eIF5A) is one of the most conserved proteins involved in protein synthesis. It plays a key role during the elongation of polypeptide chains, and its activity is critically dependent on hypusination, a post-translational modification of a specific lysine residue through two consecutive enzymatic steps carried out by deoxyhypusine synthase (DHS), with spermidine as substrate, and deoxyhypusine hydroxylase (DOHH). It is well-established that eIF5A is overexpressed in several cancer types, and it is involved in various diseases such as HIV-1 infection, malaria, and diabetes; therefore, the development of inhibitors targeting both steps of the hypusination process is considered a promising and challenging therapeutic strategy. One of the most efficient inhibitors of the hypusination process is the spermidine analog N1-guanyl-1,7-diaminoheptane (GC7). GC7 interacts in a specific binding pocket of the DHS completely blocking its activity; however, its therapeutic use is limited by poor selectivity and restricted bioavailability. Here we have performed a comparative study between human DHS (hDHS) and archaeal DHS from crenarchaeon Sulfolobus solfataricus (aDHS) to understand the structural and dynamical features of the GC7 inhibition. The advanced metadynamics (MetaD) classical molecular dynamics simulations show that the GC7 interaction is less stable in the thermophilic enzyme compared to hDHS that could underlie a lower inhibitory capacity of the hypusination process in Sulfolobus solfataricus. To confirm this hypothesis, we have tested GC7 activity on S. solfataricus by measuring cellular growth, and results have shown the lack of inhibition of aIF5A hypusination in contrast to the established effect on eukaryotic cellular growth. These results provide, for the first time, detailed molecular insights into the binding mechanism of GC7 to aDHS generating the basis for the design of new and more specific DHS inhibitors.
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Affiliation(s)
- Mattia D'Agostino
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Stefano Motta
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Alice Romagnoli
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy.,New York-Marche Structural Biology Center (Ny-Masbic), Polytechnic University of Marche, Ancona, Italy
| | - Patrick Orlando
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Luca Tiano
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Anna La Teana
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy.,New York-Marche Structural Biology Center (Ny-Masbic), Polytechnic University of Marche, Ancona, Italy
| | - Daniele Di Marino
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy.,New York-Marche Structural Biology Center (Ny-Masbic), Polytechnic University of Marche, Ancona, Italy
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22
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Chen M. Collective variable-based enhanced sampling and machine learning. THE EUROPEAN PHYSICAL JOURNAL. B 2021; 94:211. [PMID: 34697536 PMCID: PMC8527828 DOI: 10.1140/epjb/s10051-021-00220-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 10/03/2021] [Indexed: 05/14/2023]
Abstract
ABSTRACT Collective variable-based enhanced sampling methods have been widely used to study thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced sampling methods are affected by two factors: constructing appropriate collective variables for enhanced sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling methods, there are still many challenges and open questions. In this perspective, we shall review recent developments on integrating machine learning techniques and collective variable-based enhanced sampling approaches. We also discuss challenges and future research directions including generating kinetic information, exploring high-dimensional free energy surfaces, and efficiently sampling all-atom configurations.
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Affiliation(s)
- Ming Chen
- Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA
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23
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Decherchi S, Cavalli A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem Rev 2020; 120:12788-12833. [PMID: 33006893 PMCID: PMC8011912 DOI: 10.1021/acs.chemrev.0c00534] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 12/19/2022]
Abstract
Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
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Affiliation(s)
- Sergio Decherchi
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
| | - Andrea Cavalli
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology, University
of Bologna, 40126 Bologna, Italy
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24
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Chu X, Suo Z, Wang J. Investigating the trade-off between folding and function in a multidomain Y-family DNA polymerase. eLife 2020; 9:60434. [PMID: 33079059 PMCID: PMC7641590 DOI: 10.7554/elife.60434] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/16/2020] [Indexed: 01/01/2023] Open
Abstract
The way in which multidomain proteins fold has been a puzzling question for decades. Until now, the mechanisms and functions of domain interactions involved in multidomain protein folding have been obscure. Here, we develop structure-based models to investigate the folding and DNA-binding processes of the multidomain Y-family DNA polymerase IV (DPO4). We uncover shifts in the folding mechanism among ordered domain-wise folding, backtracking folding, and cooperative folding, modulated by interdomain interactions. These lead to ‘U-shaped’ DPO4 folding kinetics. We characterize the effects of interdomain flexibility on the promotion of DPO4–DNA (un)binding, which probably contributes to the ability of DPO4 to bypass DNA lesions, which is a known biological role of Y-family polymerases. We suggest that the native topology of DPO4 leads to a trade-off between fast, stable folding and tight functional DNA binding. Our approach provides an effective way to quantitatively correlate the roles of protein interactions in conformational dynamics at the multidomain level.
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Affiliation(s)
- Xiakun Chu
- Department of Chemistry, State University of New York at Stony Brook, New York, United States
| | - Zucai Suo
- Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, United States
| | - Jin Wang
- Department of Chemistry, State University of New York at Stony Brook, New York, United States
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25
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Bal KM, Fukuhara S, Shibuta Y, Neyts EC. Free energy barriers from biased molecular dynamics simulations. J Chem Phys 2020; 153:114118. [PMID: 32962376 DOI: 10.1063/5.0020240] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Atomistic simulation methods for the quantification of free energies are in wide use. These methods operate by sampling the probability density of a system along a small set of suitable collective variables (CVs), which is, in turn, expressed in the form of a free energy surface (FES). This definition of the FES can capture the relative stability of metastable states but not that of the transition state because the barrier height is not invariant to the choice of CVs. Free energy barriers therefore cannot be consistently computed from the FES. Here, we present a simple approach to calculate the gauge correction necessary to eliminate this inconsistency. Using our procedure, the standard FES as well as its gauge-corrected counterpart can be obtained by reweighing the same simulated trajectory at little additional cost. We apply the method to a number of systems-a particle solvated in a Lennard-Jones fluid, a Diels-Alder reaction, and crystallization of liquid sodium-to demonstrate its ability to produce consistent free energy barriers that correctly capture the kinetics of chemical or physical transformations, and discuss the additional demands it puts on the chosen CVs. Because the FES can be converged at relatively short (sub-ns) time scales, a free energy-based description of reaction kinetics is a particularly attractive option to study chemical processes at more expensive quantum mechanical levels of theory.
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Affiliation(s)
- Kristof M Bal
- Department of Chemistry and NANOLab Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Satoru Fukuhara
- Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yasushi Shibuta
- Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Erik C Neyts
- Department of Chemistry and NANOLab Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
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26
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Turner P, Elder RM, Nahan K, Talley A, Shah S, Duncan TV, Sussman EM, Saylor DM. Leveraging Extraction Testing to Predict Patient Exposure to Polymeric Medical Device Leachables Using Physics-based Models. Toxicol Sci 2020; 178:201-211. [DOI: 10.1093/toxsci/kfaa140] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Abstract
Toxicological risk assessment approaches are increasingly being used in lieu of animal testing to address toxicological concerns associated with release of chemical constituents from polymeric medical device components. These approaches currently rely on in vitro extraction testing in aggressive environments to estimate patient exposure to these constituents, but the clinical relevance of the test results is often ambiguous. Physics-based mass transport models can provide a framework to interpret extraction test results to provide more clinically relevant exposure estimates. However, the models require system-specific material properties, such as diffusion (D) and partition coefficients (K), to be established a priori for the extraction conditions. Using systems comprised high-density polyethylene and 4 different additives, we demonstrate that these properties can be quantified through standard extraction testing in hexane and isopropyl alcohol. The values of D and K derived in this manner were consistent with theoretical predictions for these quantities. Based on these results, we discuss both the challenges and benefits to leveraging extraction data to parameterize physics-based exposure models. Our observations suggest that clinically relevant, yet still conservative, exposure dose estimates provided by applying this approach to a single extraction measurement can be more than 100 times lower than would be measured under typical aggressive extraction conditions. However, to apply the framework on a routine basis, limiting values of D and K must be established for device-relevant systems either through the aggregation and analysis of more extensive extraction test data and/or advancements in theoretical and computational modeling efforts to predict these quantities.
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Affiliation(s)
- Paul Turner
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
| | - Robert M Elder
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
| | - Keaton Nahan
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
| | - Anne Talley
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
| | - Saloni Shah
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
- Office of Food Safety, Center for Food Safety and Applied Nutrition, FDA, Bedford Park, Illinois 60501
| | - Timothy V Duncan
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
- Office of Food Safety, Center for Food Safety and Applied Nutrition, FDA, Bedford Park, Illinois 60501
| | - Eric M Sussman
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
| | - David M Saylor
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993
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27
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Peter EK, Shea JE, Schug A. CORE-MD, a path correlated molecular dynamics simulation method. J Chem Phys 2020; 153:084114. [DOI: 10.1063/5.0015398] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Emanuel K. Peter
- John von Neumann Institute for Computing and Julich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany
| | - Joan-Emma Shea
- Department of Chemistry and Biochemistry, Department of Physics, University of California, Santa Barbara, Santa Barbara, California 93106, USA
| | - Alexander Schug
- John von Neumann Institute for Computing and Julich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany
- Faculty of Biology, University of Duisburg-Essen, Duisburg, Germany
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28
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Khan SA, Dickson BM, Peters B. How fluxional reactants limit the accuracy/efficiency of infrequent metadynamics. J Chem Phys 2020; 153:054125. [DOI: 10.1063/5.0006980] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Salman A. Khan
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106-5080, USA
| | | | - Baron Peters
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Chemistry and Biochemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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29
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Nunes-Alves A, Kokh DB, Wade RC. Recent progress in molecular simulation methods for drug binding kinetics. Curr Opin Struct Biol 2020; 64:126-133. [PMID: 32771530 DOI: 10.1016/j.sbi.2020.06.022] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/23/2020] [Accepted: 06/23/2020] [Indexed: 12/29/2022]
Abstract
Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.
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Affiliation(s)
- Ariane Nunes-Alves
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany.
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30
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Capelli R, Lyu W, Bolnykh V, Meloni S, Olsen JMH, Rothlisberger U, Parrinello M, Carloni P. Accuracy of Molecular Simulation-Based Predictions of koff Values: A Metadynamics Study. J Phys Chem Lett 2020; 11:6373-6381. [PMID: 32672983 DOI: 10.1021/acs.jpclett.0c00999] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The koff values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established method-infrequent metadynamics, based on the AMBER force field-to compute the koff of the ligand iperoxo (in clinical use) targeting the muscarinic receptor M2. The ligand charges are calculated by either (i) the Amber standard procedure or (ii) B3LYP-DFT. The calculations using (i) turn out not to provide a reasonable estimation of the transition-state free energy. Those using (ii) differ from experiment by 2 orders of magnitude. On the basis of B3LYP DFT QM/MM simulations, we suggest that the observed discrepancy in (ii) arises, at least in part, from the lack of electronic polarization and/or charge transfer in biomolecular force fields. These issues might be present in other systems, such as DNA-protein complexes.
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Affiliation(s)
- Riccardo Capelli
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- JARA-HPC, Forschungszentrum Jülich, D-54245 Jülich, Germany
| | - Wenping Lyu
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
| | - Viacheslav Bolnykh
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Simone Meloni
- Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Ferrara, Via Luigi Borsari 46, I-44121, Ferrara, Italy
| | - Jógvan Magnus Haugaard Olsen
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
- Department of Chemistry, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zürich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Paolo Carloni
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- JARA-Institute INM-11: Molecular Neuroscience and Neuroimaging, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
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31
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Ray D, Andricioaei I. Weighted ensemble milestoning (WEM): A combined approach for rare event simulations. J Chem Phys 2020; 152:234114. [DOI: 10.1063/5.0008028] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Affiliation(s)
- Dhiman Ray
- Department of Chemistry, University of California Irvine, California 92697, USA
| | - Ioan Andricioaei
- Department of Chemistry, University of California Irvine, California 92697, USA
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32
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Arsiccio A, McCarty J, Pisano R, Shea JE. Heightened Cold-Denaturation of Proteins at the Ice–Water Interface. J Am Chem Soc 2020; 142:5722-5730. [DOI: 10.1021/jacs.9b13454] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Andrea Arsiccio
- Department of Applied Science and Technology, Politecnico di Torino, 24 corso Duca degli Abruzzi, Torino 10129, Italy
| | - James McCarty
- Department of Chemistry, Western Washington University, Bellingham, Washington 98225, United States
| | - Roberto Pisano
- Department of Applied Science and Technology, Politecnico di Torino, 24 corso Duca degli Abruzzi, Torino 10129, Italy
| | - Joan-Emma Shea
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, United States
- Department of Physics, University of California, Santa Barbara, California 93106, United States
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33
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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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34
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Kieninger S, Donati L, Keller BG. Dynamical reweighting methods for Markov models. Curr Opin Struct Biol 2020; 61:124-131. [PMID: 31958761 DOI: 10.1016/j.sbi.2019.12.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/13/2019] [Accepted: 12/26/2019] [Indexed: 12/21/2022]
Abstract
Conformational dynamics is essential to biomolecular processes. Markov State Models (MSMs) are widely used to elucidate dynamic properties of molecular systems from unbiased Molecular Dynamics (MD). However, the implementation of reweighting schemes for MSMs to analyze biased simulations is still at an early stage of development. Several dynamical reweighing approaches have been proposed, which can be classified as approaches based on (i) Kramers rate theory, (ii) rescaling of the probability density flux, (iii) reweighting by formulating a likelihood function, (iv) path reweighting. We present the state-of-the-art and discuss the methodological differences of these methods, their limitations and recent applications.
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Affiliation(s)
- Stefanie Kieninger
- Freie Universität Berlin, Department of Biology, Chemistry, Pharmacy, Arnimallee 22, D-14194 Berlin, Germany
| | - Luca Donati
- Freie Universität Berlin, Department of Biology, Chemistry, Pharmacy, Arnimallee 22, D-14194 Berlin, Germany
| | - Bettina G Keller
- Freie Universität Berlin, Department of Biology, Chemistry, Pharmacy, Arnimallee 22, D-14194 Berlin, Germany.
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35
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Functional and structural basis of E. coli enolase inhibition by SF2312: a mimic of the carbanion intermediate. Sci Rep 2019; 9:17106. [PMID: 31745118 PMCID: PMC6863902 DOI: 10.1038/s41598-019-53301-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 10/22/2019] [Indexed: 11/08/2022] Open
Abstract
Many years ago, the natural secondary metabolite SF2312, produced by the actinomycete Micromonospora, was reported to display broad spectrum antibacterial properties against both Gram-positive and Gram-negative bacteria. Recent studies have revealed that SF2312, a natural phosphonic acid, functions as a potent inhibitor of human enolase. The mechanism of SF2312 inhibition of bacterial enolase and its role in bacterial growth and reproduction, however, have remained elusive. In this work, we detail a structural analysis of E. coli enolase bound to both SF2312 and its oxidized imide-form. Our studies support a model in which SF2312 acts as an analog of a high energy intermediate formed during the catalytic process. Biochemical, biophysical, computational and kinetic characterization of these compounds confirm that altering features characteristic of a putative carbanion (enolate) intermediate significantly reduces the potency of enzyme inhibition. When SF2312 is combined with fosfomycin in the presence of glucose-6 phosphate, significant synergy is observed. This suggests the two agents could be used as a potent combination, targeting distinct cellular mechanism for the treatment of bacterial infections. Together, our studies rationalize the structure-activity relationships for these phosphonates and validate enolase as a promising target for antibiotic discovery.
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36
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Zhou Y, Zou R, Kuang G, Långström B, Halldin C, Ågren H, Tu Y. Enhanced Sampling Simulations of Ligand Unbinding Kinetics Controlled by Protein Conformational Changes. J Chem Inf Model 2019; 59:3910-3918. [DOI: 10.1021/acs.jcim.9b00523] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Yang Zhou
- Department of Theoretical Chemistry and Biology, KTH Royal Institute of Technology, AlbaNova University Center, Stockholm 10691, Sweden
| | - Rongfeng Zou
- Department of Theoretical Chemistry and Biology, KTH Royal Institute of Technology, AlbaNova University Center, Stockholm 10691, Sweden
| | - Guanglin Kuang
- Department of Theoretical Chemistry and Biology, KTH Royal Institute of Technology, AlbaNova University Center, Stockholm 10691, Sweden
| | - Bengt Långström
- Department of Chemistry, Uppsala University, Uppsala 75123, Sweden
| | - Christer Halldin
- Department of Clinical Neuroscience, Center for Psychiatric Research, Karolinska Institutet and Stockholm County Council, Stockholm 17176, Sweden
| | - Hans Ågren
- Department of Theoretical Chemistry and Biology, KTH Royal Institute of Technology, AlbaNova University Center, Stockholm 10691, Sweden
- College of Chemistry and Chemical Engineering, Henan University, Kaifeng, Henan 475004, P. R. China
| | - Yaoquan Tu
- Department of Theoretical Chemistry and Biology, KTH Royal Institute of Technology, AlbaNova University Center, Stockholm 10691, Sweden
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37
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Metadynamics to Enhance Sampling in Biomolecular Simulations. Methods Mol Biol 2019. [PMID: 31396904 DOI: 10.1007/978-1-4939-9608-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Molecular dynamics is a powerful simulation method to provide detailed atomic-scale insight into a range of biological processes including protein folding, biochemical reactions, ligand binding, and many others. Over the last several decades, enhanced sampling methods have been developed to address the large separation in time scales between a molecular dynamics simulation (usually microseconds or shorter) and the time scales of biological processes (often orders of magnitude longer). This chapter specifically focuses on the metadynamics family of methods, which achieves enhanced sampling through the introduction of a history-dependent bias potential that is based on one or more slow degrees of freedom, called collective variables. We introduce the method and its recent variants related to biomolecular studies and then discuss frontier areas of the method. A large part of this chapter is devoted to helping new users of the method understand how to choose metadynamics parameters properly and apply the method to their system of interest.
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38
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Flood E, Boiteux C, Lev B, Vorobyov I, Allen TW. Atomistic Simulations of Membrane Ion Channel Conduction, Gating, and Modulation. Chem Rev 2019; 119:7737-7832. [DOI: 10.1021/acs.chemrev.8b00630] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Emelie Flood
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia
| | - Céline Boiteux
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia
| | - Bogdan Lev
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia
| | - Igor Vorobyov
- Department of Physiology & Membrane Biology/Department of Pharmacology, University of California, Davis, 95616, United States
| | - Toby W. Allen
- School of Science, RMIT University, Melbourne, Victoria 3000, Australia
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39
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Rydzewski J, Valsson O. Finding multiple reaction pathways of ligand unbinding. J Chem Phys 2019; 150:221101. [DOI: 10.1063/1.5108638] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Jakub Rydzewski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87–100 Torun, Poland
| | - Omar Valsson
- Max Planck Institute for Polymer Research, Ackermannweg 10, D-55128 Mainz, Germany
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40
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Wang AH, Zhang ZC, Li GH. Advances in enhanced sampling molecular dynamics simulations for biomolecules. CHINESE J CHEM PHYS 2019. [DOI: 10.1063/1674-0068/cjcp1905091] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- An-hui Wang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian 116024, China
| | - Zhi-chao Zhang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian 116024, China
| | - Guo-hui Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
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41
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Feher VA, Schiffer JM, Mermelstein DJ, Mih N, Pierce LCT, McCammon JA, Amaro RE. Mechanisms for Benzene Dissociation through the Excited State of T4 Lysozyme L99A Mutant. Biophys J 2019; 116:205-214. [PMID: 30606449 PMCID: PMC6349996 DOI: 10.1016/j.bpj.2018.09.035] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 06/23/2018] [Accepted: 09/27/2018] [Indexed: 12/23/2022] Open
Abstract
The atomic-level mechanisms that coordinate ligand release from protein pockets are only known for a handful of proteins. Here, we report results from accelerated molecular dynamics simulations for benzene dissociation from the buried cavity of the T4 lysozyme Leu99Ala mutant (L99A). In these simulations, benzene is released through a previously characterized, sparsely populated room-temperature excited state of the mutant, explaining the coincidence for experimentally measured benzene off rate and apo protein slow-timescale NMR relaxation rates between ground and excited states. The path observed for benzene egress is a multistep ligand migration from the buried cavity to ultimate release through an opening between the F/G-, H-, and I-helices and requires a number of cooperative multiresidue and secondary-structure rearrangements within the C-terminal domain of L99A. These rearrangements are identical to those observed along the ground state to excited state transitions characterized by molecular dynamic simulations run on the Anton supercomputer. Analyses of the molecular properties of the residues lining the egress path suggest that protein surface electrostatic potential may play a role in the release mechanism. Simulations of wild-type T4 lysozyme also reveal that benzene-egress-associated dynamics in the L99A mutant are potentially exaggerations of the substrate-processivity-related dynamics of the wild type.
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Affiliation(s)
| | | | - Daniel J Mermelstein
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California
| | - Nathan Mih
- Department of Bioinformatics and Systems Biology, University of California San Diego, La Jolla, California
| | | | - J Andrew McCammon
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California.
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42
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Ribeiro JML, Tsai ST, Pramanik D, Wang Y, Tiwary P. Kinetics of Ligand-Protein Dissociation from All-Atom Simulations: Are We There Yet? Biochemistry 2018; 58:156-165. [PMID: 30547565 DOI: 10.1021/acs.biochem.8b00977] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Large parallel gains in the development of both computational resources and sampling methods have now made it possible to simulate dissociation events in ligand-protein complexes with all-atom resolution. Such encouraging progress, together with the inherent spatiotemporal resolution associated with molecular simulations, has left their use for investigating dissociation processes brimming with potential, both in rational drug design, where it can be an invaluable tool for determining the mechanistic driving forces behind dissociation rate constants, and in force-field development, where it can provide a catalog of transient molecular structures with which to refine force fields. Although much progress has been made in making force fields more accurate, reducing their error for transient structures along a transition path could yet prove to be a critical development helping to make kinetic predictions much more accurate. In what follows, we will provide a state-of-the-art compilation of the enhanced sampling methods based on molecular dynamics (MD) simulations used to investigate the kinetics and mechanisms of ligand-protein dissociation processes. Due to the time scales of such processes being slower than what is accessible using straightforward MD simulations, several ingenious schemes are being devised at a rapid rate to overcome this obstacle. Here we provide an up-to-date compendium of such methods and their achievements and shortcomings in extracting mechanistic insight into ligand-protein dissociation. We conclude with a critical and provocative appraisal attempting to answer the title of this Perspective.
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Affiliation(s)
- João Marcelo Lamim Ribeiro
- Department of Chemistry and Biochemistry , University of Maryland , College Park , Maryland 20742 , United States.,Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States
| | - Sun-Ting Tsai
- Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States.,Department of Physics , University of Maryland , College Park , Maryland 20742 , United States
| | - Debabrata Pramanik
- Department of Chemistry and Biochemistry , University of Maryland , College Park , Maryland 20742 , United States.,Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States
| | - Yihang Wang
- Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States.,Biophysics Program , University of Maryland , College Park , Maryland 20742 , United States
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry , University of Maryland , College Park , Maryland 20742 , United States.,Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States
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43
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Smith Z, Pramanik D, Tsai ST, Tiwary P. Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization. J Chem Phys 2018; 149:234105. [DOI: 10.1063/1.5064856] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Zachary Smith
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA
| | - Debabrata Pramanik
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA
| | - Sun-Ting Tsai
- Department of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA
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44
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Bottaro S, Lindorff-Larsen K. Biophysical experiments and biomolecular simulations: A perfect match? Science 2018; 361:355-360. [DOI: 10.1126/science.aat4010] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
A fundamental challenge in biological research is achieving an atomic-level description and mechanistic understanding of the function of biomolecules. Techniques for biomolecular simulations have undergone substantial developments, and their accuracy and scope have expanded considerably. Progress has been made through an increasingly tight integration of experiments and simulations, with experiments being used to refine simulations and simulations used to interpret experiments. Here we review the underpinnings of this progress, including methods for more efficient conformational sampling, accuracy of the physical models used, and theoretical approaches to integrate experiments and simulations. These developments are enabling detailed studies of complex biomolecular assemblies.
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