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Cantero J, Ballesteros-Casallas A, Santos LHS, Paulino M, Pantano S. Pouring SIRAH on NAMD. J Phys Chem B 2024. [PMID: 39322588 DOI: 10.1021/acs.jpcb.4c03278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
Molecular dynamics (MD) simulations provide an invaluable platform for exploring the dynamics of complex biomolecular systems at atomic resolution. However, compatibility issues between force fields and MD software engines can limit the interoperability and transferability of simulations. This work demonstrates the successful use of the coarse-grained SIRAH force field on the widely used NAMD MD engine across a range of increasingly complex biomolecular systems. By leveraging NAMD's ability to read AMBER input files, SIRAH simulations can be run seamlessly on NAMD, including its recently released GPU-accelerated version, NAMD3. The benchmark systems demonstrate consistent results across AMBER, NAMD2, and NAMD3. Thus, these data highlight the enhanced simulation throughput achievable on GPU-accelerated desktop computers using all three engines along with SIRAH. Overall, this study expands the range of the SIRAH force field by utilizing advanced GPU computing resources and high-performance supercomputing facilities, which are particularly effective with NAMD.
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Affiliation(s)
- Jorge Cantero
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
- Centro de Investigaciones Médicas, Facultad de Ciencias de la Salud, Universidad Nacional del Este, Panambi 101305, Paraguay
| | - Andrés Ballesteros-Casallas
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
- Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
| | | | - Margot Paulino
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
| | - Sergio Pantano
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
- Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
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2
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Cerutti DS, Wiewiora R, Boothroyd S, Sherman W. STORMM: Structure and topology replica molecular mechanics for chemical simulations. J Chem Phys 2024; 161:032501. [PMID: 39007368 DOI: 10.1063/5.0211032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle-mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license.
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Affiliation(s)
| | | | | | - Woody Sherman
- Psivant Therapeutics, Boston, Massachusetts 02210, USA
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3
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Pirnia A, Maqdisi R, Mittal S, Sener M, Singharoy A. Perspective on Integrative Simulations of Bioenergetic Domains. J Phys Chem B 2024; 128:3302-3319. [PMID: 38562105 DOI: 10.1021/acs.jpcb.3c07335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.
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Affiliation(s)
- Adam Pirnia
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
| | - Ranel Maqdisi
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
| | - Sumit Mittal
- VIT Bhopal University, Sehore 466114, Madhya Pradesh, India
| | - Melih Sener
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
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Rácz A, Mihalovits LM, Bajusz D, Héberger K, Miranda-Quintana RA. Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices. J Chem Inf Model 2022; 62:3415-3425. [PMID: 35834424 PMCID: PMC9326969 DOI: 10.1021/acs.jcim.2c00433] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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Molecular dynamics (MD) is a core methodology of molecular
modeling
and computational design for the study of the dynamics and temporal
evolution of molecular systems. MD simulations have particularly benefited
from the rapid increase of computational power that has characterized
the past decades of computational chemical research, being the first
method to be successfully migrated to the GPU infrastructure. While
new-generation MD software is capable of delivering simulations on
an ever-increasing scale, relatively less effort is invested in developing
postprocessing methods that can keep up with the quickly expanding
volumes of data that are being generated. Here, we introduce a new
idea for sampling frames from large MD trajectories, based on the
recently introduced framework of extended similarity indices. Our
approach presents a new, linearly scaling alternative to the traditional
approach of applying a clustering algorithm that usually scales as
a quadratic function of the number of frames. When showcasing its
usage on case studies with different system sizes and simulation lengths,
we have registered speedups of up to 2 orders of magnitude, as compared
to traditional clustering algorithms. The conformational diversity
of the selected frames is also noticeably higher, which is a further
advantage for certain applications, such as the selection of structural
ensembles for ligand docking. The method is available open-source
at https://github.com/ramirandaq/MultipleComparisons.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Levente M Mihalovits
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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5
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Jones D, Allen JE, Yang Y, Drew Bennett WF, Gokhale M, Moshiri N, Rosing TS. Accelerators for Classical Molecular Dynamics Simulations of Biomolecules. J Chem Theory Comput 2022; 18:4047-4069. [PMID: 35710099 PMCID: PMC9281402 DOI: 10.1021/acs.jctc.1c01214] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.
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Affiliation(s)
- Derek Jones
- Department
of Computer Science and Engineering, University
of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Jonathan E. Allen
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Yue Yang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - William F. Drew Bennett
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Maya Gokhale
- Center
for Applied Scientific Computing, Lawrence
Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Niema Moshiri
- Department
of Computer Science and Engineering, University
of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Tajana S. Rosing
- Department
of Computer Science and Engineering, University
of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
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Karaca E, Prévost C, Sacquin-Mora S. Modeling the Dynamics of Protein-Protein Interfaces, How and Why? Molecules 2022; 27:1841. [PMID: 35335203 PMCID: PMC8950966 DOI: 10.3390/molecules27061841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/06/2022] [Accepted: 03/08/2022] [Indexed: 12/07/2022] Open
Abstract
Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.
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Affiliation(s)
- Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir 35340, Turkey;
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir 35340, Turkey
| | - Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
| | - Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
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7
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Affiliation(s)
- Heather J. Kulik
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Ave Rm 66–464 Cambridge MA 02139 USA
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8
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Vermaas JV, Sedova A, Baker MB, Boehm S, Rogers DM, Larkin J, Glaser J, Smith MD, Hernandez O, Smith JC. Supercomputing Pipelines Search for Therapeutics Against COVID-19. Comput Sci Eng 2021; 23:7-16. [PMID: 35939280 PMCID: PMC9280802 DOI: 10.1109/mcse.2020.3036540] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/31/2020] [Accepted: 11/03/2020] [Indexed: 11/15/2022]
Abstract
The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high-performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. This effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases.
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Affiliation(s)
| | - Ada Sedova
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | - Swen Boehm
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | | | - Jens Glaser
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
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Wu X, Han Q. Thermal conductivity of defective graphene: an efficient molecular dynamics study based on graphics processing units. NANOTECHNOLOGY 2020; 31:215708. [PMID: 32032004 DOI: 10.1088/1361-6528/ab73bc] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The exceptional thermal transport properties of graphene are affected due to the presence of various topological defects, which include single vacancy, double vacancies and Stone-Wales defects. The present article is intended to study on thermal transport properties of defective graphene by comparing the effects of topological defects on the thermal conductivity of graphene. This study developed a program for constructing defective graphene models with customizable defect concentrations and distribution types. The efficient molecular dynamics method based on graphics processing units is applied, which can achieve efficient and accurate calculation of material thermal conductivity. It is revealed that the existence of topological defects has a considerable reduce on the thermal conductivity of graphene, and the declining rate of the value get less with increasing defects concentration. At the same concentration, the weakening effect of SW defects on the thermal conductivity of graphene is evidently less than the other two defects. We also explored the effect of temperature on the thermal conductivity of graphene with different defects. These findings were discussed from the phonon perspective that elucidate the atomic level mechanisms, which provide guidance for thermal management of graphene devices.
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Affiliation(s)
- Xin Wu
- Department of Engineering Mechanics, School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong Province 510640, People's Republic of China
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