1
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Manathunga M, Jin C, Cruzeiro VWD, Miao Y, Mu D, Arumugam K, Keipert K, Aktulga HM, Merz KM, Götz AW. Harnessing the Power of Multi-GPU Acceleration into the Quantum Interaction Computational Kernel Program. J Chem Theory Comput 2021; 17:3955-3966. [PMID: 34062061 DOI: 10.1021/acs.jctc.1c00145] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We report a new multi-GPU capable ab initio Hartree-Fock/density functional theory implementation integrated into the open source QUantum Interaction Computational Kernel (QUICK) program. Details on the load balancing algorithms for electron repulsion integrals and exchange correlation quadrature across multiple GPUs are described. Benchmarking studies carried out on up to four GPU nodes, each containing four NVIDIA V100-SXM2 type GPUs demonstrate that our implementation is capable of achieving excellent load balancing and high parallel efficiency. For representative medium to large size protein/organic molecular systems, the observed parallel efficiencies remained above 82% for the Kohn-Sham matrix formation and above 90% for nuclear gradient calculations. The accelerations on NVIDIA A100, P100, and K80 platforms also have realized parallel efficiencies higher than 68% in all tested cases, paving the way for large-scale ab initio electronic structure calculations with QUICK.
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Affiliation(s)
- Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Chi Jin
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Vinícius Wilian D Cruzeiro
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0505, United States.,Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Yipu Miao
- Facebook, 1 Hacker Way, Menlo Park, California 94025, United States
| | - Dawei Mu
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 W Clark Street, Urbana, Illinois 61801, United States
| | - Kamesh Arumugam
- NVIDIA Corporation, Santa Clara, California 95051, United States
| | | | - Hasan Metin Aktulga
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0505, United States
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2
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Casalino L, Dommer A, Gaieb Z, Barros EP, Sztain T, Ahn SH, Trifan A, Brace A, Bogetti A, Ma H, Lee H, Turilli M, Khalid S, Chong L, Simmerling C, Hardy DJ, Maia JDC, Phillips JC, Kurth T, Stern A, Huang L, McCalpin J, Tatineni M, Gibbs T, Stone JE, Jha S, Ramanathan A, Amaro RE. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.11.19.390187. [PMID: 33236007 PMCID: PMC7685317 DOI: 10.1101/2020.11.19.390187] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.
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Affiliation(s)
| | | | | | | | | | | | - Anda Trifan
- Argonne National Lab
- University of Illinois at Urbana-Champaign
| | | | | | | | - Hyungro Lee
- Rutgers University & Brookhaven National Lab
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3
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Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, Buch R, Fiorin G, Hénin J, Jiang W, McGreevy R, Melo MCR, Radak BK, Skeel RD, Singharoy A, Wang Y, Roux B, Aksimentiev A, Luthey-Schulten Z, Kalé LV, Schulten K, Chipot C, Tajkhorshid E. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 2020; 153:044130. [PMID: 32752662 PMCID: PMC7395834 DOI: 10.1063/5.0014475] [Citation(s) in RCA: 1332] [Impact Index Per Article: 333.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023] Open
Abstract
NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.
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Affiliation(s)
| | - David J. Hardy
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Julio D. C. Maia
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - John E. Stone
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - João V. Ribeiro
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Rafael C. Bernardi
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | | | - Giacomo Fiorin
- National Heart, Lung and Blood Institute, National
Institutes of Health, Bethesda, Maryland 20814,
USA
| | - Jérôme Hénin
- Laboratoire de Biochimie Théorique UPR 9080, CNRS
and Université de Paris, Paris, France
| | | | - Ryan McGreevy
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | | | - Brian K. Radak
- NIH Center for Macromolecular Modeling and
Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for
Advanced Science and Technology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Robert D. Skeel
- School of Mathematical and Statistical Sciences,
Arizona State University, Tempe, Arizona 85281,
USA
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State
University, Tempe, Arizona 85281, USA
| | - Yi Wang
- Department of Physics, The Chinese University of
Hong Kong, Shatin, Hong Kong, China
| | - Benoît Roux
- Department of Biochemistry, University of
Chicago, Chicago, Illinois 60637, USA
| | | | | | | | | | - Christophe Chipot
- Authors to whom correspondence should be addressed:
and . URL: http://www.ks.uiuc.edu
| | - Emad Tajkhorshid
- Authors to whom correspondence should be addressed:
and . URL: http://www.ks.uiuc.edu
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4
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Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. Biophys J 2020; 118:2042-2055. [PMID: 32224301 DOI: 10.1016/j.bpj.2020.03.006] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 02/04/2020] [Accepted: 03/09/2020] [Indexed: 11/19/2022] Open
Abstract
Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.
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5
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Jung J, Nishima W, Daniels M, Bascom G, Kobayashi C, Adedoyin A, Wall M, Lappala A, Phillips D, Fischer W, Tung CS, Schlick T, Sugita Y, Sanbonmatsu KY. Scaling molecular dynamics beyond 100,000 processor cores for large-scale biophysical simulations. J Comput Chem 2019; 40:1919-1930. [PMID: 30994934 PMCID: PMC7153361 DOI: 10.1002/jcc.25840] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 03/06/2019] [Accepted: 03/20/2019] [Indexed: 01/27/2023]
Abstract
The growing interest in the complexity of biological interactions is continuously driving the need to increase system size in biophysical simulations, requiring not only powerful and advanced hardware but adaptable software that can accommodate a large number of atoms interacting through complex forcefields. To address this, we developed and implemented strategies in the GENESIS molecular dynamics package designed for large numbers of processors. Long-range electrostatic interactions were parallelized by minimizing the number of processes involved in communication. A novel algorithm was implemented for nonbonded interactions to increase single instruction multiple data (SIMD) performance, reducing memory usage for ultra large systems. Memory usage for neighbor searches in real-space nonbonded interactions was reduced by approximately 80%, leading to significant speedup. Using experimental data describing physical 3D chromatin interactions, we constructed the first atomistic model of an entire gene locus (GATA4). Taken together, these developments enabled the first billion-atom simulation of an intact biomolecular complex, achieving scaling to 65,000 processes (130,000 processor cores) with 1 ns/day performance. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | - Wataru Nishima
- Los Alamos National Laboratory, Los Alamos, New Mexico
- New Mexico Consortium, Los Alamos, New Mexico
| | | | | | - Chigusa Kobayashi
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | | | - Michael Wall
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | - Anna Lappala
- Los Alamos National Laboratory, Los Alamos, New Mexico
| | | | | | | | | | - Yuji Sugita
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | - Karissa Y Sanbonmatsu
- Los Alamos National Laboratory, Los Alamos, New Mexico
- New Mexico Consortium, Los Alamos, New Mexico
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6
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Olsen JMH, Bolnykh V, Meloni S, Ippoliti E, Bircher MP, Carloni P, Rothlisberger U. MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry. J Chem Theory Comput 2019; 15:3810-3823. [DOI: 10.1021/acs.jctc.9b00093] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jógvan Magnus Haugaard Olsen
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
| | - Viacheslav Bolnykh
- Department of Physics, RWTH Aachen University, 52056 Aachen, Germany
- CaSToRC, The Cyprus
Institute, 2121 Aglantzia, Nicosia, Cyprus
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Simone Meloni
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy
| | - Emiliano Ippoliti
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Martin P. Bircher
- Laboratory of Computational Chemistry and Biochemistry, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Paolo Carloni
- Department of Physics, RWTH Aachen University, 52056 Aachen, Germany
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute for Neuroscience and Medicine (INM-11), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
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7
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Acun B, Hardy DJ, Kale LV, Li K, Phillips JC, Stone JE. Scalable Molecular Dynamics with NAMD on the Summit System. IBM JOURNAL OF RESEARCH AND DEVELOPMENT 2018; 62:1-9. [PMID: 32154805 PMCID: PMC7059615 DOI: 10.1147/jrd.2018.2888986] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
NAMD (NAnoscale Molecular Dynamics) is a parallel molecular dynamics application that has been used to make breakthroughs in understanding the structure and dynamics of large biomolecular complexes, such as viruses like HIV and various types of influenza. State-of-the-art biomolecular simulations often require integration of billions of timesteps, computing all interatomic forces for each femtosecond timestep. Molecular dynamics simulation of large biomolecular systems and long-timescale biological phenomena requires tremendous computing power. NAMD harnesses the power of thousands of heterogeneous processors to meet this demand. In this paper, we present algorithm improvements and performance optimizations that enable NAMD to achieve high performance on the IBM Newell platform (with POWER9 processors and NVIDIA Volta V100 GPUs) which underpins the Oak Ridge National Laboratory's Summit and Lawrence Livermore National Laboratory's Sierra supercomputers. The Top-500 supercomputers June 2018 list shows Summit at the number one spot with 187 Petaflop/s peak performance and Sierra third with 119 Petaflop/s. Optimizations for NAMD on Summit include: data layout changes for GPU acceleration and CPU vectorization, improving GPU offload efficiency, increasing performance with PAMI support in Charm++, improving efficiency of FFT calculations, improving load balancing, enabling better CPU vectorization and cache performance, and providing an alternative thermostat through stochastic velocity rescaling. We also present performance scaling results on early Newell systems.
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Affiliation(s)
- B Acun
- IBM Research Division, IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - D J Hardy
- Theoretical and Computational Biophysics Group, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - L V Kale
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - K Li
- Nvidia Corporation, Santa Clara, CA 95051, USA
| | - J C Phillips
- NCSA Blue Waters Project Office, University of Illinois at Urbana-Champaign, Urbana IL 61801, USA
| | - J E Stone
- Theoretical and Computational Biophysics Group, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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8
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Physical properties of the HIV-1 capsid from all-atom molecular dynamics simulations. Nat Commun 2017; 8:15959. [PMID: 28722007 PMCID: PMC5524983 DOI: 10.1038/ncomms15959] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 05/12/2017] [Indexed: 12/20/2022] Open
Abstract
Human immunodeficiency virus type 1 (HIV-1) infection is highly dependent on its capsid. The capsid is a large container, made of ∼1,300 proteins with altogether 4 million atoms. Although the capsid proteins are all identical, they nevertheless arrange themselves into a largely asymmetric structure made of hexamers and pentamers. The large number of degrees of freedom and lack of symmetry pose a challenge to studying the chemical details of the HIV capsid. Simulations of over 64 million atoms for over 1 μs allow us to conduct a comprehensive study of the chemical–physical properties of an empty HIV-1 capsid, including its electrostatics, vibrational and acoustic properties, and the effects of solvent (ions and water) on the capsid. The simulations reveal critical details about the capsid with implications to biological function. The large and complex HIV-1 capsid modulates several molecular events during HIV-1’s infective cycle. Here the authors use all-atom molecular dynamic simulations to probe the biophysical properties of the genome-free HIV-1 capsid.
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9
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Nash JA, Kwansa AL, Peerless JS, Kim HS, Yingling YG. Advances in Molecular Modeling of Nanoparticle–Nucleic Acid Interfaces. Bioconjug Chem 2016; 28:3-10. [DOI: 10.1021/acs.bioconjchem.6b00534] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jessica A. Nash
- Department of Materials Science
and Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Albert L. Kwansa
- Department of Materials Science
and Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - James S. Peerless
- Department of Materials Science
and Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Ho Shin Kim
- Department of Materials Science
and Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Yaroslava G. Yingling
- Department of Materials Science
and Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
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10
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Jung J, Naurse A, Kobayashi C, Sugita Y. Graphics Processing Unit Acceleration and Parallelization of GENESIS for Large-Scale Molecular Dynamics Simulations. J Chem Theory Comput 2016; 12:4947-4958. [DOI: 10.1021/acs.jctc.6b00241] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jaewoon Jung
- RIKEN Theoretical Molecular Science Laboratory, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- RIKEN Advanced Institute for Computational Science, 7-1-26 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 640-0047, Japan
| | - Akira Naurse
- NVIDIA, 2-11-7, Akasaka,
Minato-ku, Tokyo 107-0052, Japan
| | - Chigusa Kobayashi
- RIKEN Advanced Institute for Computational Science, 7-1-26 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 640-0047, Japan
| | - Yuji Sugita
- RIKEN Theoretical Molecular Science Laboratory, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- RIKEN Advanced Institute for Computational Science, 7-1-26 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 640-0047, Japan
- RIKEN iTHES, 2-1 Hirosawa,
Wako, Saitama 351-0198, Japan
- Laboratory
for Biomolecular Function Simulation, RIKEN Quantitative Biology Center (QBiC), 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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11
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Stone JE, Sener M, Vandivort KL, Barragan A, Singharoy A, Teo I, Ribeiro JV, Isralewitz B, Liu B, Goh BC, Phillips JC, MacGregor-Chatwin C, Johnson MP, Kourkoutis LF, Hunter CN, Schulten K. Atomic Detail Visualization of Photosynthetic Membranes with GPU-Accelerated Ray Tracing. PARALLEL COMPUTING 2016; 55:17-27. [PMID: 27274603 PMCID: PMC4890717 DOI: 10.1016/j.parco.2015.10.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
The cellular process responsible for providing energy for most life on Earth, namely photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. We present, in two accompanying movies, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movies are the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. We describe the techniques that were used to build, simulate, analyze, and visualize the structures shown in the movies, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers.
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Affiliation(s)
- John E Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Melih Sener
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Kirby L Vandivort
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Angela Barragan
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Abhishek Singharoy
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Ivan Teo
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 W Green St, Urbana, IL 61801, USA
| | - João V Ribeiro
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Barry Isralewitz
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Bo Liu
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Boon Chong Goh
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA; Department of Physics, University of Illinois at Urbana-Champaign, 1110 W Green St, Urbana, IL 61801, USA
| | - James C Phillips
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA
| | - Craig MacGregor-Chatwin
- Department of Molecular Biology and Biotechnology, University of Sheffield, Western Bank, Sheffield, South Yorkshire S10 2TN, UK
| | - Matthew P Johnson
- Department of Molecular Biology and Biotechnology, University of Sheffield, Western Bank, Sheffield, South Yorkshire S10 2TN, UK
| | - Lena F Kourkoutis
- School of Applied and Engineering Physics, Cornell University, 271 Clark Hall, Ithaca, New York 14853, USA; Kavli Institute at Cornell for Nanoscale Sciences, 420 Physical Sciences Building, Ithaca, New York 14853, USA
| | - C Neil Hunter
- Department of Molecular Biology and Biotechnology, University of Sheffield, Western Bank, Sheffield, South Yorkshire S10 2TN, UK
| | - Klaus Schulten
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, USA; Department of Physics, University of Illinois at Urbana-Champaign, 1110 W Green St, Urbana, IL 61801, USA
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12
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Molecular dynamics simulations of large macromolecular complexes. Curr Opin Struct Biol 2015; 31:64-74. [PMID: 25845770 DOI: 10.1016/j.sbi.2015.03.007] [Citation(s) in RCA: 264] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Revised: 03/13/2015] [Accepted: 03/16/2015] [Indexed: 12/11/2022]
Abstract
Connecting dynamics to structural data from diverse experimental sources, molecular dynamics simulations permit the exploration of biological phenomena in unparalleled detail. Advances in simulations are moving the atomic resolution descriptions of biological systems into the million-to-billion atom regime, in which numerous cell functions reside. In this opinion, we review the progress, driven by large-scale molecular dynamics simulations, in the study of viruses, ribosomes, bioenergetic systems, and other diverse applications. These examples highlight the utility of molecular dynamics simulations in the critical task of relating atomic detail to the function of supramolecular complexes, a task that cannot be achieved by smaller-scale simulations or existing experimental approaches alone.
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