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Spivak M, Stone JE, Ribeiro J, Saam J, Freddolino PL, Bernardi RC, Tajkhorshid E. VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations. J Chem Inf Model 2023; 63:4664-4678. [PMID: 37506321 PMCID: PMC10516160 DOI: 10.1021/acs.jcim.3c00658] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.
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Affiliation(s)
- Mariano Spivak
- Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - John E Stone
- Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - João Ribeiro
- Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jan Saam
- Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Peter L Freddolino
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Rafael C Bernardi
- Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Physics, Auburn University, Auburn, Alabama 36849, United States
| | - Emad Tajkhorshid
- Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Biochemistry, Center for Biophyics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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Gilbert BR, Thornburg ZR, Brier TA, Stevens JA, Grünewald F, Stone JE, Marrink SJ, Luthey-Schulten Z. Dynamics of chromosome organization in a minimal bacterial cell. Front Cell Dev Biol 2023; 11:1214962. [PMID: 37621774 PMCID: PMC10445541 DOI: 10.3389/fcell.2023.1214962] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/10/2023] [Indexed: 08/26/2023] Open
Abstract
Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication and inheritance of genetic material. By creating a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics, we investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell-cycle. To achieve cell-scale chromosome structures that are realistic, we model the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. In addition, the conformations of the circular DNA must avoid overlapping with ribosomes identitied in cryo-electron tomograms. While Syn3A lacks the complex regulatory systems known to orchestrate chromosome segregation in other bacteria, its minimized genome retains essential loop-extruding structural maintenance of chromosomes (SMC) protein complexes (SMC-scpAB) and topoisomerases. Through implementing the effects of these proteins in our simulations of replicating chromosomes, we find that they alone are sufficient for simultaneous chromosome segregation across all generations within nested theta structures. This supports previous studies suggesting loop-extrusion serves as a near-universal mechanism for chromosome organization within bacterial and eukaryotic cells. Furthermore, we analyze ribosome diffusion under the influence of the chromosome and calculate in silico chromosome contact maps that capture inter-daughter interactions. Finally, we present a methodology to map the polymer model of the chromosome to a Martini coarse-grained representation to prepare molecular dynamics models of entire Syn3A cells, which serves as an ultimate means of validation for cell states predicted by the WCM.
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Affiliation(s)
- Benjamin R. Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Zane R. Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Troy A. Brier
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jan A. Stevens
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Fabian Grünewald
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - John E. Stone
- NVIDIA Corporation, Santa Clara, CA, United States
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Siewert J. Marrink
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- NSF Center for the Physics of Living Cells, Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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Elwasif W, Godoy W, Hagerty N, Harris JA, Hernandez O, Joo B, Kent P, Lebrun-Grandié D, Maccarthy E, Melesse Ver-Gara VG, Messer B, Miller R, Oral S, Bastrakov S, Bussmann M, Debus A, Steiniger K, Stephan J, Widera R, Bryngelson SH, LE Berre H, Radhakrishnan A, Young J, Chandrasekaran S, Ciorba F, Simsek O, Clark K, Spiga F, Hammond J, Stone JE, Hardy D, Keller S, Piccinali JG, Trott C. Application Experiences on a GPU-Accelerated Arm-based HPC Testbed. Proc Int Conf High Perform Comput Asia Pac Reg HPC Asia 2023 Workshops (2023) 2023; 2023:35-49. [PMID: 38197035 PMCID: PMC10773486 DOI: 10.1145/3581576.3581621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jan Stephan
- Helmholtz-Zentrum Dresden-Rossendorf, Germany
| | - René Widera
- Helmholtz-Zentrum Dresden-Rossendorf, Germany
| | | | | | | | | | | | | | | | | | | | | | | | - David Hardy
- University of Illinois at Urbana-Champaign, USA
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Trifan A, Gorgun D, Salim M, Li Z, Brace A, Zvyagin M, Ma H, Clyde A, Clark D, Hardy DJ, Burnley T, Huang L, McCalpin J, Emani M, Yoo H, Yin J, Tsaris A, Subbiah V, Raza T, Liu J, Trebesch N, Wells G, Mysore V, Gibbs T, Phillips J, Chennubhotla SC, Foster I, Stevens R, Anandkumar A, Vishwanath V, Stone JE, Tajkhorshid E, A. Harris S, Ramanathan A. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. Int J High Perform Comput Appl 2022; 36:603-623. [PMID: 38464362 PMCID: PMC10923581 DOI: 10.1177/10943420221113513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.
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Affiliation(s)
- Anda Trifan
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | - Defne Gorgun
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | | | | | | | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ian Foster
- Argonne National Laboratory
- University of Chicago
| | - Rick Stevens
- Argonne National Laboratory
- University of Chicago
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Barhaghi MS, Crawford B, Schwing G, Hardy DJ, Stone JE, Schwiebert L, Potoff J, Tajkhorshid E. py-MCMD: Python Software for Performing Hybrid Monte Carlo/Molecular Dynamics Simulations with GOMC and NAMD. J Chem Theory Comput 2022; 18:4983-4994. [PMID: 35621307 PMCID: PMC9760104 DOI: 10.1021/acs.jctc.1c00911] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
py-MCMD, an open-source Python software, provides a robust workflow layer that manages communication of relevant system information between the simulation engines NAMD and GOMC and generates coherent thermodynamic properties and trajectories for analysis. To validate the workflow and highlight its capabilities, hybrid Monte Carlo/molecular dynamics (MC/MD) simulations are performed for SPC/E water in the isobaric-isothermal (NPT) and grand canonical (GC) ensembles as well as with Gibbs ensemble Monte Carlo (GEMC). The hybrid MC/MD approach shows close agreement with reference MC simulations and has a computational efficiency that is 2 to 136 times greater than traditional Monte Carlo simulations. MC/MD simulations performed for water in a graphene slit pore illustrate significant gains in sampling efficiency when the coupled-decoupled configurational-bias MC (CD-CBMC) algorithm is used compared with simulations using a single unbiased random trial position. Simulations using CD-CBMC reach equilibrium with 25 times fewer cycles than simulations using a single unbiased random trial position, with a small increase in computational cost. In a more challenging application, hybrid grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations are used to hydrate a buried binding pocket in bovine pancreatic trypsin inhibitor. Water occupancies produced by GCMC/MD simulations are in close agreement with crystallographically identified positions, and GCMC/MD simulations have a computational efficiency that is 5 times better than MD simulations. py-MCMD is available on GitHub at https://github.com/GOMC-WSU/py-MCMD.
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Affiliation(s)
- Mohammad Soroush Barhaghi
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Brad Crawford
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Gregory Schwing
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - David J Hardy
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - John E Stone
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Loren Schwiebert
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Jeffrey Potoff
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Emad Tajkhorshid
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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Stone JE, Griffin KS, Amstutz J, DeMarle DE, Sherman WR, Gunther J. ANARI: A 3-D Rendering API Standard. Comput Sci Eng 2022; 24:7-18. [DOI: 10.1109/mcse.2022.3163151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- John E. Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
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Navratil P, Gribble C, Grosset P, Stone JE. Analytic Rendering and Hardware-Accelerated Simulation for Scientific Applications. Comput Sci Eng 2022. [DOI: 10.1109/mcse.2022.3163480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
| | | | | | - John E. Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
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8
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White V, Linardon J, Stone JE, Holmes-Truscott E, Olive L, Mikocka-Walus A, Hendrieckx C, Evans S, Speight J. Online psychological interventions to reduce symptoms of depression, anxiety, and general distress in those with chronic health conditions: a systematic review and meta-analysis of randomized controlled trials. Psychol Med 2022; 52:548-573. [PMID: 32674747 DOI: 10.1017/s0033291720002251] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Over the past 15 years, there has been substantial growth in web-based psychological interventions. We summarize evidence regarding the efficacy of web-based self-directed psychological interventions on depressive, anxiety and distress symptoms in people living with a chronic health condition. METHOD We searched Medline, PsycINFO, CINAHL, EMBASE databases and Cochrane Database from 1990 to 1 May 2019. English language papers of randomized controlled trials (usual care or waitlist control) of web-based psychological interventions with a primary or secondary aim to reduce anxiety, depression or distress in adults with a chronic health condition were eligible. Results were assessed using narrative synthases and random-effects meta-analyses. RESULTS In total 70 eligible studies across 17 health conditions [most commonly: cancer (k = 20), chronic pain (k = 9), arthritis (k = 6) and multiple sclerosis (k = 5), diabetes (k = 4), fibromyalgia (k = 4)] were identified. Interventions were based on CBT principles in 46 (66%) studies and 42 (60%) included a facilitator. When combining all chronic health conditions, web-based interventions were more efficacious than control conditions in reducing symptoms of depression g = 0.30 (95% CI 0.22-0.39), anxiety g = 0.19 (95% CI 0.12-0.27), and distress g = 0.36 (95% CI 0.23-0.49). CONCLUSION Evidence regarding effectiveness for specific chronic health conditions was inconsistent. While self-guided online psychological interventions may help to reduce symptoms of anxiety, depression and distress in people with chronic health conditions in general, it is unclear if these interventions are effective for specific health conditions. More high-quality evidence is needed before definite conclusions can be made.
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Affiliation(s)
- V White
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
| | - J Linardon
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
| | - J E Stone
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria3168, Australia
| | - E Holmes-Truscott
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
- The Australian Centre for Behavioural Research in Diabetes, Diabetes Victoria, Melbourne, Victoria3000, Australia
| | - L Olive
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
| | - A Mikocka-Walus
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
| | - C Hendrieckx
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
- The Australian Centre for Behavioural Research in Diabetes, Diabetes Victoria, Melbourne, Victoria3000, Australia
| | - S Evans
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
| | - J Speight
- School of Psychology, Faculty of Health, Deakin University Geelong, Victoria3220, Australia
- The Australian Centre for Behavioural Research in Diabetes, Diabetes Victoria, Melbourne, Victoria3000, Australia
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Hardy DJ, Isralewitz B, Stone JE, Tajkhorshid E. Lessons Learned from Responsive Molecular Dynamics Studies of the COVID-19 Virus. Proc UrgentHPC 2021 (2021) 2021; 2021:1-10. [PMID: 36573923 PMCID: PMC9788906 DOI: 10.1109/urgenthpc54802.2021.00006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Over the past 18 months, the need to perform atomic detail molecular dynamics simulations of the SARS-CoV-2 virion, its spike protein, and other structures related to the viral infection cycle has led biomedical researchers worldwide to urgently seek out all available biomolecular structure information, appropriate molecular modeling and simulation software, and the necessary computing resources to conduct their work. We describe our experiences from several COVID-19 research collaborations and the challenges they presented in terms of our molecular modeling software development and support efforts, our laboratory's local computing environment, and our scientists' use of non-traditional HPC hardware platforms such as public clouds for large scale parallel molecular dynamics simulations.
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Affiliation(s)
- David J. Hardy
- Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana, Illinois, USA
| | - Barry Isralewitz
- Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana, Illinois, USA
| | - John E. Stone
- Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana, Illinois, USA
| | - Emad Tajkhorshid
- Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana, Illinois, USA
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Casalino L, Dommer AC, Gaieb Z, Barros EP, Sztain T, Ahn SH, Trifan A, Brace A, Bogetti AT, Clyde A, Ma H, Lee H, Turilli M, Khalid S, Chong LT, Simmerling C, Hardy DJ, Maia JD, Phillips JC, Kurth T, Stern AC, Huang L, McCalpin JD, Tatineni M, Gibbs T, Stone JE, Jha S, Ramanathan A, Amaro RE. AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. Int J High Perform Comput Appl 2021; 35:432-451. [PMID: 38603008 PMCID: PMC8064023 DOI: 10.1177/10943420211006452] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
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)
- Lorenzo Casalino
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | - Abigail C Dommer
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | - Zied Gaieb
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | | | - Terra Sztain
- University of California San Diego, La Jolla, CA, USA
| | - Surl-Hee Ahn
- University of California San Diego, La Jolla, CA, USA
| | - Anda Trifan
- Argonne National Lab, Lemont, IL, USA
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | - Austin Clyde
- Argonne National Lab, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Heng Ma
- Argonne National Lab, Lemont, IL, USA
| | | | | | | | | | | | - David J Hardy
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Julio Dc Maia
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | | | - Lei Huang
- Texas Advanced Computing Center, Austin, TX, USA
| | | | | | - Tom Gibbs
- NVIDIA Corporation, Santa Clara, CA, USA
| | - John E Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Shantenu Jha
- Rutgers University, Piscataway, NJ, USA
- Brookhaven National Lab, Upton, NY, USA
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11
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Abstract
Enveloped viruses, such as SARS-CoV-2, infect cells via fusion of their envelope with the host membrane. By employing molecular simulations to characterize viral envelopes, researchers can gain insights into key determinants of infection. Here, the Frontera supercomputer is leveraged for large-scale modeling and analysis of authentic viral envelopes, whose lipid compositions are complex and realistic. Visual Molecular Dynamics (VMD) with support for MPI is employed, overcoming previous computational limitations and enabling investigation into virus biology at an unprecedented scale. The techniques applied here to an authentic HIV-1 envelope at two levels of spatial resolution (29 million particles and 280 million atoms) are broadly applicable to the study of other viruses. The authors are actively employing these techniques to develop and characterize an authentic SARS-CoV-2 envelope. A general framework for carrying out scalable analysis of simulation trajectories on Frontera is presented, expanding the utility of the machine in humanity’s ongoing fight against infectious diseases.
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13
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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: 1203] [Impact Index Per Article: 300.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Stone JE, McGlashan EM, Cain SW, Phillips AJ. 0433 Targeting Light Sensitivity Parameters to Optimize Circadian Phase Predictions. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Existing models of the human circadian clock accurately predict phase at group-level but not at individual-level. Interindividual variability in light sensitivity is not currently accounted for in these models and may be a practical approach to improving individual-level predictions. Using the gold-standard predictive model, we (i) identified whether varying light sensitivity parameters produces meaningful changes in predicted phase in field conditions; and (ii) tested whether optimizing parameters can significantly improve accuracy of circadian phase prediction.
Methods
Healthy participants (n=12, 7 women, aged 18-26) underwent continuous light and activity monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim light melatonin onset (DLMO) was measured each week. A model of the human circadian clock and its response to light was used to predict the three weekly DLMO times using the individual’s light data. A sensitivity analysis was performed varying three model parameters within physiological ranges: (i) amplitude of the light response [p]; (ii) advance vs. delay bias of the light response [K]; and (iii) intrinsic circadian period [tau]. These parameters were then fitted using least squares estimation to obtain optimal predictions of DLMO for each individual. Accuracy was compared between optimized parameters and default parameters.
Results
The default model predicted DLMO with mean absolute error of 1.02h. Sensitivity analysis showed the average range of variation in predicted DLMOs across participants was 0.65h for p, 4.28h for K and 3.26h for tau. Fitting parameters independently, we found mean absolute error of 0.85h for p, 0.71h for K and 0.75h for tau. Fitting p and K together reduced mean absolute error to 0.57h.
Conclusion
Light sensitivity parameters capture similar or greater variability in phase as intrinsic circadian period, indicating they are a viable option for individualising circadian phase predictions. Future prospective work is needed using measures of light sensitivity to validate this approach.
Support
N/A
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Affiliation(s)
- J E Stone
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, AUSTRALIA
| | - E M McGlashan
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, AUSTRALIA
| | - S W Cain
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, AUSTRALIA
| | - A J Phillips
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, AUSTRALIA
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15
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Stone JE, Cheong F, Phillips AJ. 0289 What is the Optimal Duration to Sleep in on Weekends? Sleep 2020. [DOI: 10.1093/sleep/zsaa056.286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Introduction
Most individuals in the workforce exhibit differing sleep/wake patterns between work days and weekends. Work days are typically characterized by shorter and earlier sleep. On weekends, sleep debt is repaid by sleeping later and longer, often due to evening events. While social jet-lag (the mismatch in work vs. free sleep timing) is associated with poor health outcomes, repaying sleep debt is beneficial to health. The degree to which individuals should sleep in on weekends is currently unknown.
Methods
We used a mathematical model of human sleep/wake timing, which has been validated for predicting sleep/wake patterns in a variety of field/lab conditions. Sleep timing constraints are inputs, and the model generates predicted sleep/wake patterns and alertness levels. We simulated a traditional 7-day work week, with 7am rise times on week days. Inter-individual differences in chronotype were modeled by varying intrinsic circadian period. The model was applied to two conditions: (i) free choice of sleep onset times on weekends; or (ii) late nights on weekends (2am bedtime). Weekend rise time was systematically varied to optimize predicted daytime alertness.
Results
Optimal weekend rise times varied as a function of chronotype. With free choice sleep onset times, the model predicted optimal rise time was later for late types than early types, ranging from 7:20 to 8:40am across individuals. Sleeping later than optimal was associated with poorer performance due to misaligned circadian phase. The same trend was observed in the late-night condition, but with later optimal rise times, ranging from 8:30 to 9:50am.
Conclusion
Although individuals should maintain a consistent sleep/wake pattern on all days of the week, they often do not, due to work or social commitments. Within real-world constraints, we provided the first objective recommendations for sleep timing on the weekend, finding a compromise between repaying sleep debt and avoiding circadian misalignment.
Support
N/A
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Affiliation(s)
- J E Stone
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, AUSTRALIA
| | - F Cheong
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, AUSTRALIA
| | - A J Phillips
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, AUSTRALIA
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16
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Singharoy A, Maffeo C, Delgado-Magnero KH, Swainsbury DJK, Sener M, Kleinekathöfer U, Vant JW, Nguyen J, Hitchcock A, Isralewitz B, Teo I, Chandler DE, Stone JE, Phillips JC, Pogorelov TV, Mallus MI, Chipot C, Luthey-Schulten Z, Tieleman DP, Hunter CN, Tajkhorshid E, Aksimentiev A, Schulten K. Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism. Cell 2020; 179:1098-1111.e23. [PMID: 31730852 DOI: 10.1016/j.cell.2019.10.021] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 09/04/2019] [Accepted: 10/21/2019] [Indexed: 01/01/2023]
Abstract
We report a 100-million atom-scale model of an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium, that reveals the cascade of energy conversion steps culminating in the generation of ATP from sunlight. Molecular dynamics simulations of this vesicle elucidate how the integral membrane complexes influence local curvature to tune photoexcitation of pigments. Brownian dynamics of small molecules within the chromatophore probe the mechanisms of directional charge transport under various pH and salinity conditions. Reproducing phenotypic properties from atomistic details, a kinetic model evinces that low-light adaptations of the bacterium emerge as a spontaneous outcome of optimizing the balance between the chromatophore's structural integrity and robust energy conversion. Parallels are drawn with the more universal mitochondrial bioenergetic machinery, from whence molecular-scale insights into the mechanism of cellular aging are inferred. Together, our integrative method and spectroscopic experiments pave the way to first-principles modeling of whole living cells.
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Affiliation(s)
- Abhishek Singharoy
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University at Tempe, Tempe, AZ 85282, USA.
| | - Christopher Maffeo
- Department of Physics, NSF Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Karelia H Delgado-Magnero
- Centre for Molecular Simulation and Department of Biological Sciences, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - David J K Swainsbury
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield S10 2TN, UK
| | - Melih Sener
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Ulrich Kleinekathöfer
- Department of Physics and Earth Sciences, Jacobs University Bremen, 28759 Bremen, Germany
| | - John W Vant
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University at Tempe, Tempe, AZ 85282, USA
| | - Jonathan Nguyen
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University at Tempe, Tempe, AZ 85282, USA
| | - Andrew Hitchcock
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield S10 2TN, UK
| | - Barry Isralewitz
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Ivan Teo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Danielle E Chandler
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - John E Stone
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - James C Phillips
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Taras V Pogorelov
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Department of Chemistry, School of Chemical Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - M Ilaria Mallus
- Department of Physics and Earth Sciences, Jacobs University Bremen, 28759 Bremen, Germany
| | - Christophe Chipot
- Department of Physics, NSF Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Laboratoire International Associé CNRS-UIUC, UMR 7019, Université de Lorraine, 54506 Vandœuvre-lès-Nancy, France
| | - Zaida Luthey-Schulten
- Department of Physics, NSF Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Department of Chemistry, School of Chemical Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - D Peter Tieleman
- Centre for Molecular Simulation and Department of Biological Sciences, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - C Neil Hunter
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield S10 2TN, UK.
| | - Emad Tajkhorshid
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Departments of Biochemistry, Chemistry, Bioengineering, and Pharmacology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - Aleksei Aksimentiev
- Department of Physics, NSF Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - Klaus Schulten
- Department of Physics, NSF Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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17
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Sener M, Levy S, Stone JE, Christensen AJ, Isralewitz B, Patterson R, Borkiewicz K, Carpenter J, Hunter CN, Luthey-Schulten Z, Cox D. Multiscale modeling and cinematic visualization of photosynthetic energy conversion processes from electronic to cell scales. Parallel Comput 2020; 102:102698. [PMID: 34824485 PMCID: PMC8612599 DOI: 10.1016/j.parco.2020.102698] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Conversion of sunlight into chemical energy, namely photosynthesis, is the primary energy source of life on Earth. A visualization depicting this process, based on multiscale computational models from electronic to cell scales, is presented in the form of an excerpt from the fulldome show Birth of Planet Earth. This accessible visual narrative shows a lay audience, including children, how the energy of sunlight is captured, converted, and stored through a chain of proteins to power living cells. The visualization is the result of a multi-year collaboration among biophysicists, visualization scientists, and artists, which, in turn, is based on a decade-long experimental-computational collaboration on structural and functional modeling that produced an atomic detail description of a bacterial bioenergetic organelle, the chromatophore. Software advancements necessitated by this project have led to significant performance and feature advances, including hardware-accelerated cinematic ray tracing and instanced visualizations for efficient cell-scale modeling. The energy conversion steps depicted feature an integration of function from electronic to cell levels, spanning nearly 12 orders of magnitude in time scales. This atomic detail description uniquely enables a modern retelling of one of humanity's earliest stories-the interplay between light and life.
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Affiliation(s)
- Melih Sener
- Beckman Institute, University of Illinois at Urbana-Champaign
| | - Stuart Levy
- Advanced Visualization Laboratory, NCSA, University of Illinois at Urbana-Champaign
| | - John E. Stone
- Beckman Institute, University of Illinois at Urbana-Champaign
| | - AJ Christensen
- Advanced Visualization Laboratory, NCSA, University of Illinois at Urbana-Champaign
| | | | - Robert Patterson
- Advanced Visualization Laboratory, NCSA, University of Illinois at Urbana-Champaign
| | - Kalina Borkiewicz
- Advanced Visualization Laboratory, NCSA, University of Illinois at Urbana-Champaign
| | - Jeffrey Carpenter
- Advanced Visualization Laboratory, NCSA, University of Illinois at Urbana-Champaign
| | - C. Neil Hunter
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, U.K
| | | | - Donna Cox
- Beckman Institute, University of Illinois at Urbana-Champaign
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18
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Bryer AJ, Hadden-Perilla JA, Stone JE, Perilla JR. High-Performance Analysis of Biomolecular Containers to Measure Small-Molecule Transport, Transbilayer Lipid Diffusion, and Protein Cavities. J Chem Inf Model 2019; 59:4328-4338. [PMID: 31525965 PMCID: PMC6817393 DOI: 10.1021/acs.jcim.9b00324] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
![]()
Compartmentalization is a central
theme in biology. Cells are composed
of numerous membrane-enclosed structures, evolved to facilitate specific
biochemical processes; viruses act as containers of genetic material,
optimized to drive infection. Molecular dynamics simulations provide
a mechanism to study biomolecular containers and the influence they
exert on their environments; however, trajectory analysis software
generally lacks knowledge of container interior versus exterior. Further,
many relevant container analyses involve large-scale particle tracking
endeavors, which may become computationally prohibitive with increasing
system size. Here, a novel method based on 3-D ray casting is presented,
which rapidly classifies the space surrounding biomolecular containers
of arbitrary shape, enabling fast determination of the identities
and counts of particles (e.g., solvent molecules) found inside and
outside. The method is broadly applicable to the study of containers
and enables high-performance characterization of properties such as
solvent density, small-molecule transport, transbilayer lipid diffusion,
and topology of protein cavities. The method is implemented in VMD,
a widely used simulation analysis tool that supports personal computers,
clouds, and parallel supercomputers, including ORNL’s Summit
and Titan and NCSA’s Blue Waters, where the method can be employed
to efficiently analyze trajectories encompassing millions of particles.
The ability to rapidly characterize the spatial relationships of particles
relative to a biomolecular container over many trajectory frames,
irrespective of large particle counts, enables analysis of containers
on a scale that was previously unfeasible, at a level of accuracy
that was previously unattainable.
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Affiliation(s)
- Alexander J Bryer
- Department of Chemistry and Biochemistry , University of Delaware , Newark , Delaware 19716 , United States
| | - Jodi A Hadden-Perilla
- Department of Chemistry and Biochemistry , University of Delaware , Newark , Delaware 19716 , United States
| | - John E Stone
- Beckman Institute for Advanced Science and Technology , University of Illinois at Urbana-Champaign , Urbana , Illinois 61801 , United States
| | - Juan R Perilla
- Department of Chemistry and Biochemistry , University of Delaware , Newark , Delaware 19716 , United States
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19
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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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20
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Chandrasekaran S, Juckeland G, Lin M, Otten M, Pleiter D, Stone JE, Lucio-Vega J, Zingale M, Foertter F. Best Practices in Running Collaborative GPU Hackathons: Advancing Scientific Applications with a Sustained Impact. Comput Sci Eng 2018. [DOI: 10.1109/mcse.2018.042781332] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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21
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Ganesan S, Magee M, Stone JE, Mulhall MD, Collins A, Howard M, Lockley SW, Rajaratnam S, Sletten TL. 0175 Shift Work and its Impact on Sleep, Alertness and Performance in Intensive Care Health Workers. Sleep 2018. [DOI: 10.1093/sleep/zsy061.174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- S Ganesan
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
| | - M Magee
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
| | - J E Stone
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
| | - M D Mulhall
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
| | - A Collins
- Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, AUSTRALIA
| | - M Howard
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
- Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, AUSTRALIA
| | - S W Lockley
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA
| | - S Rajaratnam
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA
| | - T L Sletten
- Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, AUSTRALIA
- School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Victoria, AUSTRALIA
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22
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Earnest TM, Watanabe R, Stone JE, Mahamid J, Baumeister W, Villa E, Luthey-Schulten Z. Challenges of Integrating Stochastic Dynamics and Cryo-Electron Tomograms in Whole-Cell Simulations. J Phys Chem B 2017; 121:3871-3881. [PMID: 28291359 DOI: 10.1021/acs.jpcb.7b00672] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Cryo-electron tomography (cryo-ET) has rapidly emerged as a powerful tool to investigate the internal, three-dimensional spatial organization of the cell. In parallel, the GPU-based technology to perform spatially resolved stochastic simulations of whole cells has arisen, allowing the simulation of complex biochemical networks over cell cycle time scales using data taken from -omics, single molecule experiments, and in vitro kinetics. By using real cell geometry derived from cryo-ET data, we have the opportunity to imbue these highly detailed structural data-frozen in time-with realistic biochemical dynamics and investigate how cell structure affects the behavior of the embedded chemical reaction network. Here we present two examples to illustrate the challenges and techniques involved in integrating structural data into stochastic simulations. First, a tomographic reconstruction of Saccharomyces cerevisiae is used to construct the geometry of an entire cell through which a simple stochastic model of an inducible genetic switch is studied. Second, a tomogram of the nuclear periphery in a HeLa cell is converted directly to the simulation geometry through which we study the effects of cellular substructure on the stochastic dynamics of gene repression. These simple chemical models allow us to illustrate how to build whole-cell simulations using cryo-ET derived geometry and the challenges involved in such a process.
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Affiliation(s)
- Tyler M Earnest
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign , Urbana, Illinois, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign , Urbana, Illinois, United States
| | - Reika Watanabe
- Department of Chemistry and Biochemistry, University of California , San Diego, California, United States
| | - John E Stone
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign , Urbana, Illinois, United States
| | - Julia Mahamid
- Department of Molecular Structural Biology, Max Planck Institute of Biochemistry , Munich, Germany
| | - Wolfgang Baumeister
- Department of Molecular Structural Biology, Max Planck Institute of Biochemistry , Munich, Germany
| | - Elizabeth Villa
- Department of Chemistry and Biochemistry, University of California , San Diego, California, United States
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign , Urbana, Illinois, United States.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign , Urbana, Illinois, United States
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23
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Singharoy A, Teo I, McGreevy R, Stone JE, Zhao J, Schulten K. Molecular dynamics-based refinement and validation for sub-5 Å cryo-electron microscopy maps. eLife 2016; 5. [PMID: 27383269 PMCID: PMC4990421 DOI: 10.7554/elife.16105] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 07/06/2016] [Indexed: 12/12/2022] Open
Abstract
Two structure determination methods, based on the molecular dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or ensembles of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria based on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services. DOI:http://dx.doi.org/10.7554/eLife.16105.001 To understand the roles that proteins and other large molecules play inside cells, it is important to determine their structures. One of the techniques that researchers can use to do this is called cryo-electron microscopy (cryo-EM), which rapidly freezes molecules to fix them in position before imaging them in fine detail. The cryo-EM images are like maps that show the approximate position of atoms. These images must then be processed in order to build a three-dimensional model of the protein that shows how its atoms are arranged relative to each other. One computational approach called Molecular Dynamics Flexible Fitting (MDFF) works by flexibly fitting possible atomic structures into cryo-EM maps. Although this approach works well with relatively undetailed (or ‘low resolution’) cryo-EM images, it struggles to handle the high-resolution cryo-EM maps now being generated. Singharoy, Teo, McGreevy et al. have now developed two MDFF methods – called cascade MDFF and resolution exchange MDFF – that help to resolve atomic models of biological molecules from cryo-EM images. Each method can refine poorly guessed models into ones that are consistent with the high-resolution experimental images. The refinement is achieved by interpreting a range of images that starts with a ‘fuzzy’ image. The contrast of the image is then progressively improved until an image is produced that has a resolution that is good enough to almost distinguish individual atoms. The method works because each cryo-EM image shows not just one, but a collection of atomic structures that the molecule can take on, with the fuzzier parts of the image representing the more flexible parts of the molecule. By taking into account this flexibility, the large-scale features of the protein structure can be determined first from the fuzzier images, and increasing the contrast of the images allows smaller-scale refinements to be made to the structure. The MDFF tools have been designed to be easy to use and are available to researchers at low cost through cloud computing platforms. They can now be used to unravel the structure of many different proteins and protein complexes including those involved in photosynthesis, respiration and protein synthesis. DOI:http://dx.doi.org/10.7554/eLife.16105.002
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Affiliation(s)
- Abhishek Singharoy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States
| | - Ivan Teo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
| | - Ryan McGreevy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States
| | - John E Stone
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States
| | - Jianhua Zhao
- Department of Biochemistry and Biophysics, University of California San Francisco School of Medicine, San Francisco, United States
| | - Klaus Schulten
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
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24
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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 Comput 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] [What about the content of this article? (0)] [Affiliation(s)] [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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25
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Hardy DJ, Wu Z, Phillips JC, Stone JE, Skeel RD, Schulten K. Multilevel summation method for electrostatic force evaluation. J Chem Theory Comput 2016; 11:766-79. [PMID: 25691833 PMCID: PMC4325600 DOI: 10.1021/ct5009075] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Indexed: 12/18/2022]
Abstract
![]()
The
multilevel summation method (MSM) offers an efficient algorithm
utilizing convolution for evaluating long-range forces arising in
molecular dynamics simulations. Shifting the balance of computation
and communication, MSM provides key advantages over the ubiquitous
particle–mesh Ewald (PME) method, offering better scaling on
parallel computers and permitting more modeling flexibility, with
support for periodic systems as does PME but also for semiperiodic
and nonperiodic systems. The version of MSM available in the simulation
program NAMD is described, and its performance and accuracy are compared
with the PME method. The accuracy feasible for MSM in practical applications
reproduces PME results for water property calculations of density,
diffusion constant, dielectric constant, surface tension, radial distribution
function, and distance-dependent Kirkwood factor, even though the
numerical accuracy of PME is higher than that of MSM. Excellent agreement
between MSM and PME is found also for interface potentials of air–water
and membrane–water interfaces, where long-range Coulombic interactions
are crucial. Applications demonstrate also the suitability of MSM
for systems with semiperiodic and nonperiodic boundaries. For this
purpose, simulations have been performed with periodic boundaries
along directions parallel to a membrane surface but not along the
surface normal, yielding membrane pore formation induced by an imbalance
of charge across the membrane. Using a similar semiperiodic boundary
condition, ion conduction through a graphene nanopore driven by an
ion gradient has been simulated. Furthermore, proteins have been simulated
inside a single spherical water droplet. Finally, parallel scalability
results show the ability of MSM to outperform PME when scaling a system
of modest size (less than 100 K atoms) to over a thousand processors,
demonstrating the suitability of MSM for large-scale parallel simulation.
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Affiliation(s)
- David J Hardy
- Beckman Institute, University of Illinois at Urbana−Champaign, 405 North Mathews Avenue, Urbana, Illinois 61801, United States
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26
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Vermaas JV, Hardy DJ, Stone JE, Tajkhorshid E, Kohlmeyer A. TopoGromacs: Automated Topology Conversion from CHARMM to GROMACS within VMD. J Chem Inf Model 2016; 56:1112-6. [PMID: 27196035 DOI: 10.1021/acs.jcim.6b00103] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Molecular dynamics (MD) simulation engines use a variety of different approaches for modeling molecular systems with force fields that govern their dynamics and describe their topology. These different approaches introduce incompatibilities between engines, and previously published software bridges the gaps between many popular MD packages, such as between CHARMM and AMBER or GROMACS and LAMMPS. While there are many structure building tools available that generate topologies and structures in CHARMM format, only recently have mechanisms been developed to convert their results into GROMACS input. We present an approach to convert CHARMM-formatted topology and parameters into a format suitable for simulation with GROMACS by expanding the functionality of TopoTools, a plugin integrated within the widely used molecular visualization and analysis software VMD. The conversion process was diligently tested on a comprehensive set of biological molecules in vacuo. The resulting comparison between energy terms shows that the translation performed was lossless as the energies were unchanged for identical starting configurations. By applying the conversion process to conventional benchmark systems that mimic typical modestly sized MD systems, we explore the effect of the implementation choices made in CHARMM, NAMD, and GROMACS. The newly available automatic conversion capability breaks down barriers between simulation tools and user communities and allows users to easily compare simulation programs and leverage their unique features without the tedium of constructing a topology twice.
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Affiliation(s)
- Josh V Vermaas
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States.,Department of Biochemistry, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States.,Beckman Insitute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - David J Hardy
- Beckman Insitute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - John E Stone
- Beckman Insitute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - Emad Tajkhorshid
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States.,Department of Biochemistry, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States.,Beckman Insitute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - Axel Kohlmeyer
- Insitute for Computational Molecular Science, Temple University , Philadelphia, Pennsylvania 19122, United States
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27
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Stone JE, Messmer P, Sisneros R, Schulten K. High Performance Molecular Visualization: In-Situ and Parallel Rendering with EGL. IEEE Int Symp Parallel Distrib Process Workshops Phd Forum 2016; 2016:1014-1023. [PMID: 27747137 PMCID: PMC5061511 DOI: 10.1109/ipdpsw.2016.127] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Large scale molecular dynamics simulations produce terabytes of data that is impractical to transfer to remote facilities. It is therefore necessary to perform visualization tasks in-situ as the data are generated, or by running interactive remote visualization sessions and batch analyses co-located with direct access to high performance storage systems. A significant challenge for deploying visualization software within clouds, clusters, and supercomputers involves the operating system software required to initialize and manage graphics acceleration hardware. Recently, it has become possible for applications to use the Embedded-system Graphics Library (EGL) to eliminate the requirement for windowing system software on compute nodes, thereby eliminating a significant obstacle to broader use of high performance visualization applications. We outline the potential benefits of this approach in the context of visualization applications used in the cloud, on commodity clusters, and supercomputers. We discuss the implementation of EGL support in VMD, a widely used molecular visualization application, and we outline benefits of the approach for molecular visualization tasks on petascale computers, clouds, and remote visualization servers. We then provide a brief evaluation of the use of EGL in VMD, with tests using developmental graphics drivers on conventional workstations and on Amazon EC2 G2 GPU-accelerated cloud instance types. We expect that the techniques described here will be of broad benefit to many other visualization applications.
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Affiliation(s)
- John E Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL;
| | - Peter Messmer
- NVIDIA, Developer Technology Group, Zurich, Switzerland;
| | - Robert Sisneros
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL;
| | - Klaus Schulten
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL;
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28
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Stone JE, Sherman WR, Schulten K. Immersive Molecular Visualization with Omnidirectional Stereoscopic Ray Tracing and Remote Rendering. IEEE Int Symp Parallel Distrib Process Workshops Phd Forum 2016; 2016:1048-1057. [PMID: 27747138 PMCID: PMC5063251 DOI: 10.1109/ipdpsw.2016.121] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Immersive molecular visualization provides the viewer with intuitive perception of complex structures and spatial relationships that are of critical interest to structural biologists. The recent availability of commodity head mounted displays (HMDs) provides a compelling opportunity for widespread adoption of immersive visualization by molecular scientists, but HMDs pose additional challenges due to the need for low-latency, high-frame-rate rendering. State-of-the-art molecular dynamics simulations produce terabytes of data that can be impractical to transfer from remote supercomputers, necessitating routine use of remote visualization. Hardware-accelerated video encoding has profoundly increased frame rates and image resolution for remote visualization, however round-trip network latencies would cause simulator sickness when using HMDs. We present a novel two-phase rendering approach that overcomes network latencies with the combination of omnidirectional stereoscopic progressive ray tracing and high performance rasterization, and its implementation within VMD, a widely used molecular visualization and analysis tool. The new rendering approach enables immersive molecular visualization with rendering techniques such as shadows, ambient occlusion lighting, depth-of-field, and high quality transparency, that are particularly helpful for the study of large biomolecular complexes. We describe ray tracing algorithms that are used to optimize interactivity and quality, and we report key performance metrics of the system. The new techniques can also benefit many other application domains.
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Affiliation(s)
- John E. Stone
- Beckman Institute, University of Illinois at Urbana-Champaign,
Urbana, IL, USA
| | - William R. Sherman
- Pervasive Technology Institute, Indiana University, Bloomington,
IN, USA
| | - Klaus Schulten
- Department of Physics, University of Illinois at Urbana-Champaign,
Urbana, IL, USA
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29
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Stone JE, Hallock MJ, Phillips JC, Peterson JR, Luthey-Schulten Z, Schulten K. Evaluation of Emerging Energy-Efficient Heterogeneous Computing Platforms for Biomolecular and Cellular Simulation Workloads. IEEE Int Symp Parallel Distrib Process Workshops Phd Forum 2016; 2016:89-100. [PMID: 27516922 DOI: 10.1109/ipdpsw.2016.130] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Many of the continuing scientific advances achieved through computational biology are predicated on the availability of ongoing increases in computational power required for detailed simulation and analysis of cellular processes on biologically-relevant timescales. A critical challenge facing the development of future exascale supercomputer systems is the development of new computing hardware and associated scientific applications that dramatically improve upon the energy efficiency of existing solutions, while providing increased simulation, analysis, and visualization performance. Mobile computing platforms have recently become powerful enough to support interactive molecular visualization tasks that were previously only possible on laptops and workstations, creating future opportunities for their convenient use for meetings, remote collaboration, and as head mounted displays for immersive stereoscopic viewing. We describe early experiences adapting several biomolecular simulation and analysis applications for emerging heterogeneous computing platforms that combine power-efficient system-on-chip multi-core CPUs with high-performance massively parallel GPUs. We present low-cost power monitoring instrumentation that provides sufficient temporal resolution to evaluate the power consumption of individual CPU algorithms and GPU kernels. We compare the performance and energy efficiency of scientific applications running on emerging platforms with results obtained on traditional platforms, identify hardware and algorithmic performance bottlenecks that affect the usability of these platforms, and describe avenues for improving both the hardware and applications in pursuit of the needs of molecular modeling tasks on mobile devices and future exascale computers.
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Affiliation(s)
- John E Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Michael J Hallock
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - James C Phillips
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Joseph R Peterson
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Klaus Schulten
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
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30
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Cabezas J, Gelado I, Stone JE, Navarro N, Kirk DB, Hwu WM. Runtime and Architecture Support for Efficient Data Exchange in Multi-Accelerator Applications. IEEE Trans Parallel Distrib Syst 2015; 26:1405-1418. [PMID: 26180487 PMCID: PMC4500157 DOI: 10.1109/tpds.2014.2316825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Heterogeneous parallel computing applications often process large data sets that require multiple GPUs to jointly meet their needs for physical memory capacity and compute throughput. However, the lack of high-level abstractions in previous heterogeneous parallel programming models force programmers to resort to multiple code versions, complex data copy steps and synchronization schemes when exchanging data between multiple GPU devices, which results in high software development cost, poor maintainability, and even poor performance. This paper describes the HPE runtime system, and the associated architecture support, which enables a simple, efficient programming interface for exchanging data between multiple GPUs through either interconnects or cross-node network interfaces. The runtime and architecture support presented in this paper can also be used to support other types of accelerators. We show that the simplified programming interface reduces programming complexity. The research presented in this paper started in 2009. It has been implemented and tested extensively in several generations of HPE runtime systems as well as adopted into the NVIDIA GPU hardware and drivers for CUDA 4.0 and beyond since 2011. The availability of real hardware that support key HPE features gives rise to a rare opportunity for studying the effectiveness of the hardware support by running important benchmarks on real runtime and hardware. Experimental results show that in a exemplar heterogeneous system, peer DMA and double-buffering, pinned buffers, and software techniques can improve the inter-accelerator data communication bandwidth by 2×. They can also improve the execution speed by 1.6× for a 3D finite difference, 2.5× for 1D FFT, and 1.6× for merge sort, all measured on real hardware. The proposed architecture support enables the HPE runtime to transparently deploy these optimizations under simple portable user code, allowing system designers to freely employ devices of different capabilities. We further argue that simple interfaces such as HPE are needed for most applications to benefit from advanced hardware features in practice.
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Affiliation(s)
- Javier Cabezas
- Department of Computer Science, Barcelona Supercomputing Center and with the Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Isaac Gelado
- NVIDIA Corporation at Santa Clara, California, United States
| | - John E. Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Nacho Navarro
- Department of Computer Science, Barcelona Supercomputing Center and with the Universitat Politècnica de Catalunya, Barcelona, Spain
| | - David B. Kirk
- NVIDIA Corporation at Santa Clara, California, United States
| | - Wen-mei Hwu
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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31
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Cai E, Ge P, Lee SH, Jeyifous O, Wang Y, Liu Y, Wilson KM, Lim SJ, Baird MA, Stone JE, Lee KY, Davidson MW, Chung HJ, Schulten K, Smith AM, Green WN, Selvin PR. Stable Small Quantum Dots for Synaptic Receptor Tracking on Live Neurons. Angew Chem Int Ed Engl 2014. [DOI: 10.1002/ange.201405735] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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32
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Cai E, Ge P, Lee SH, Jeyifous O, Wang Y, Liu Y, Wilson KM, Lim SJ, Baird MA, Stone JE, Lee KY, Davidson MW, Chung HJ, Schulten K, Smith AM, Green WN, Selvin PR. Stable small quantum dots for synaptic receptor tracking on live neurons. Angew Chem Int Ed Engl 2014; 53:12484-8. [PMID: 25255882 DOI: 10.1002/anie.201405735] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 08/13/2014] [Indexed: 11/06/2022]
Abstract
We developed a coating method to produce functionalized small quantum dots (sQDs), about 9 nm in diameter, that were stable for over a month. We made sQDs in four emission wavelengths, from 527 to 655 nm and with different functional groups. AMPA receptors on live neurons were labeled with sQDs and postsynaptic density proteins were visualized with super-resolution microscopy. Their diffusion behavior indicates that sQDs access the synaptic clefts significantly more often than commercial QDs.
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Affiliation(s)
- En Cai
- Department of Physics and Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, 1110 W Green St., Urbana, IL 61801 (USA)
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Stone JE, McGreevy R, Isralewitz B, Schulten K. GPU-accelerated analysis and visualization of large structures solved by molecular dynamics flexible fitting. Faraday Discuss 2014; 169:265-83. [PMID: 25340325 DOI: 10.1039/c4fd00005f] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Hybrid structure fitting methods combine data from cryo-electron microscopy and X-ray crystallography with molecular dynamics simulations for the determination of all-atom structures of large biomolecular complexes. Evaluating the quality-of-fit obtained from hybrid fitting is computationally demanding, particularly in the context of a multiplicity of structural conformations that must be evaluated. Existing tools for quality-of-fit analysis and visualization have previously targeted small structures and are too slow to be used interactively for large biomolecular complexes of particular interest today such as viruses or for long molecular dynamics trajectories as they arise in protein folding. We present new data-parallel and GPU-accelerated algorithms for rapid interactive computation of quality-of-fit metrics linking all-atom structures and molecular dynamics trajectories to experimentally-determined density maps obtained from cryo-electron microscopy or X-ray crystallography. We evaluate the performance and accuracy of the new quality-of-fit analysis algorithms vis-à-vis existing tools, examine algorithm performance on GPU-accelerated desktop workstations and supercomputers, and describe new visualization techniques for results of hybrid structure fitting methods.
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Affiliation(s)
- John E Stone
- Beckman Institute, University of Illinois, 405 N. Mathews Ave, Urbana, IL, USA
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Chavent M, Reddy T, Goose J, Dahl ACE, Stone JE, Jobard B, Sansom MSP. Methodologies for the analysis of instantaneous lipid diffusion in MD simulations of large membrane systems. Faraday Discuss 2014; 169:455-75. [PMID: 25341001 PMCID: PMC4208077 DOI: 10.1039/c3fd00145h] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Interactions between lipids and membrane proteins play a key role in determining the nanoscale dynamic and structural properties of biological membranes. Molecular dynamics (MD) simulations provide a valuable tool for studying membrane models, complementing experimental approaches. It is now possible to simulate large membrane systems, such as simplified models of bacterial and viral envelope membranes. Consequently, there is a pressing need to develop tools to visualize and quantify the dynamics of these immense systems, which typically comprise millions of particles. To tackle this issue, we have developed visual and quantitative analyses of molecular positions and their velocity field using path line, vector field and streamline techniques. This allows us to highlight large, transient flow-like movements of lipids and to better understand crowding within the lipid bilayer. The current study focuses on visualization and analysis of lipid dynamics. However, the methods are flexible and can be readily applied to e.g. proteins and nanoparticles within large complex membranes. The protocols developed here are readily accessible both as a plugin for the molecular visualization program VMD and as a module for the MDAnalysis library.
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Affiliation(s)
- Matthieu Chavent
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, United Kingdom.
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Hallock MJ, Stone JE, Roberts E, Fry C, Luthey-Schulten Z. Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations. Parallel Comput 2014; 40:86-99. [PMID: 24882911 PMCID: PMC4039640 DOI: 10.1016/j.parco.2014.03.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.
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Affiliation(s)
- Michael J. Hallock
- School of Chemical Sciences, University of Illinois at Urbana-Champaign, 600 S. Mathews Ave., Urbana, IL 61801
| | - John E. Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL 61801
| | - Elijah Roberts
- Department of Biophysics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218
| | - Corey Fry
- Department of Chemistry, University of Illinois at Urbana-Champaign, 600 S. Mathews Ave., Urbana, IL 61801
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, 600 S. Mathews Ave., Urbana, IL 61801
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Liu Y, Stone JE, Cai E, Fei J, Lee SH, Park S, Ha T, Selvin PR, Schulten K. VMD as a Software for Visualization and Quantitative Analysis of Super Resolution Imaging and Single Particle Tracking. Biophys J 2014. [DOI: 10.1016/j.bpj.2013.11.1187] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Roberts E, Stone JE, Luthey-Schulten Z. Lattice Microbes: high-performance stochastic simulation method for the reaction-diffusion master equation. J Comput Chem 2012; 34:245-55. [PMID: 23007888 DOI: 10.1002/jcc.23130] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Revised: 08/13/2012] [Accepted: 08/31/2012] [Indexed: 11/09/2022]
Abstract
Spatial stochastic simulation is a valuable technique for studying reactions in biological systems. With the availability of high-performance computing (HPC), the method is poised to allow integration of data from structural, single-molecule and biochemical studies into coherent computational models of cells. Here, we introduce the Lattice Microbes software package for simulating such cell models on HPC systems. The software performs either well-stirred or spatially resolved stochastic simulations with approximated cytoplasmic crowding in a fast and efficient manner. Our new algorithm efficiently samples the reaction-diffusion master equation using NVIDIA graphics processing units and is shown to be two orders of magnitude faster than exact sampling for large systems while maintaining an accuracy of !0.1%. Display of cell models and animation of reaction trajectories involving millions of molecules is facilitated using a plug-in to the popular VMD visualization platform. The Lattice Microbes software is open source and available for download at http://www.scs.illinois.edu/schulten/lm
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Affiliation(s)
- Elijah Roberts
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Levine BG, Stone JE, Kohlmeyer A. Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units-Radial Distribution Function Histogramming. J Comput Phys 2011; 230:3556-3569. [PMID: 21547007 PMCID: PMC3085256 DOI: 10.1016/j.jcp.2011.01.048] [Citation(s) in RCA: 110] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
The calculation of radial distribution functions (RDFs) from molecular dynamics trajectory data is a common and computationally expensive analysis task. The rate limiting step in the calculation of the RDF is building a histogram of the distance between atom pairs in each trajectory frame. Here we present an implementation of this histogramming scheme for multiple graphics processing units (GPUs). The algorithm features a tiling scheme to maximize the reuse of data at the fastest levels of the GPU's memory hierarchy and dynamic load balancing to allow high performance on heterogeneous configurations of GPUs. Several versions of the RDF algorithm are presented, utilizing the specific hardware features found on different generations of GPUs. We take advantage of larger shared memory and atomic memory operations available on state-of-the-art GPUs to accelerate the code significantly. The use of atomic memory operations allows the fast, limited-capacity on-chip memory to be used much more efficiently, resulting in a fivefold increase in performance compared to the version of the algorithm without atomic operations. The ultimate version of the algorithm running in parallel on four NVIDIA GeForce GTX 480 (Fermi) GPUs was found to be 92 times faster than a multithreaded implementation running on an Intel Xeon 5550 CPU. On this multi-GPU hardware, the RDF between two selections of 1,000,000 atoms each can be calculated in 26.9 seconds per frame. The multi-GPU RDF algorithms described here are implemented in VMD, a widely used and freely available software package for molecular dynamics visualization and analysis.
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Affiliation(s)
- Benjamin G Levine
- Institute for Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia, PA
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Stone JE, Hardy DJ, Ufimtsev IS, Schulten K. GPU-accelerated molecular modeling coming of age. J Mol Graph Model 2010; 29:116-25. [PMID: 20675161 PMCID: PMC2934899 DOI: 10.1016/j.jmgm.2010.06.010] [Citation(s) in RCA: 210] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2010] [Revised: 06/24/2010] [Accepted: 06/30/2010] [Indexed: 12/19/2022]
Abstract
Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks.
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Affiliation(s)
- John E. Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, 405N. Mathews Ave., Urbana, IL, 61801
| | - David J. Hardy
- Beckman Institute, University of Illinois at Urbana-Champaign, 405N. Mathews Ave., Urbana, IL, 61801
| | - Ivan S. Ufimtsev
- Department of Chemistry, Stanford University, 333 Campus Drive, Stanford, CA 94305
| | - Klaus Schulten
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 W. Green, Urbana, IL, 61801
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Abstract
We provide an overview of the key architectural features of recent microprocessor designs and describe the programming model and abstractions provided by OpenCL, a new parallel programming standard targeting these architectures.
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Affiliation(s)
- John E Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
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Gelado I, Stone JE, Cabezas J, Patel S, Navarro N, Hwu WMW. An asymmetric distributed shared memory model for heterogeneous parallel systems. ACTA ACUST UNITED AC 2010. [DOI: 10.1145/1735970.1736059] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Heterogeneous computing combines general purpose CPUs with accelerators to efficiently execute both sequential control-intensive and data-parallel phases of applications. Existing programming models for heterogeneous computing rely on programmers to explicitly manage data transfers between the CPU system memory and accelerator memory.
This paper presents a new programming model for heterogeneous computing, called Asymmetric Distributed Shared Memory (ADSM), that maintains a shared logical memory space for CPUs to access objects in the accelerator physical memory but not vice versa. The asymmetry allows light-weight implementations that avoid common pitfalls of symmetrical distributed shared memory systems. ADSM allows programmers to assign data objects to performance critical methods. When a method is selected for accelerator execution, its associated data objects are allocated within the shared logical memory space, which is hosted in the accelerator physical memory and transparently accessible by the methods executed on CPUs.
We argue that ADSM reduces programming efforts for heterogeneous computing systems and enhances application portability. We present a software implementation of ADSM, called GMAC, on top of CUDA in a GNU/Linux environment. We show that applications written in ADSM and running on top of GMAC achieve performance comparable to their counterparts using programmer-managed data transfers. This paper presents the GMAC system and evaluates different design choices. We further suggest additional architectural support that will likely allow GMAC to achieve higher application performance than the current CUDA model.
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Affiliation(s)
- Isaac Gelado
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | | | | | - Sanjay Patel
- University of Illinois, Urbana-Champaign, IL, USA
| | - Nacho Navarro
- Universitat Politecnica de Catalunya, Barcelona, Spain
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Stone JE, Kohlmeyer A, Vandivort KL, Schulten K. Immersive Molecular Visualization and Interactive Modeling with Commodity Hardware. Advances in Visual Computing 2010. [DOI: 10.1007/978-3-642-17274-8_38] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Cross S, Kuttel MM, Stone JE, Gain JE. Visualisation of cyclic and multi-branched molecules with VMD. J Mol Graph Model 2009; 28:131-9. [PMID: 19473861 DOI: 10.1016/j.jmgm.2009.04.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2009] [Revised: 04/23/2009] [Accepted: 04/24/2009] [Indexed: 11/27/2022]
Abstract
We report the addition of two visualisation algorithms, termed PaperChain and Twister, to the freely available Visual Molecular Dynamics (VMD) package. These algorithms produce visualisations of complex cyclic molecules and multi-branched polysaccharides and are a generalization and optimization of those we previously developed in a standalone package for carbohydrates. PaperChain highlights each ring in a molecular structure with a polygon, which is coloured according to the ring pucker. Twister traces glycosidic bonds with a ribbon that twists according to the relative orientation of successive sugar residues. Combination of these novel algorithms and new ring selection statements with the large set of visualisations already available in VMD allows for unprecedented flexibility in the level of detail displayed for glycoconjugate, glycoprotein and carbohydrate-binding protein structures, as well as other cyclic structures. We highlight the efficacy of these algorithms with selected illustrative examples, clearly demonstrating the value of the new visualisations, not only for structure validation, but also for facilitating insights into molecular structure and mechanism.
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Affiliation(s)
- Simon Cross
- Computer Science Department, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa.
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Abstract
Physical and engineering practicalities involved in microprocessor design have resulted in flat performance growth for traditional single-core microprocessors. The urgent need for continuing increases in the performance of scientific applications requires the use of many-core processors and accelerators such as graphics processing units (GPUs). This paper discusses GPU acceleration of the multilevel summation method for computing electrostatic potentials and forces for a system of charged atoms, which is a problem of paramount importance in biomolecular modeling applications. We present and test a new GPU algorithm for the long-range part of the potentials that computes a cutoff pair potential between lattice points, essentially convolving a fixed 3-D lattice of "weights" over all sub-cubes of a much larger lattice. The implementation exploits the different memory subsystems provided on the GPU to stream optimally sized data sets through the multiprocessors. We demonstrate for the full multilevel summation calculation speedups of up to 26 using a single GPU and 46 using multiple GPUs, enabling the computation of a high-resolution map of the electrostatic potential for a system of 1.5 million atoms in under 12 seconds.
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Affiliation(s)
- David J. Hardy
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL, 61801
| | - John E. Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL, 61801
| | - Klaus Schulten
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL, 61801
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 W. Green, Urbana, IL, 61801
- Corresponding author. Email addresses: (David J. Hardy), (John E. Stone), (Klaus Schulten)
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Abstract
VMD (Visual Molecular Dynamics) is a molecular visualization and analysis program designed for biological systems such as proteins, nucleic acids, lipid bilayer assemblies, etc. This unit will serve as an introductory VMD tutorial. We will present several step-by-step examples of some of VMD's most popular features, including visualizing molecules in three dimensions with different drawing and coloring methods, rendering publication-quality figures, animating and analyzing the trajectory of a molecular dynamics simulation, scripting in the text-based Tcl/Tk interface, and analyzing both sequence and structure data for proteins.
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Affiliation(s)
- Jen Hsin
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Abstract
Molecular mechanics simulations offer a computational approach to study the behavior of biomolecules at atomic detail, but such simulations are limited in size and timescale by the available computing resources. State-of-the-art graphics processing units (GPUs) can perform over 500 billion arithmetic operations per second, a tremendous computational resource that can now be utilized for general purpose computing as a result of recent advances in GPU hardware and software architecture. In this article, an overview of recent advances in programmable GPUs is presented, with an emphasis on their application to molecular mechanics simulations and the programming techniques required to obtain optimal performance in these cases. We demonstrate the use of GPUs for the calculation of long-range electrostatics and nonbonded forces for molecular dynamics simulations, where GPU-based calculations are typically 10-100 times faster than heavily optimized CPU-based implementations. The application of GPU acceleration to biomolecular simulation is also demonstrated through the use of GPU-accelerated Coulomb-based ion placement and calculation of time-averaged potentials from molecular dynamics trajectories. A novel approximation to Coulomb potential calculation, the multilevel summation method, is introduced and compared with direct Coulomb summation. In light of the performance obtained for this set of calculations, future applications of graphics processors to molecular dynamics simulations are discussed.
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Affiliation(s)
- John E Stone
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Stone JE, Parker R, Gilks CB, Stanbridge EJ, Liao SY, Aquino-Parsons C. Intratumoral oxygenation of invasive squamous cell carcimoma of the vulva is not correlated with regional lymph node metastasis. EUR J GYNAECOL ONCOL 2005; 26:31-5. [PMID: 15754996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
INTRODUCTION Tumour hypoxia has been found to be associated with tumour aggressiveness. Our primary aim was to explore the relationship between pretreatment tumour oxygenation in primary vulvar carcinoma and nodal status. Our secondary objective was to assess if there was a relationship between the clinical and biological variables. METHODS 20 women with ISCC of the vulva were assessed with pretreatment primary tumour oxygenation with an Eppendorf pO2 probe. Patients underwent standard surgical management. Pathological assessment of the primary and nodal tissues was then performed. Primary tumour specimens were also stained for microvessel density and carbonic anhydrase IX. The relationship between smoking, preoperative Hgb, tumour CAIX expression, MVD, and Eppendorf pO2 measurements vs nodal metastasis and between these clinical and biological variables was assessed. RESULTS Seven patients had positive lymph nodes, 13 had negative nodes. While neither current smoking status, tumour size, tumour oxygen measurements, MVD and CAIX expression correlated with metastatic nodal disease, a low preoperative Hgb correlated with pathological nodal status (p < 0.027). CONCLUSIONS Although this analysis failed to demonstrate a strong correlation between various measures of tumour oxygenation with nodal metastasis, it may be due to the small number of patients. Only preoperative anaemia is correlated with nodal metastasis in early ISCC of the vulva.
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Affiliation(s)
- J E Stone
- Department of Gynecology, Department of Gynecologic Oncology, University of British Columbia, Vancouver
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Bowron A, Moorghen M, Morgan JE, Osborne JR, Stansbie D, Stone JE. Cost-effective strategy for the serological investigation of coeliac disease. Ann Clin Biochem 2000; 37 ( Pt 4):467-70. [PMID: 10902862 DOI: 10.1177/000456320003700406] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Increased numbers of requests for serological investigation of coeliac disease, and a local trend to request both anti-gliadin antibodies (AGA) and anti-endomysium antibodies (AEA) simultaneously, resulted in cost pressures that prompted a review of our practice. Serology results from all patients (771 children, 511 adults) investigated for coeliac disease over a 3-year period were compared with small intestine histology where available. IgG AGA and IgA AGA were measured by enzyme-linked immunosorbent assay (in-house), IgA AEA by immunofluorescence (send-away contract). Overall diagnostic performance was as follows: AGA sensitivity 84%, specificity 88%, positive predictive value (PPV) 24%, negative predictive value (NPV) 99%; AEA sensitivity 88%, specificity 97%, PPV 65%, NPV 99%. Results showed AGA, with its high NPV, to be a suitable first-line test to exclude coeliac disease. The high specificity of AEA makes it a suitable confirmatory test when AGA is positive. Introduction of this step-wise approach to coeliac disease investigation resulted in cost savings of at least Pound Sterling 5000 per year without detriment to the clinical service.
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Affiliation(s)
- A Bowron
- Department of Chemical Pathology, Bristol Royal Infirmary, UK.
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Abstract
Spo11, a type II topoisomerase, is likely to be required universally for initiation of meiotic recombination. However, a dichotomy exists between budding yeast and the animals Caenorhabditis elegans and Drosophila melanogaster with respect to additional roles of Spo11 in meiosis. In Saccharomyces cerevisiae, Spo11 is required for homolog pairing, as well as axial element (AE) and synaptonemal complex (SC) formation. All of these functions are Spo11 independent in C.elegans and D.melanogaster. We examined Spo11 function in a multicellular fungus, Coprinus cinereus. The C.cinereus spo11-1 mutant shows high levels of homolog pairing and occasionally forms full-length AEs, but no SC. In C.cinereus, Spo11 is also required for maintenance of meiotic chromosome condensation and proper spindle formation. Meiotic progression in spo11-1 is aberrant; late in meiosis basidia undergo programmed cell death (PCD). To our knowledge, this is the first example of meiotic PCD outside the animal kingdom. Ionizing radiation can partially rescue spo11-1 for both AE and SC formation and viable spore production, suggesting that the double-strand break function of Spo11 is conserved and is required for these functions.
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Affiliation(s)
- M Celerin
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
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Desbarats J, Stone JE, Lin L, Zakeri ZF, Davis GS, Pfeiffer LM, Titus RG, Newell MK. Rapid early onset lymphocyte cell death in mice resistant, but not susceptible to Leishmania major infection. Apoptosis 2000; 5:189-96. [PMID: 11232247 DOI: 10.1023/a:1009601200580] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Leishmania major (Lm) infection in mice is a prototypical model for the role of immune deviation in disease resistance. Resistant strains of mice develop a Th1 response to Lm infection, distinguished by secretion of IL-12 and interferon gamma. In contrast, susceptible strains display sustained IL-4 expression characteristic of a Th2 response. However, when mechanisms of cell death are blocked, mice display a susceptible phenotype even in the presence of a strong Th1 response, suggesting that cell death, and not cytokine bias, may be an important factor in disease resistance. Here, we investigated this hypothesis by comparing lymphocyte cellularity, cell death and Fas expression in resistant CBA and susceptible BALB/c mice during the course of Lm infection. We found that delayed onset of cell death and late Fas induction correlated with massive lymphocyte accumulation and susceptibility to leishmaniasis, while early cell death and rapid Fas induction occurred in resistant mice.
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Affiliation(s)
- J Desbarats
- Department of Medicine, University of Vermont College of Medicine, Burlington, Vermont 05405, USA
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