research-article

The Impact of Heterogeneous Computing on Workflows for Biomolecular Simulation and Analysis

Authors:

Thomas E. Cheatham,

Daniel R. RoeAuthors Info & Claims

Computing in Science & Engineering, Volume 17, Issue 2

Pages 30 - 39

https://doi.org/10.1109/MCSE.2015.7

Published: 01 March 2015 Publication History

Abstract

The field of biomolecular simulation has matured to where detailed, accurate, and functionally relevant information that complements experimental data about the structure, dynamics, and interactions of biomolecules can now be routinely discovered. This has been enabled by access to large-scale and heterogeneous high-performance computing resources, including special-purpose hardware. The improved performance of modern simulation methods coupled with hardware advances is shifting the rate-limiting steps of common biomolecular simulations of small to moderately sized systems from the generation of data (for example, via production molecular dynamics simulations that used to take weeks or even months) to the pre- and postprocessing phases of the workflow, namely, simulation setup and data processing, management, and analysis. Because the computational resources that are optimal for generating data aren't necessarily the same as for processing that data, access to heterogeneous computational resources enables a broader exploration of biomolecular structure and dynamics by facilitating distinct aspects of typical biomolecular simulation workflows, which might not be as efficient on a one-size-fits-all computational platform.

References

[1]

T. Schlick et al., “Biomolecular Modeling and Simulation: A Field Coming of Age,” Q. Rev. Biophysics, vol. 44, no. 2,2011, pp. 191–228.

[2]

R.O. Dror et al., “Biomolecular Simulation: A Computational Microscope for Molecular Biology,” Annual Rev. Biophysics, vol. 41, 2012, pp. 429–452.

[3]

T.J. Lane et al., “To Milliseconds and Beyond: Challenges in the Simulation of Protein Folding,” Current Opinion Structural Biology, vol. 23, no. 1,2013, pp. 58–65.

[4]

S. Piana, J.L. Klepeis, and D.E. Shaw, “Assessing the Accuracy of Physical Models Used in Protein-Folding Simulations: Quantitative Evidence from Long Molecular Dynamics Simulations,” Current Opinion Structural Biology, vol. 24, 2014, pp. 98–105.

[5]

R.O. Dror et al., “Pathway and Mechanism of Drug Binding to G-Protein-Coupled Receptors,” Proc. Nat’l Academy Science, vol. 108, no. 32,2011, pp. 13118–13123.

[6]

R.O. Dror et al., “Activation Mechanism of the Beta2-Adrenergic Receptor,” Proc. Nat’l Academy Science, vol. 108, no. 46,2011, pp. 18684–18689.

[7]

K.J. Kohlhoff et al., “Cloud-Based Simulations on Google Exacycle Reveal Ligand Modulation of GPCR Activation Pathways,” Nature Chemistry, vol. 6, no. 1,2014, pp. 15–21.

[8]

N.M. Henriksen, D.R. Roe, and T.E. Cheatham III, “Reliable Oligonucleotide Conformational Ensemble Generation in Explicit Solvent for Force Field Assessment Using Reservoir Replica Exchange Molecular Dynamics Simulations,” J. Physical Chemistry B, vol. 117, no. 15,2013, pp. 4014–4027.

[9]

C. Bergonzo et al., “Multidimensional Replica Exchange Molecular Dynamics Yields a Converged Ensemble of an RNA Tetranucleotide,” J. Chemical Theory and Computation, vol. 10, no. 1,2014, pp. 492–499.

[10]

R. Galindo-Murillo, D.R. Roe, and T.E. Cheatham III, “On the Absence of Intrahelical DNA Dynamics on the ms to ms Timescale,” Nature Communications, vol. 5, no. 5152,2014, pp. 1–8; http://dx.doi.org:/10.1038/ncomms6152.

[11]

R. Galindo-Murillo, D.R. Roe, and T.E. Cheatham III, “Convergence and Reproducibility in Molecular Dynamics Simulations of the DNA Duplex d(GCACGAACGAACGAACGC),” Biochimica et Biophysica Acta, 2014; https://doi.org/10.1016/j.bbagen.2014.09.007.

[12]

D.A. Pearlman et al., “AMBER, A Package of Computer Programs for Applying Molecular Mechanics, Normal Mode Analysis, Molecular Dynamics and Free Energy Calculations to Simulate the Structure and Energetic Properties of Molecules,” Computer Physics Communications, vol. 91, nos. 1–3, 1995, pp. 1–41.

[13]

B.R. Brooks et al., “CHARMm: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations,” J. Computational Chemistry, vol. 4, 1983, pp. 187–217.

[14]

B.R. Brooks and M. Hodoscek, “Parallelization of CHARMm for MIMD Machines,” Chemical Design Automation News, vol. 7, no. 12,1992, pp. 16–22.

[15]

T.W. Clark et al., Parallelizing Molecular Dynamics Using Spatial Decomposition, IEEE, 1994.

[16]

M. Nelson et al., “NAMD: A Parallel, Object-Oriented Molecular Dynamics Program,” Int’l J. Supercomputer Applications and High-Performance Computing, vol. 10, 1996, pp. 251–268.

Digital Library

[17]

R.H. Reid, C.A. Hooper, and B.R. Brooks, “Computer Simulations of a Tumor Surface Octapeptide Epitope,” Biopolymers, vol. 28, no. 1,1989, pp. 525–530.

[18]

M. Taiji et al., “MD-GRAPE: A Parallel Special-Purpose Computer System for Classical Molecular Dynamics Simulations,” Proc. 6th Joint EPS-APS Int’l Conf. Physics Computing, 1994, pp. 200–203.

[19]

E. Luttmann et al., “Accelerating Molecular Dynamic Simulation on the Cell Processor and PlayStation 3,” J. Computational Chemistry, vol. 30, no. 2,2009, pp. 268–274.

[20]

M.J. Harvey, G. Giupponi, and G. De Fabritiis, “ACEMD: Accelerating Bio-Molecular Dynamics in the Microsecond Time-Scale,” J. Chemical Theory and Computation, vol. 5, no 6, 2009, pp. 1632–1639; arxiv.org/pdf/0902.0827.

[21]

A.W. Goetz et al., “Routine Microsecond Molecular Dynamics Simulation with AMBER—Part I: Generalized Born,” J. Chemical Theory and Computation, vol. 8, no. 5,2012, pp. 1542–1555.

[22]

R. Salomon-Ferrer et al., “Routine Microsecond Molecular Dynamics Simulations with AMBER—Part II: Particle Mesh Ewald,” J. Chemical Theory and Computation, vol. 9, no. 9,2013, pp. 3878–3888.

[23]

M.S. Friedrichs et al., “Accelerating Molecular Dynamic Simulation on Graphics Processing Units,” J. Computational Chemistry, vol. 30, no. 6,2009, pp. 864–872.

[24]

C. Mei et al., “Enabling and Scaling Biomolecular Simulations of 100 Million Atoms on Petascale Machines with a Multicode-Optimized Message-Driven Runtime,” Proc. ACM/IEEE Conf. Supercomputing (SC11), ACM 978-1-4503-0771-0/11/11, 2011, pp. 1–11.

[25]

G. Zhao et al., “Mature HIV-1 Capsid Structure by Cryo-Electron Microscopy and All-Atom Molecular Dynamics,” Nature, vol. 497, no. 7451,2013, pp. 643–646.

[26]

U. Essmann et al., “A Smooth Particle Mesh Ewald Method,” J. Chemical Physics, vol. 103, no. 19,1995, pp. 8577–8593.

[27]

K.A. Feenstra, B. Hess, and H.J.C. Berendsen, “Improving Efficiency of Large Time-Scale Molecular Dynamics Simulations of Hydrogen-Rich Systems,” J. Computational Chemistry, vol. 20, no. 8,1999, pp. 786–798.

[28]

S. Le Grand, A.W. Goetz, and R.C. Walker, “SPFP: Speed without Compromise: A Mixed Precision Model for GPU Accelerated Molecular Dynamics Simulations,” Computational Physics Communications, vol. 184, no. 2,2013, pp. 374–380.

[29]

D.E. Shaw et al., “Anton, a Special-Purpose Machine for Molecular Dynamics Simulation,” ACM SIGARCH Computer Architecture News, vol. 35, no. 2,2007, pp. 1–12.

Digital Library

[30]

J.C. Phillips et al., “Scalable Molecular Dynamics with NAMD,” J. Computational Chemistry, vol. 26, no. 16,2005, pp. 1781–1802.

[31]

D. Van Der Spoel et al., “GROMACS: Fast, Flexible, and Free,” J. Computational Chemistry, vol. 26, no. 16,2005, pp. 1701–1718.

[32]

B.R. Brooks et al., “CHARMM: The Biomolecular Simulation Program,” J. Computational Chemistry, vol. 30, no. 10,2009, pp. 1545–1614.

[33]

D.A. Case et al., “The Amber Biomolecular Simulation Programs,” J. Computational Chemistry, vol. 26, no. 16,2005, pp. 1668–1688.

[34]

B.G. Fitch et al., “Blue Matter: Approaching the Limits of Concurrency for Classical Molecular Dynamics,” Proc. Supercomputing, 2006, p. 44; https://doi.org/10.1109/SC.2006.16.

[35]

K.J. Bowers et al., “Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters,” Proc. ACM/IEEE SC 2006 Conf., 2006, p. 43; https://doi.org/10.1109/SC.2006.54.

[36]

W.C. Swope, J.W. Pitera, and F. Suits, “Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory,” J. Physical Chemistry B, vol. 108, no. 21,2004, pp. 6571–6581.

[37]

N. Singhal, C.D. Snow, and V.S. Pande, “Using Path Sampling to Build Better Markovian State Models: Predicting the Folding Rate and Mechanism of a Tryptophan Zipper Beta Hairpin,” J. Chemical Physics, vol. 121, no. 1,2004, pp. 415–425.

[38]

S. Sriraman, I.G. Kevrekidis, and G. Hummer, “Coarse Master Equation from Bayesian Analysis of Replica Molecular Dynamics Simulations,” J. Physical Chemistry B, vol. 109, no. 14, 2005, pp. 6479–6484.

[39]

G.M. Torrie and J.P. Valleau, “Nonphysical Sampling Distributions in Monte Carlo Free-Energy Estimation: Umbrella Sampling,” J. Computational Physics, vol. 23, no. 2,1977, pp. 187–199.

[40]

D. Hamelberg, J. Mongan, and J.A. McCammon, “Accelerated Molecular Dynamics: A Promising and Efficient Simulation Method for Biomolecules,” J. Chemical Physics, vol. 120, no. 24, 2004, pp. 11919–11929.

[41]

X. Wu and B.R. Brooks, “Self-Guided Langevin Dynamics Simulation Method,” Chemical Physics Letters, vol. 381, no. 3,2003, pp. 512–518.

[42]

Y. Sugita and Y. Okamoto, “Replica-Exchange Molecular Dynamics Methods for Protein Folding,” Chemical Physics Letters, vol. 314, 1999, pp. 141–151.

[43]

Y. Sugita, A. Kitao, and Y. Okamoto, “Multidimensional Replica-Exchange Method for Free-Energy Calculations,” J. Chemical Physics, vol. 113, 2000, pp. 6042–6051.

[44]

A. Thota, A. Luckow, and S. Jha, “Efficient Large-Scale Replica-Exchange Simulations on Production Infrastructure,” Philosophical Trans. A, #Mathematics, |Physicals, and Eng. Sciences, vol. 369, no. 1949,2011, pp. 3318–3335.

[45]

R.C. Walker, L. Pierce, and R. Salamon, “Transforming Molecular Biology Research through Extreme Acceleration of AMBER Molecular Dynamics Simulations: Sampling for the 99%,” Proc. XSEDE, 2012; https://doi.org/10.1145/2335755.2335810.

[46]

B. Hess et al., “GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation,” J. Chemical Theory and Computation, vol. 4, no. 3,2008, pp. 435–447.

[47]

T. Meyer et al., “Essential Dynamics: A Tool for Efficient Trajectory Compression and Management,” J. Chemical Theory and Computation, vol. 2, no. 2,2006, pp. 251–258.

[48]

P. Marais et al., “Efficient Compression of Molecular Dynamics Trajectory Files,” J. Computational Chemistry, vol. 33, no. 27,2012, pp. 2131–2141.

[49]

A. Kumar et al., “Compression in Molecular Simulation Datasets,” LNCS 8261, Springer, 2013, pp. 22–29.

[50]

P. de Buyl, P.H. Colberg, and F. Höfling, “H5MD: A Structured, Efficient, and Portable File Format for Molecular Data,” Computational Physics Comm., vol. 185, no. 6,2014, pp. 1546–1553.

[51]

K. Tai et al., “BioSimGrid: Towards a Worldwide Repository for Biomolecular Simulations,” Organic Biomolecular Chemistry, vol. 2, no. 22, 2004, pp. 3219–3221.

[52]

D.A. Beck et al., “Dynameomics: Mass Annotation of Protein Dynamics and Unfolding in Water by High-Throughput Atomistic Molecular Dynamics Simulations,” Protein Eng., Design, and Selection, vol. 21, no. 6,2008, pp. 353–368.

[53]

E.K. Salje et al., “eScience for Molecular-Scale Simulations and the eMinerals Project,” Philosophical Trans. A, #Mathematics, |Physicals, and Eng. Sciences, vol. 367, no. 1890,2009, pp. 967–985.

[54]

T. Meyer et al., “MoDEL (Molecular Dynamics Extended Library): A Database of Atomistic Molecular Dynamics Trajectories,” Structure, vol. 18, no. 11,2010, pp. 1399–1409.

[55]

J.C. Thibault, J.C. Facelli, and T.E. Cheatham III, “iBIOMES: Managing and Sharing Biomolecular Simulation Data in a Distributed Environment,” J. Chemical Information and Modeling, vol. 53, no. 3,2013, pp. 726–736.

[56]

D.R. Roe and T.E. Cheatham III, “PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data,” J. Chemical Theory and Computation, vol. 9, 2013, pp. 3084–3095.

[57]

T. Tu et al., “A Scalable Parallel Framework for Analyzing Terascale Molecular Dynamics Simulation Trajectories,” Proc. ACM/IEEE SC ’08 Proc. Conf., 2008, article 56, p. 12.

[58]

J.E. Stone et al., “Accelerating Molecular Modeling Applications with Graphics Processors,” J. Computational Chemistry, vol. 28, no. 16, 2007, pp. 2618–2640.

[59]

B.G. Levine, J.E. Stone, and A. Kohlmeyer, “Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units—Radial Distribution Function Histogramming,” J. Computational Physics, vol. 230, no. 9,2011, pp. 3556–3569.

Digital Library

[60]

S.O. Yesylevskyy, “Pteros: Fast and Easy to Use Open-Source C++ Library for Molecular Analysis,” J. Computational Chemistry, vol. 33, no. 19,2012, pp. 1632–1636.

[61]

N.M. Glykos, “Software News and Updates. Carma: A Molecular Dynamics Analysis Program,” J. Computational Chemistry, vol. 27, no. 14,2006, pp. 1765–1768.

[62]

N. Michaud-Agrawal et al., “MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations,” J. Computational Chemistry, vol. 32, 2011, pp. 2319–2327.

[63]

A.V. Popov, Y.N. Vorobjev, and D.O. Zharkov, “MDTRAJ: A Molecular Dynamics Trajectory Analyzer with a Graphical User Interface,” J. Computational Chemistry, vol. 34, no. 4,2013, pp. 319–325.

[64]

T.D. Romo, N. Leioatts, and A. Grossfield, “Lightweight Object Oriented Structure Analysis: Tools for Building Tools to Analyze Molecular Dynamics Simulations,” J. Computational Chemistry, vol. 35, no. 32,2014, pp. 2305–2318.

[65]

J.E. Stone, K.L. Vandivort, and K. Schulten, “Immersive Out-of-Core Visualization of Large-Size and Long-Timescale Molecular Dynamics Trajectories,” LNCS 6939, Springer, 2011, pp. 1–12.

Cited By

Li YJu XXiao YJia QZhou YQian SLin RYang BShi SLiu XGao JWang ZLiu STan JWang XHu ZYan LXue WMohror KArnold DBadia R(2023)Rapid simulations of atmospheric data assimilation of hourly-scale phenomena with modern neural networksProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607031(1-13)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607031
Balasubramanian VJensen TTurilli MKasson PShirts MJha S(2020)Adaptive Ensemble Biomolecular Applications at ScaleSN Computer Science10.1007/s42979-020-0081-11:2Online publication date: 28-Mar-2020
https://dl.acm.org/doi/10.1007/s42979-020-0081-1
Duran DHermosilla PRopinski TKozlíková BVinacua ÁVázquez P(2019)Visualization of Large Molecular TrajectoriesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286485125:1(987-996)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TVCG.2018.2864851
Show More Cited By

Index Terms

The Impact of Heterogeneous Computing on Workflows for Biomolecular Simulation and Analysis
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Modeling and simulation

Index terms have been assigned to the content through auto-classification.

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cover image Computing in Science and Engineering

Computing in Science and Engineering Volume 17, Issue 2

Mar.-Apr. 2015

86 pages

ISSN:1521-9615

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IEEE Educational Activities Department

United States

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Published: 01 March 2015

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Cited By

Li YJu XXiao YJia QZhou YQian SLin RYang BShi SLiu XGao JWang ZLiu STan JWang XHu ZYan LXue WMohror KArnold DBadia R(2023)Rapid simulations of atmospheric data assimilation of hourly-scale phenomena with modern neural networksProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607031(1-13)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607031
Balasubramanian VJensen TTurilli MKasson PShirts MJha S(2020)Adaptive Ensemble Biomolecular Applications at ScaleSN Computer Science10.1007/s42979-020-0081-11:2Online publication date: 28-Mar-2020
https://dl.acm.org/doi/10.1007/s42979-020-0081-1
Duran DHermosilla PRopinski TKozlíková BVinacua ÁVázquez P(2019)Visualization of Large Molecular TrajectoriesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286485125:1(987-996)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TVCG.2018.2864851
Paraskevakos ILuckow AKhoshlessan MChantzialexiou GCheatham TBeckstein OFox GJha S(2018)Task-parallel Analysis of Molecular Dynamics TrajectoriesProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225128(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225128

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