Hybrid Computing Clusters to Study Protein Structure, Function and Regulation

Authors

  • Dmitry A. Suplatov The A.N. Belozersky Institute Of Physico-Chemical Biology
  • Nina N. Popova Faculty of Computational Mathematics and Cybernetics at the Lomonosov Moscow State University
  • Kirill E. Kopylov Faculty of Chemistry at the Lomonosov Moscow State University
  • Maxim V. Shegai Faculty of Computational Mathematics and Cybernetics at the Lomonosov Moscow State University
  • Vladimir V. Voevodin Faculty of Computational Mathematics and Cybernetics, Research Computing Center at the Lomonosov Moscow State University
  • Vytas K. Svedas Faculty of Bioengineering and Bioinformatics at the Lomonosov Moscow State University

DOI:

https://doi.org/10.14529/cmse170406

Keywords:

Hybrid HPC clusters, bioinformatics, molecular modelling, codesign, GPU accelerators

Abstract

Studying protein structure, function and regulation using bioinformatics and molecular modeling is a complex task that requires a combination of various methods and ways to implement them. The process can be seen as a pipeline of sequential steps executed by various programs which benefit from customized hardware. Hybrid computing clusters characterized by a significant performance and a variety of hardware capabilities are necessary to optimally execute each individual step of the complex solution. It can be specifically noted that GPU accelerators open new opportunities for efficient solution of resource-intensive tasks of bioinformatics and molecular modeling.

Author Biographies

Dmitry A. Suplatov, The A.N. Belozersky Institute Of Physico-Chemical Biology

Ph.D. in Chemistry, Senior Research Scientist in The A.N. Belozersky Institute Of Physico-Chemical Biology at the Lomonosov Moscow State University

Nina N. Popova, Faculty of Computational Mathematics and Cybernetics at the Lomonosov Moscow State University

Ph.D. in Mathematics and Physics, Associate professor of Faculty of Computational Mathematics and Cybernetics at the Lomonosov Moscow State University

Kirill E. Kopylov, Faculty of Chemistry at the Lomonosov Moscow State University

Student

Maxim V. Shegai, Faculty of Computational Mathematics and Cybernetics at the Lomonosov Moscow State University

Ph.D. Student

Vladimir V. Voevodin, Faculty of Computational Mathematics and Cybernetics, Research Computing Center at the Lomonosov Moscow State University

D.Sc. in Mathematics and Physics, corresponding member of RAS, deputy Director of Research Computing Center at the Lomonosov Moscow State University

Vytas K. Svedas, Faculty of Bioengineering and Bioinformatics at the Lomonosov Moscow State University

D.Sc. in Chemistry, Professor of Faculty of Bioengineering and Bioinformatics at the Lomonosov Moscow State University

References

Suplatov D., Švedas V. Study of Functional and Allosteric sites in Protein Superfamilies. Acta Naturae. 2015. vol. 7, no. 4, pp. 34–45.

Zuckerkandl E., Pauling L., Evolutionary Divergence and Convergence in Proteins. Evolving Genes and Proteins. 1965. vol. 97. pp. 97–166.

Fourment M., Gillings M.R. A Comparison of Common Programming Languages Used in Bioinformatics. BMC Bioinformatics. 2008. vol. 9, no. 1. pp. 82. DOI: 10.1186/1471-2105-9-82.

Kourist R., Jochens H., Bartsch S., Kuipers R., Padhi S.K., Gall M., Dominique B., Henk-Jan J., Bornscheuer U.T. The α/β‐hydrolase Fold 3DM Database (ABHDB) as a Tool for Protein Engineering. ChemBioChem. 2010. vol. 11, no. 12. pp. 1635–1643. DOI: 10.1002/cbic.201000213.

BioProdict software. Available at: https://www.bio-prodict.nl/ (accessed: 28.09.2017).

Vouzis P.D., Sahinidis N.V. GPU-BLAST: Using Graphics Processors to Accelerate Protein Sequence Alignment. Bioinformatics. 2011. vol. 27, no. 2. pp. 182–188. DOI: 10.1093/bioinformatics/btq644.

Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI-BLAST: a New Generation of Protein Database Search Programs. Nucleic Acids Research. 1997. vol. 25, no. 17. pp. 3389–3402. DOI: 10.1093/nar/25.17.3389.

Suplatov D., Shalaeva D., Kirilin E., Arzhanik V., Švedas V. Bioinformatic Analysis of Protein Families for Identification of Variable Amino Acid Residues Responsible for Functional Diversity. Journal of Biomolecular Structure and Dynamics. 2014. vol. 32, no. 1. pp. 75–87. DOI: 10.1080/07391102.2012.750249.

Suplatov D., Kirilin E., Takhaveev V., Švedas V. Zebra: Web-server for Bioinformatic Analysis of Diverse Protein Families. Journal of Biomolecular Structure and Dynamics. 2014. vol. 32, no. 11. pp. 1752–1758. DOI: 10.1080/07391102.2013.834514.

Suplatov D., Kirilin E., Arbatsky M., Takhaveev V., Švedas V. PocketZebra: a Webserver for Automated Selection and Classification of Subfamily-specific Binding Sites by Bioinformatic Analysis of Diverse Protein Families. Nucleic Acids Research. 2014. vol. 42, no. W1. pp. W344–W349. DOI: 10.1093/nar/gku448.

Suplatov D., Kirilin E., Švedas V. Bioinformatic Analysis of Protein Families to Select Function-related Variable Positions “Understanding Enzymes: Function, Design, Engineering and Analysis” (Allan Svendsen) Pan Stanford Publishing 2016. pp. 351–385.

Suplatov D., Popova N., Zhumatiy S., Voevodin V., Švedas V. Parallel Workflow Manager for Non-parallel Bioinformatic Applications to Solve Large-scale Biological Problems on a Supercomputer. Journal of Bioinformatics and Computational Biology. 2016. vol. 14, no. 2. pp. 1641008. DOI: 10.1142/S0219720016410080.

Le Grand S., Götz A.W., Walker R.C. SPFP: Speed Without Compromise — A Mixed Precision Model for GPU Accelerated Molecular Dynamics Simulations. Computer Physics Communications. 2013. vol. 184, no. 2. pp. 374–380. DOI: 10.1016/j.cpc.2012.09.022.

Salomon-Ferrer R., Goetz A.W., Poole D., Le Grand S., Walker R.C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. Journal of Chemical Theory and Computation. 2013. vol. 9, no. 9. pp. 3878–3888. DOI: 10.1021/ct400314y.

Goetz A.W., Williamson M.J., Xu D., Poole D., Le Grand S., Walker R.C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born. Journal of Chemical Theory and Computation. 2012. vol. 8, no. 5. pp. 1542–1555. DOI: 10.1021/ct200909j.

Bezsudnova E.Y., Stekhanova T.N., Suplatov D.A., Mardanov A.V., Ravin N.V., Popov V.O. Experimental and Computational Studies on the Unusual Substrate Specificity of Branched-chain Amino Acid Aminotransferase from Thermoproteus uzoniensis. Archives of Biochemistry and Biophysics. 2016. vol. 607. pp. 27–36. DOI:10.1016/j.abb.2016.08.009.

Suplatov D.A., Voevodin V.V., Svedas V.K. Robust Enzyme Design: Bioinformatic Tools for Improved Protein Stability. Biotechnology Journal. 2015. vol. 10, no. 3. pp. 344–355. DOI:10.1002/biot.201400150.

Shcherbakova T., Panin N., Suplatov D., Shapovalova I, Švedas V. The βD484N Mutant of Penicillin Acylase from Escherichia coli is More Resistant to Inactivation by Substrates and Can Effectively Perform Peptide Synthesis in Aqueous Medium. Journal of Molecular Catalysis B: Enzymatic. 2015. vol. 112. pp. 66–68. DOI:10.1016/j.molcatb.2014.11.015.

Suplatov D., Panin N., Kirilin E., Shcherbakova T., Kudryavtsev P., Švedas V. Computational Design of a pH Stable Enzyme: Understanding Molecular Mechanism of Penicillin Acylase's Adaptation to Alkaline Conditions. PLoS ONE. 2014. vol. 9, no. 6. pp. e100643. DOI: 10.1371/journal.pone.0100643.

Suplatov D., Besenmatter W., Švedas V., Svendsen A. Bioinformatic Analysis of Alpha/beta-hydrolase Fold Enzymes Reveals Subfamily-specific Positions Responsible for Discrimination of Amidase and Lipase Activities. Protein Engineering, Design and Selection. 2012. vol. 25, no. 11. pp. 689–697. DOI: 10.1093/protein/gzs068.

Voevodin Vl.V., Zhumatiy S.A., Sobolev S.I., Antonov A.S., Bryzgalov P.A., Nikitenko D.A., Stefanov K.S., Voevodin Vad.V. Praktika supercomputera “Lomonosov” [Practice of the “Lomonosov” Supercomputer]. Otkrytye sistemy [Open Systems]. 2012. vol. 7, pp. 36–39 (in Russian)

Nobile M.S., Cazzaniga P., Tangherloni A., Besozzi D. Graphics Processing Units in Bioinformatics, Computational Biology and Systems Biology. Briefings in Bioinformatics. 2016. pp. 870–885. DOI: 10.1093/bib/bbw058.

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Published

2017-12-11

Issue

Vol. 6 No. 4 (2017)

Section

Supercomputer Modeling

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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.