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2021 | OriginalPaper | Chapter

High-Performance Cloud Computing for Exhaustive Protein–Protein Docking

Authors : Masahito Ohue, Kento Aoyama, Yutaka Akiyama

Published in: Advances in Parallel & Distributed Processing, and Applications

Publisher: Springer International Publishing

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Abstract

Public cloud computing environments, such as Amazon Web Services, Microsoft Azure, and the Google Cloud Platform, have achieved remarkable improvements in computational performance in recent years and are also expected to be able to perform massively parallel computing. As the cloud enables users to use thousands of CPU cores and GPU accelerators casually, and various software types can be used very easily by cloud images, the cloud is beginning to be used in the field of bioinformatics. In this study, we ported the original protein–protein interaction prediction (protein–protein docking) software, MEGADOCK, into Microsoft Azure as an example of an HPC cloud environment. A cloud parallel computing environment with up to 1600 CPU cores and 960 GPUs was constructed using four CPU instance types and two GPU instance types, and the parallel computing performance was evaluated. Our MEGADOCK on Azure system showed a strong scaling value of 0.93 for the CPU instance when H16 instance with 100 instances was used compared to 50 and a strong scaling value of 0.89 for the GPU instance when NC24 instance with 20 was used compared to 5. Moreover, the results of the usage fee and total computation time supported that using a GPU instance reduced the computation time of MEGADOCK and the cloud usage fee required for the computation. The developed environment deployed on the cloud is highly portable, making it suitable for applications in which an on-demand and large-scale HPC environment is desirable.

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I.A.T. Hashem, I. Yaqoob, N.B. Anuar et al., The rise of “big data” on cloud computing: Review and open research issues. Inf. Syst. 47, 98–115 (2015). https://doi.org/10.1016/j.is.2014.07.006 CrossRef

R. Tudoran, A. Costan, G. Antoniu et al., A performance evaluation of Azure and Nimbus clouds for scientific applications, in Proc CloudCP’12 (2012), pp. 1–6. https://doi.org/10.1145/2168697.2168701

A. O’Driscoll, J. Daugelaite, R.D. Sleator, ‘Big data’, Hadoop and cloud computing in genomics. J. Biomed. Inform. 46(5), 774–781 (2013). https://doi.org/10.1016/j.jbi.2013.07.001

V. Sobeslav, P. Maresova, O. Krejcar et al., Use of cloud computing in biomedicine. J. Biomol. Struct. Dyn. 34(12), 2688–2697 (2016). https://doi.org/10.1080/07391102.2015.1127182

J. Karlsson, O. Torreno, D. Ramet et al., Enabling large-scale bioinformatics data analysis with cloud computing, in Proc IEEE ISPA2012 (2012), pp. 640–645. https://doi.org/10.1109/ISPA.2012.95

H.P. Shanahan, A.M. Owen, A.P. Harrison, Bioinformatics on the cloud computing platform azure. PLoS One 9(7), e102642 (2014). https://doi.org/10.1371/journal.pone.0102642

Ekanayake J, Gunarathne T, Qiu J (2011) Cloud Technologies for Bioinformatics Applications. IEEE Trans Parallel Distrib Syst 22(6), 998–1011. https://doi.org/10.1109/TPDS.2010.178 CrossRef

A. Matsunaga, M. Tsugawa, J. Fortes, CloudBLAST: Combining MapReduce and virtualization on distributed resources for bioinformatics applications, in Proc IEEE eScience2008 (2008), pp. 222–229. https://doi.org/10.1109/eScience.2008.62

W. Lu, J. Jackson, R. Barga, AzureBlast: A case study of developing science applications on the cloud, in Proc ACM HPDC’10 (2010), pp. 413–420. https://doi.org/10.1145/1851476.1851537

10.

T. Gunarathne, T.-L. Wu, J.Y. Choi et al., Cloud computing paradigms for pleasingly parallel biomedical applications. Concurr. Comput.. Pract. Exp. 23(17), 2338–2354 (2011). https://doi.org/10.1002/cpe.1780 CrossRef

11.

D. Mrozek, B. Małysiak-Mrozek, A. Kłapciński, Cloud4Psi: cloud computing for 3D protein structure similarity searching. Bioinformatics 30(19), 2822–2825 (2014). https://doi.org/10.1093/bioinformatics/btu389 CrossRef

12.

D. Mrozek, T. Kutyła, B. Małysiak-Mrozek, Accelerating 3D protein structure similarity searching on microsoft azure cloud with local replicas of macromolecular data, in Proc PPAM2015, LNCS 9574 (2016), pp. 254–265. https://doi.org/10.1007/978-3-319-32152-3_24

13.

D. Mrozek, P. Gosk, B. Małysiak-Mrozek, Scaling Ab Initio Predictions of 3D protein structures in microsoft azure cloud. J Grid Comput 13, 561–585 (2015). https://doi.org/10.1007/s10723-015-9353-8 CrossRef

14.

B.T. Moghadam, J. Alvarsson, M. Holm et al., Scaling predictive modeling in drug development with cloud computing. J. Chem. Inf. Model 55(1), 19–25 (2015). https://doi.org/10.1021/ci500580y CrossRef

15.

Z. Farkas, P. Kacsuk, T. Kiss et al., AutoDock gateway for molecular docking simulations in cloud systems, in Cloud Computing with e-Science Applications (2015), pp. 217–236. https://doi.org/10.1201/b18021-11

16.

R. De Paris, D.A.D. Ruiz, O.N. de Souza, A cloud-based workflow approach for optimizing molecular docking simulations of fully-flexible receptor models and multiple ligands, in Proc IEEE CloudCom2015 (2015), pp. 495–498. https://doi.org/10.1109/CloudCom.2015.43

17.

P. Hodor, A. Chawla, A. Clark et al., cl-dash: rapid configuration and deployment of Hadoop clusters for bioinformatics research in the cloud. Bioinformatics 32(2), 301–303 (2015). https://doi.org/10.1093/bioinformatics/btv553

18.

J. Qiu, J. Ekanayake, T. Gunarathne et al., Hybrid cloud and cluster computing paradigms for life science applications. BMC Bioinform. 11, S3 (2010). https://doi.org/10.1186/1471-2105-11-S12-S3 CrossRef

19.

H.A. Hassan, S.A. Mohamed, W.M. Sheta, Scalability and communication performance of HPC on Azure Cloud. Egypt Inform. J. 17(2), 175–182 (2016). https://doi.org/10.1016/j.eij.2015.11.001 CrossRef

20.

Y. Matsuzaki, N. Uchikoga, M. Ohue et al., Rigid-docking approaches to explore protein-protein interaction space. Adv. Biochem. Eng. Biotechnol. 160, 33–55 (2017). https://doi.org/10.1007/10_2016_41

21.

E. Chermak, A. Petta, L. Serra et al., CONSRANK: a server for the analysis, comparison and ranking of docking models based on inter-residue contacts. Bioinformatics 31(9), 1481–1483 (2015). https://doi.org/10.1093/bioinformatics/btu837 CrossRef

22.

G. Launay, M. Ohue, J.P. Santero et al., Rescoring ensembles of protein-protein docking poses using consensus approaches (2020). bioRxiv 2020.04.24.059469. https://doi.org/10.1101/2020.04.24.059469

23.

A. Lopes, S. Sacquin-Mora, V. Dimitrova et al., Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information. PLoS Comput. Biol. 9, e1003369 (2013). https://doi.org/10.1371/journal.pcbi.1003369 CrossRef

24.

M. Ohue, T. Shimoda, S. Suzuki et al., MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers. Bioinformatics 30(22), 3281–3283 (2014). https://doi.org/10.1093/bioinformatics/btu532

25.

T. Hayashi, Y. Matsuzaki, K. Yanagisawa et al., MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions. BMC Bioinform. 19, 62 (2018). https://doi.org/10.1186/s12859-018-2073-x CrossRef

26.

M.N. Wass, G. Fuentes, C. Pons et al., Towards the prediction of protein interaction partners using physical docking. Mol. Syst. Biol. 7, 469 (2011). https://doi.org/10.1038/msb.2011.3 CrossRef

27.

C. Zhang, B. Tang, Q. Wang et al., Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening. Proteins 82(10), 2472–2482 (2014). https://doi.org/10.1002/prot.24611 CrossRef

28.

E. Karaca, A.M.J.J. Bonvin, A multidomain flexible docking approach to deal with large conformational changes in the modeling of biomolecular complexes. Structure 19(4), 555–565 (2011). https://doi.org/10.1016/j.str.2011.01.014 CrossRef

29.

Y. Matsuzaki, N. Uchikoga, M. Ohue et al., MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments. Source Code Biol. Med. 8, 18 (2013). https://doi.org/10.1186/1751-0473-8-18

30.

M. Ohue, Y. Matsuzaki, N. Uchikoga et al., MEGADOCK: An all-to-all protein-protein interaction prediction system using tertiary structure data. Protein Pept. Lett. 21(8), 766–778 (2014). https://doi.org/10.2174/09298665113209990050 CrossRef

31.

E. Katchalski-Katzir, I. Shariv, M. Eisenstein et al., Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc. Natl. Acad. Sci. U. S. A. 89(6), 2195–2199 (1992). https://doi.org/10.1073/pnas.89.6.2195 CrossRef

32.

T. Shimoda, S. Suzuki, M. Ohue et al., Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures. BMC Syst. Biol. 9(Suppl 1), S6 (2015). https://doi.org/10.1186/1752-0509-9-S1-S6

33.

R. Chen, J. Mintseris, J. Janin et al., A protein-protein docking benchmark. Proteins 52, 88–91 (2003). https://doi.org/10.1002/prot.10390 CrossRef

34.

K. Aoyama, Y. Yamamoto, M. Ohue et al., Performance evaluation of MEGADOCK protein–protein interaction prediction system implemented with distributed containers on a cloud computing environment, in Proc PDPTA’19 (2019), pp. 175–181

Title: High-Performance Cloud Computing for Exhaustive Protein–Protein Docking
Authors: Masahito Ohue
Kento Aoyama
Yutaka Akiyama
Publisher: Springer International Publishing
Book: Advances in Parallel & Distributed Processing, and Applications
Print ISBN: 978-3-030-69983-3

Electronic ISBN: 978-3-030-69984-0

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-69984-0_53

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