Simulating the Long-timescale Structural Behavior of Bacterial and Influenza Neuraminidases with Different HPC Resources

Yana A. Sharapova; Dmitry A. Suplatov; Vytas K. Švedas

doi:10.14529/jsfi180303

Authors

Yana A. Sharapova Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University
Dmitry A. Suplatov Belozersky Institute Of Physicochemical Biology, Lomonosov Moscow State University
Vytas K. Švedas Faculty of Bioengineering and Bioinformatics and Belozersky Institute Of Physicochemical Biology, Lomonosov Moscow State University

DOI:

https://doi.org/10.14529/jsfi180303

Abstract

Understanding the conformational dynamics which affects ligand binding by Neuraminidases is needed to improve the in silico selection of novel drug candidates targeting these pathogenicity factors and to adequately estimate the efficacy of potential drugs. Conventional molecular dynamics (MD) is a powerful tool to study conformational sampling, drug-target recognition and binding, but requires significant computational effort to reach timescales relevant for biology. In this work the advances in a computer power and specialized architectures were evaluated at simulating long MD trajectories of the structural behavior of Neuraminidases. We conclude that modern GPU accelerators enable calculations at the timescales that would previously have been intractable, providing routine access to microsecond-long trajectories in a daily laboratory practice. This opens an opportunity to move away from the "static" affinity-driven strategies in drug design towards a deeper understanding of ligand-specific conformational adaptation of target sites in protein structures, leading to a better selection of efficient drug candidates in silico. However, the performance of modern GPUs is yet far behind the deeply-specialized supercomputers co-designed for MD. Further development of affordable specialized architectures is needed to move towards the much-desired millisecond timescale to simulate large proteins at a daily routine.

References

Abed, Y., Boivin, G.: A review of clinical influenza A and B infections with reduced susceptibility to both Oseltamivir and Zanamivir. Open Forum Infectious Diseases 4(3), ofx105 (2017), DOI: 10.1093/ofid/ofx105

Durrant, J.D., Bush, R.M., Amaro, R.E.: Microsecond molecular dynamics simulations of influenza neuraminidase suggest a mechanism for the increased virulence of stalk-deletion mutants. The Journal of Physical Chemistry B 120(33), 8590–8599 (2016), DOI: 10.1021/acs.jpcb.6b02655

Ganesan, A., Coote, M.L., Barakat, K.: Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug Discovery Today 22(2), 249–269 (2017), DOI: 10.1016/j.drudis.2016.11.001

Sadovnichy, V., Tikhonravov, A., Voevodin, V., Opanasenko, V.: Lomonosov: Supercomputing at Moscow State University. In: Contemporary High Performance Computing: From Petascale Toward Exascale (Chapman & Hall/CRC Computational Science). pp. 283–307. CRC Press Boca Raton, Fla, USA (2013), DOI: 10.1201/2F9781351104005

Sharapova, Y., Suplatov, D., ˇSvedas, V.: Neuraminidase a from streptococcus pneumoniae has a modular organization of catalytic and lectin domains separated by a flexible linker. The FEBS Journal 285(13), 2428–2445 (2018), DOI: 10.1111/febs.14486

Shaw, D.E., et al.: Anton 2: Raising the Bar for Performance and Programmability in a Special-purpose Molecular Dynamics Supercomputer. In: International Conference for High Performance Computing, Networking, Storage and Analysis, 2014, 16–21 Nov. 2014, New Orleans, LA, USA. pp. 41–53. IEEE Press (2014), DOI: 10.1109/SC.2014.9

Xu, Z., et al.: Sequence diversity of nana manifests in distinct enzyme kinetics and inhibitor susceptibility. Scientific Reports 6, 25169 (2016), DOI: 10.1038/srep25169