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. 2022 May 30:e1622.
doi: 10.1002/wcms.1622. Online ahead of print.

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case

Miłosz Wieczór  1   2 , Vito Genna  1 , Juan Aranda  1 , Rosa M Badia  3 , Josep Lluís Gelpí  3   4 , Vytautas Gapsys  5 , Bert L de Groot  5 , Erik Lindahl  6   7 , Martí Municoy  8 , Adam Hospital  1 , Modesto Orozco  1   4
Affiliations

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case

Miłosz Wieczór et al. Wiley Interdiscip Rev Comput Mol Sci. .

Abstract

Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under:Data Science > Computer Algorithms and ProgrammingData Science > Databases and Expert SystemsMolecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods.

Keywords: BioExcel; COVID19; exascale; molecular dynamics.

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Figures

FIGURE 1
FIGURE 1
Left: Schematic representation of the nonequilibrium alchemical free energy calculations (taken from Aldeghi, 2019). Right: Alchemical free energy calculations workflow implemented with BioExcel building blocks.
FIGURE 2
FIGURE 2
High parallelization reached thanks to the PyCOMPSs workflow manager. Example of a single job using 32 nodes (1536 cores) of the Marenostrum supercomputer at the BSC. Each line shows the CPU usage in % of a single MareNostrum node.
FIGURE 3
FIGURE 3
Impact of human polymorphisms in RBD‐hACE2 binding free energy, computed with BioExcel workflows including GROMACS and pmx. Human ACE2 protein mutations with higher frequency in the population are shown. Positive ΔΔG values indicate that a mutation reduces binding affinity. The ΔΔG values are in kcal/mol.
FIGURE 4
FIGURE 4
A multi‐step strategy to predict the effect of ACE2 mutations on the ACE‐RBD binding affinity. In the first step, the ACE2 residues at the protein binding interface are scanned by means of the computationally efficient Rosetta flex ddG protocol. In the second step, a selection of the mutations is probed by the computationally more demanding MD based free energy calculations using pmx and Gromacs software packages. The calculations allow evaluating the effect of mutations on the protein binding affinity, as well as on the stability of the ACE2 protein. This strategy can be further adapted to predict the effects of virus mutations, design high affinity antibodies or peptide therapeutics.
FIGURE 5
FIGURE 5
Screenshots from the BioExcel‐CV19 web server. (a) Trajectory representation (berzosertib, an FDA approved drug molecule binding to the ectodomain of human ACE2); (b) analysis of the ligand pocket flexibility; (c) Analysis of electrostatic and Van der Waals interactions between the ligand and the ACE2 receptor; (d) analysis of hydrogen bonding between the ligand and the ACE2 receptor; (e) electrostatic potential surface in the ligand‐binding pocket; (f) insight on the main interaction between the ligand and the ACE2 receptor involving an aspartic acid. URL: https://bioexcel‐cv19.bsc.es/#/browse/MCV1900072
See this image and copyright information in PMC

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