Experiences with managing data parallel computational workflows for High-throughput Fragment Molecular Orbital (FMO) Calculations

TL;DR

This paper discusses a framework for managing large-scale, high-throughput FMO calculations on biomolecular systems, demonstrated through drug-repurposing studies related to SARS-CoV-2, enhancing computational efficiency and workflow management.

Contribution

The paper introduces a novel static parameter sweeping framework using Apache Airavata for managing high-throughput FMO calculations on large biomolecular datasets.

Findings

Successfully managed 2820 FMO calculations for SARS-CoV-2 drug screening

Framework improves workflow automation and error handling in large-scale FMO computations

Potential for broad application in biomolecular FMO studies

Abstract

Fragment Molecular Orbital (FMO) calculations provide a framework to speed up quantum mechanical calculations and so can be used to explore structure-energy relationships in large and complex biomolecular systems. These calculations are still onerous, especially when applied to large sets of molecules. Therefore, cyberinfrastructure that provides mechanisms and user interfaces that manage job submissions, failed job resubmissions, data retrieval, and data storage for these calculations are needed. Motivated by the need to rapidly identify drugs that are likely to bind to targets implicated in SARS-CoV-2, the virus that causes COVID-19, we developed a static parameter sweeping framework with Apache Airavata middleware to apply to complexes formed between SARS-CoV-2 M-pro (the main protease in SARS-CoV-2) and 2820 small-molecules in a drug-repurposing library. Here we describe the implementation of our framework for managing the executions of the high-throughput FMO calculations. The approach is general and so should find utility in large-scale FMO calculations on biomolecular systems.