Antimicrobial Drug Repurposing Through Molecular Modelling: Acquisition, Analyis and Prediction

Abstract

Antimicrobial resistance has sparked unprecedented medical crises around the world, not only increasing the mortality rate but also impacting nosocomial resources. Methicillin-resistant Staphylococcus aureus (MRSA) has consistently evaded the available range of antibiotics and is a typical case study for new generation drugs. Drug development has been conventionally suffering from exceedingly high costs and overdrawn timelines. Drug Repurposing can be a solution to alleviate those burdens. Put simply, DR is a mechanism to identify new usages of existing drugs, typically targeted to treat diseases different to the ones that these were initially intended for. This inherently interdisciplinary research targets to identify the best MRSA drug candidates analysing protein (BIG) data, in the process developing a combination of techniques from stochastic mathematics, statistics and data analytics that can generically identify drug targets from the databank. Structure-based virtual screening was used to repurpose an extensive range of marketed drugs and Phase I/II/III trials. Molecular docking methods were used for virtual screening against MRSA targets based on sequence alignment to match gene sequences against proteins in the Protein Data Bank (PDB). Ligands from the Database of Useful Decoys - Enhanced were docked against MRSA-oriented target proteins using 10 open-source docking programmes for benchmark. The novel consensus scoring methods prove superior to other reported consensus scores in terms of discrimination between decoys and active ligands concerning MRSA drug target identification. The consensus scoring predictions are then applied to docking data between MRSA targets and compounds from the Repurposing Hub to identify a list of potential drug candidates for anti-MRSA treatment. MRSA is currently an apocalypse across the world with limited prevention and medications. This study provided more potential candidates to help fight against MRSA. The consensus scoring developed in this study can be generically implemented to unlock other antimicrobial drug candidates.