Search for acrylonitrile-based inhibitors of SAR-Cov-19 main and papain-like proteases through covalent docking and high-throughput virtual screening

Abstract

The sudden outbreak of SARS-CoV-2 formerly known as the 2019 novel coronavirus (2019-nCoV) quickly turned into a pandemic of coronavirus disease 2019 (COVID-19), the scale of which has never been seen before. High infection rates and mortality from COVID-19 placed pressure on global health services, and this has been to the detriment of the global economy. However, treatment options for COVID-19 are still very limited; hence, it is now as important as ever that researchers explore searching for new compounds with pharmacokinetic properties that inhibit the two COVID proteases - the main protease (Mpro) and the papain-like protease (PLpro). The main protease is a cysteine protease; as such, it is susceptible to permanent inhibition by reactive species (warheads) that may covalently bind to cysteine residues. One such class of compounds is acrylonitriles, in which the reactive acrylonitrile is reactive towards cysteine through a Michael addition reaction. The resulting covalent interaction is permanent and inactivates the cysteine residue and hence the protease within the context of the COVID-19 life-cycle. In this context, this study seeks to utilize computational-based approaches to identify acrylonitrile-based inhibitors of coronavirus drug targets. To do this, the ZINC database has been screened for compounds containing acrylonitrile functionality, due to its known nature as a warhead that binds to cysteine residues. Pharmacokinetic properties are computed to evaluate the viability of identified inhibitors, and covalent and non-covalent molecular docking approaches to the Mpro enzyme crystal structure have also been used to assess the identified systems. To gather more information and evaluate the most promising systems, a subset of the most promising compounds have been subjected to molecular dynamics simulation (for both covalently bound and non-covalently bound systems).