An in silico discovery of potential 3CL protease inhibitors of SARS-CoV-2 based upon inactivation of the cysteine 145-Histidine 41 catalytic dyad

Abstract

Coronavirus disease 19 (COVID19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, several countries are at risk of the pandemic caused by this virus. In the absence of any vaccine or virus-specific antiviral treatments, the need is to fast track search for potential drug candidates to combat the virus. Though there are known drugs that are being repurposed to fight against the SARS-CoV-2, there is a requirement for the virus-specific drugs at the earliest. One of the main drug targets of SARS-CoV-2 is an essential non-structural protein, 3CL protease, critical for the life cycle of the virus. We have used molecular docking studies to screen a chemically diverse set of small molecules to identify potential drug candidates to target this protein. Of the 22,630 molecules from varied small molecule libraries, based on the binding affinities and physicochemical properties, we finalized 30 molecules to be potential drug candidates. Eight of these molecules bind in a manner allowing for the scope of a nearly orthogonal backside nucleophilic attack on their suitably placed electrophilic carbonyl groups by the thiol group of cysteine residue 145, while remaining inside a 4 Ǻ distance range. It is interesting since carbonyl groups are known to be attacked in a similar fashion by external nucleophiles and can be relevant when considering these molecules as potential mechanism-based irreversible inhibitors of the 3CL^Pro. Further, ADMET analysis and Molecular dynamics simulations and available bioactive assays led to the identification of three molecules with high potential to be explored as drug candidates/lead molecules to target 3CL^Pro of SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• 3CL protease
• virtual screening
• irreversible inhibitors
• flavonoids

Acknowledgements

BM is funded by the Ph.D. fellowship from BITS Pilani. We thank Dr. Paramita Haldar for useful discussions. We would like to acknowledge the supercomputing facility (Kosambi) at BITS Pilani K Birla Goa campus for providing the computational resources to carry out the calculations. We are thankful to Supercomputing Facility for Bioinformatics & Computational Biology (SCFBio), IIT Delhi for providing free compute time on their cluster which helped us in carrying out the MD Simulations.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

The authors declare no funding source.