Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking

RIBONE, SERGIO R.; PAZ, S. ALEXIS; ABRAMS, CAMERON F.; VILLARREAL, MARCOS A.

J. COMPUT. AIDED MOL. RESIGN

Springer

Año: 2021 vol. 36 p. 25 - 37

0920-654X

Screening already approved drugs for activity against a novel pathogen can be an important part of global rapid-responsestrategies in pandemics. Such high-throughput repurposing screens have already identifed several existing drugs with poten-tial to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifcally againstthis pathogen requires unambiguous identifcation of their corresponding targets, something the high-throughput screens arenot typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom proteinstructures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plural-ity of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method withknown drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentiallysuitable for repurposing to the inverse docking procedure. The most common preferential targets were the human enzymesTMPRSS2 and PIKfyve, followed by the viral enzymes Helicase and PLpro. All compounds that selected TMPRSS2 areknown serine protease inhibitors, and those that selected PIKfyve are known tyrosine kinase inhibitors. Detailed structuralanalysis of the docking poses revealed important insights into why these selections arose, and could potentially lead to morerational design of new drugs against these targets.