In silico-based screening of natural products as potential inhibitors of SARS-CoV-2 macrodomain 1

Abstract

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide has led to over 600 million cases of coronavirus disease 2019 (COVID-19). Identifying effective molecules that can counteract the virus is imperative. SARS-CoV-2 macrodomain 1 (Mac1) represents a promising antiviral drug target. In this study, we predicted potential inhibitors of SARS-CoV-2 Mac1 from natural products using in silico-based screening. Based on the high-resolution crystal structure of Mac1 bound to its endogenous ligand ADP-ribose (ADPr), we first performed a docking-based virtual screening of Mac1 inhibitors against a natural product library and obtained five representative compounds (MC1–MC5) by clustering analysis. All five compounds were stably bound to Mac1 during 500 ns long molecular dynamics simulations. The binding free energy of these compounds to Mac1 was calculated using molecular mechanics generalized Born surface area and further refined with localized volume-based metadynamics. The results demonstrated that both MC1 (−9.8 ± 0.3 kcal/mol) and MC5 (−9.6 ± 0.3 kcal/mol) displayed more favorable affinities to Mac1 with respect to ADPr (−8.9 ± 0.3 kcal/mol), highlighting their potential as potent SARS-CoV-2 Mac1 inhibitors. Overall, this study provides potential SARS-CoV-2 Mac1 inhibitors, which may pave the way for developing effective therapeutics for COVID-19.

Communicated by Ramaswamy H. Sarma

Highlights

• Mac1 is a promising anti-SARS-CoV-2 therapeutic target.

• Virtual screening of natural products targeting SARS-CoV-2 Mac1 was performed against a library containing 282,192 compounds.

• Two potential Mac1 inhibitors were theoretically identified with more favorable affinities than the endogenous ligand ADPr.

Keywords:

• SARS-CoV-2
• COVID-19
• macrodomain
• inhibitor
• MD simulation
• metadynamics

Disclosure statement

No potential conflict of interest was reported by the author(s)

Additional information

Funding

We gratefully acknowledge financial supports from the Natural Science Foundation of China (22173020), the Natural Science Foundation of Fujian Province (2019J06007) and the Joint Funds for the Innovation of Science and Technology, Fujian Province (2019Y9040).