Abstract
β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.
| Original language | English |
|---|---|
| Pages (from-to) | 3058-3073 |
| Number of pages | 16 |
| Journal | Journal of Chemical Information and Modeling |
| Volume | 61 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 28 2021 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-accessplan ). The authors would like to thank the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim ( http://hecbiosim.ac.uk ) for time to run simulations on ARCHER. B.I. would like to acknowledge the COVID-19 pump-priming grant from the University of Huddersfield for funding computing resources for analysis. S.H. would like to thank Prof Frank Kozielski for insightful discussions on the manuscript. This material is based upon the work supported by the U.S. Department of Energy, Office of Science, through the Advanced Scientific Computing Research (ASCR), under contract number DEAC05-00OR22725 and the Exascale Computing Project (ECP) (17-SC-20-SC). This work was performed at the Oak Ridge Leadership Computing Facility (OLCF) of the Oak Ridge National Laboratory (ORNL) and used the Extreme Science and Engineering Discovery Environment (XSEDE) COVID-19HPC Consortium at the IBM AC922 Summit supercomputer of the OLCF at ORNL through allocation TG-ASC200020. D.B. would like to thank ASCR and ECP for assistance in the implementation of deep-learning, data processing, and data analysis algorithms and would like to thank members of the DOE National Virtual Biotechnology Laboratory (NVBL) projects for insightful discussions on the design of and results from the calculations described in this manuscript.
