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SARS-CoV-2-Modelling

This is the repository for supplementary files from:

Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding

H. T. Henry Chan, Marc A. Moesser, Rebecca K. Walters, Tika R. Malla, Rebecca M. Twidale, Tobias John, Helen M. Deeks, Tristan Johnston-Wood, Victor Mikhailov, Richard B. Sessions, William Dawson, Eidarus Salah, Petra Lukacik, Claire Strain-Damerell, David Owen, Takahito Nakajima, Katarzyna Swiderek, Alessio Lodola, Vicent Moliner, David R. Glowacki, Martin A. Walsh, Christopher J. Schofield, Luigi Genovese, Deborah Shoemark, Adrian J. Mulholland, Fernanda Duarte, Garrett M. Morris

doi: https://doi.org/10.1101/2021.06.18.446355

Here's an overview of our work:

Overview of Work

The SARS-CoV-2 Mpro:substrate models reveal the stereochemical constraints of substrate specificity at each position from P6 through P5’. Those positions facing “into” the Mpro are spatially constrained, while the alternating positions facing “away” from Mpro are not. Here is a grid image of the 2q6g crystal structure (SARS-CoV Mpro H41A + T S A V L Q - S G F R K in pink) and the 11 energy minimized models of SARS-CoV-2 Mpro and the 11 native cleavage sites in orange.

2q6g+11_SARS-CoV-2_Mpro_substrates_min_grid.png

Our work has greatly benefited from the publicly-available X-ray crystal structures of SARS-CoV-2 Mpro at the PDB and Diamond Light Source's XChem fragment screen.

Many of us have contributed to the COVID Moonshot project, too:

COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

Authors: https://tinyurl.com/y3r7redd

doi: https://www.biorxiv.org/content/10.1101/2020.10.29.339317v1