In Silico End-to-End Protein-Ligand Interaction Characterization Pipeline: The Case of SARS-CoV-2

SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an efl'ective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (M-pro), which has been extensively studied in the last few months. There is an urgency for developing efl'ective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein-ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 M-pro with an alpha-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein-ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 M-pro and the alpha-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.

Automated summary

This study introduced computational protocols for targeting the SARS-CoV-2 main protease, including uploading, visualizing, and managing three-dimensional structures, homology modeling, protein-ligand docking, virtual screening, and molecular dynamics simulations. These protocols offer important insights into addressing one of the biggest biological problems currently facing the world.