Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2

Abstract

Background: The main protease of SARS-CoV-2 (M pro ) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. Objective: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the M pro using the program AutoDock4. Methods: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for M pro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. Results: The application of docking against the structures available for the M pro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of M pro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. Conclusion: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of M pro . Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to M pro . This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.

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