Open Collections

Garland, Olivia

University of British Columbia

The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs) targeting the virus’s essential proteins, such as Papain-like protease or PLpro. This enzyme plays a dual role participating in the maturation of viral proteins and in the suppression of the host immune system. In this work, we performed a virtual screening of ultra-large chemical libraries to identify prospective non-covalent PLpro inhibitors. The analysis of active compounds revealed their somewhat limited diversity, which is a recurring theme across publications on the discovery of PLpro ligands. This is likely attributed to the induced-fit nature of the enzyme’s active site which limits the effectiveness of rigid molecular docking protocols. Even with such constraint, we demonstrate that the identified compound VPC-300195 ranks among the top non-covalent PLpro inhibitors discovered through in silico methodologies and reported to date. After discovering an initial set of promising compounds through virtual screening, we developed a deep reinforcement learning-based approach to further optimize the hit molecules. This method modifies the candidate molecule inside of the protein pocket using fragment-based addition and replacement. We optimized the series of VPC-300195 derivatives on multiple parameters including Quantitative Estimate of Druglikeness (QED), Synthetic Accessibility (SA) score, predicted toxicity, and binding free energy. The state of the newly elaborated molecule was estimated using Molecular Mechanics Generalized Born Solvent Accessible Surface Area (MMGBSA) while performing complementary minimization (taking into account the solvent effects and protein dynamics). Taken together, the results of this work laid a foundation for the development of effective non-covalent SARS-CoV-2 inhibitors and provided a potential platform for effective optimization of initial hit compounds using methods of reinforcement learning.

Text

2024-01-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87232

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/