AUTHOR=Mohammad Abeer , Zheoat Ahmed , Oraibi Amjad , Manaithiya Ajay , S. Almaary Khalid , Allah Nafidi Hiba , Bourhia Mohammed , Kilani-Jaziri Soumaya , A. Bin Jardan Yousef
TITLE=Integrating virtual screening, pharmacoinformatics profiling, and molecular dynamics: identification of promising inhibitors targeting 3CLpro of SARS-CoV-2
JOURNAL=Frontiers in Molecular Biosciences
VOLUME=10
YEAR=2024
URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2023.1306179
DOI=10.3389/fmolb.2023.1306179
ISSN=2296-889X
ABSTRACT=Introduction: The pursuit of effective therapeutic solutions for SARS-CoV-2 infections and COVID-19 necessitates the repurposing of existing compounds. This study focuses on the detailed examination of the central protease, 3-chymotrypsin-like protease (3CLpro), a pivotal player in virus replication. The combined approach of molecular dynamics simulations and virtual screening is employed to identify potential inhibitors targeting 3CLpro.
Methods: A comprehensive virtual screening of 7120 compounds sourced from diverse databases was conducted. Four promising inhibitors, namely EN1036, F6548-4084, F6548-1613, and PUBT44123754, were identified. These compounds exhibited notable attributes, including high binding affinity (ranging from −5.003 to −5.772 Kcal/mol) and superior Induced Fit Docking scores (ranging from −671.66 to −675.26 Kcal/mol) compared to co-crystallized ligands.
Results: In-depth analysis revealed that F6548-1613 stood out, demonstrating stable hydrogen bonds with amino acids His41 and Thr62. Notably, F6548-1613 recorded a binding energy of −65.72 kcal/mol in Molecular Mechanics Generalized Born Surface Area (MMGBSA) simulations. These findings were supported by Molecular Dynamics simulations, highlighting the compound’s efficacy in inhibiting 3CLpro.
Discussion: The identified compounds, in compliance with Lipinski’s rule of five and exhibiting functional molecular interactions with 3CLpro, present promising therapeutic prospects. The integration of in silico methodologies significantly expedites drug discovery, laying the foundation for subsequent experimental validation and optimization. This approach holds the potential to develop effective therapeutics for SARS-CoV-2.