Targeting viral RNA pseudoknots: a multi-level computational approach to identify RNA-binding novel small molecules.

Abstract

The RNA pseudoknot of SARS-CoV-2 plays a pivotal role in - 1 programmed ribosomal frameshifting (- 1 PRF), which is essential for viral protein synthesis and replication. Targeting this RNA structural element offers a novel therapeutic strategy against COVID-19. In this study, we applied an integrative computational approach combining molecular docking, MM-GBSA binding free energy calculations, ADME-Tox profiling, and extended 500 ns molecular dynamics simulations to identify small molecules capable of disrupting the pseudoknot function. F2879-5340 emerged as a promising RNA-targeting candidate, demonstrating stable interactions with key pseudoknot nucleotides and favorable ΔG_bind values. Compared to the control compound Nafamostat, F2879-5340 exhibited superior predicted pharmacokinetic properties, including higher intestinal absorption, better bioavailability, and no mutagenic potential. These results suggest that F2879-5340 is a potent candidate for further experimental validation as an orally bioavailable - 1 PRF inhibitor. This work presents a novel computational pipeline for RNA-targeted drug discovery in the context of SARS-CoV-2.