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

Area of Science:

• Virology
• Computational Chemistry
• Drug Discovery

Background:

• The SARS-CoV-2 RNA pseudoknot is crucial for viral replication via -1 programmed ribosomal frameshifting (-1 PRF).
• Targeting this RNA structure presents a therapeutic strategy against COVID-19.

Purpose of the Study:

• To identify small molecules that disrupt the SARS-CoV-2 RNA pseudoknot function using computational methods.
• To evaluate potential drug candidates for their efficacy and pharmacokinetic properties.

Main Methods:

• Integrative computational approach including molecular docking, MM-GBSA binding free energy calculations.
• ADME-Tox profiling and 500 ns molecular dynamics simulations were performed.
• Candidate molecules were assessed for interactions with pseudoknot nucleotides and binding affinity.

Main Results:

• F2879-5340 was identified as a promising RNA-targeting candidate with stable interactions and favorable binding free energy (ΔG_bind).
• F2879-5340 demonstrated superior predicted pharmacokinetic properties compared to Nafamostat, including better bioavailability and no mutagenicity.
• The compound showed potential as an orally bioavailable -1 PRF inhibitor.

Conclusions:

• F2879-5340 is a potent candidate for further experimental validation as a COVID-19 therapeutic.
• The study presents a novel computational pipeline for RNA-targeted drug discovery against SARS-CoV-2.