A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data...

Area of Science:

• Computational biology
• Drug discovery
• Virology

Background:

• The SARS-CoV-2 pandemic has caused millions of cases and deaths globally.
• Effective treatments for COVID-19 remain limited despite extensive research efforts.

Purpose of the Study:

• To accelerate the identification of drug repurposing candidates for SARS-CoV-2.
• To identify novel therapeutic agents targeting key viral proteins.

Main Methods:

• Development of a novel computational pipeline for comparing 3D protein structures.
• Integration of topological data analysis (TDA) with molecular docking and transcriptomic analyses.
• Screening of potential drug candidates against SARS-CoV-2 viral proteins (3CL protease, NSP15, NSP12).

Main Results:

• Identification of several potential drug repurposing candidates, including rutin, dexamethasone, and vemurafenib.
• Demonstration of a TDA-based strategy for protein structure comparison in drug repurposing.
• Targeting of crucial viral proteins: 3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase.

Conclusions:

• A novel computational pipeline effectively identifies drug repurposing candidates for SARS-CoV-2.
• Rutin, dexamethasone, and vemurafenib show potential as therapeutic agents against SARS-CoV-2.
• The study highlights the utility of TDA in accelerating drug discovery for infectious diseases.