In silico identification of antiviral compounds for the treatment of chikungunya virus infection: qsar modelling and md simulation analysis

Abstract

Chikungunya virus (CHIKV), transmitted by arthropods, has gained global recognition for its impact on public health. It has expanded globally, including Africa, Asia, and the Indian subcontinent, and has a helicase protein in its genome that is crucial for its replication. Thus, the study targeted the helicase protein of CHIKV with 745 antiviral compounds using an ML-based QSAR model and molecular docking. Top binders (5279172, 78161839, 6474310, and 5330286) were selected for MD simulation based on the control (Silvestrol). All compounds had the highest binding scores, with 78161839 showing the most consistent RMSD and the least conformational variation, indicating high stability. It also showed the lowest binding free energy (ΔG ​= ​−31.31 ​kcal/mol), indicating energetically favourable binding. PCA and FEL also depicted the stable complex confirmation of the protein and 78161839 complex during the 100 ns simulation. Overall, this study aimed to identify helicase function antiviral binders that could be experimentally tested for treating CHIKV.

Keywords

• Chikungunya virus
• Machine learning based QSAR
• Molecular dynamic solution
• Helicase