Structure-Based identification of potent dengue virus RNA-dependent RNA polymerase inhibitors using integrated computational workflow
TL;DR
An integrated computational workflow was applied to identify potential inhibitors of dengue virus RNA-dependent RNA polymerase (RdRp) after screening of approximately 9,900 bioactive compounds through structure-based virtual screening and molecular docking, supporting these compounds as promising RdRp inhibitors.
Abstract
ABSTRACT An integrated computational workflow was applied to identify potential inhibitors of dengue virus RNA-dependent RNA polymerase (RdRp) after screening of approximately 9,900 bioactive compounds through structure-based virtual screening and molecular docking. Promising hits were further evaluated using density functional theory (DFT), molecular dynamics (MD) simulations, MM/GBSA free energy calculations, principal component analysis (PCA), free energy landscape (FEL) analysis, machine learning – based QSAR, and ADMET profiling. Redocking highlighted F2924-0102, F3299-0084, and F1411-0380 as top candidates, with docking scores of −12.6, −9.7, and −9.1 kcal/mol, respectively, comparable to the reference inhibitor 68 T (−8.6 kcal/mol). Replicated 500 ns MD simulations demonstrated stable conformations with consistent RMSD, RMSF, radius of gyration (RoG), and solvent-accessible surface area (SASA), indicating system stability and convergence. MM/GBSA analysis revealed favourable binding free energies, particularly for F1411-0380 (−64.02 ± 4.94 kcal/mol) and F2924-0102 (−57.73 ± 4.36 kcal/mol), primarily driven by van der Waals and hydrophobic interactions. Energy decomposition confirmed stable binding to catalytic residues. PCA and FEL analyses identified F2924-0102 as the most stable complex. QSAR predicted pIC50 values between 7.110 and 7.279, while ADMET results indicated good pharmacokinetic properties, supporting these compounds as promising RdRp inhibitors.