In Silico Mining of NPACT Database Toward Identification of EBNA1 Inhibitor: Virtual Screening, Molecular Dynamics Simulations, and DFT Calculations.

Abstract

Epstein-Barr nuclear antigen 1 (EBNA1) is an attractive therapeutic target for identifying pharmaceutical drug molecules to fight Epstein-Barr virus (EBV) contagion because of its key function in viral reproduction. To find potent EBNA1 inhibitors, the Naturally Occurring Plant-based Anticancer Compound-Activity-Target (NPACT) database, including > 1500 compounds, was filtered utilizing computational approaches. The efficiency of the docking technique used to anticipate the inhibitor-EBNA1 binding pose was initially evaluated based on obtainable experimental data. Upon the computed docking scores, molecular dynamics simulations (MDSs) were executed for the most superior NPACT compounds bound to EBNA1, accompanied by binding affinity estimations utilizing the MM/GBSA approach. According to binding affinity computations over 200 ns MDS, bitucarpin A demonstrated stronger ΔG _binding than KWG, an EBNA1 reference inhibitor, with values of -39.1 and -32.4 kcal/mol, respectively. Post-MD analyses assured the steadiness of bitucarpin A inside the EBNA1 binding pocket over 200 ns MDS. Besides, pharmacokinetics, physicochemical, and toxicity features were predicted for bitucarpin A and demonstrated its promising oral bioavailability. Density functional theory calculations were executed, and their outcomes substantiated the results given by docking and MDS computations. According to these findings, bitucarpin A showed promising inhibitory activity as a potent EBNA1 inhibitor that may be a prospective anti-EBV drug candidate.