Computational exploration of high-throughput virtual screening and molecular dynamics analysis targeting BfmR in Acinetobacter baumannii: Towards anti-virulent therapeutics.

The opportunistic pathogen Acinetobacter baumannii, a major cause of nosocomial infections, has exhibited a rapid increase in resistance to conventional antimicrobial therapies, emphasizing the urgent need for alternative strategies such as anti-virulence approaches. The response regulator BfmR is a critical mediator of biofilm formation and virulence, making it an attractive yet underexplored therapeutic target. In this study, we established a comprehensive in silico pipeline to identify potential BfmR inhibitors through large-scale virtual screening and advanced computational analyses. The crystal structure of BfmR (PDB ID: 5HM6) was prepared using Schrödinger's Protein Preparation Wizard with the OPLS3 force field. A compound library comprising 66,734 molecules from CMNPD, Enamine, ChemDiv, and Asinex databases was processed using LigPrep and Epik to generate appropriate protonation states and stereoisomers at physiological pH. Virtual screening was performed using GLIDE in a hierarchical workflow including HTVS, SP, and XP docking. Pharmacokinetic and toxicity profiles were assessed using QikProp to ensure drug-likeness. Top-scoring compounds were further evaluated using triplicate 500 ns molecular dynamics simulations in GROMACS 2023 with the CHARMM36 force field, employing TIP3P water models and system neutralization. Post-simulation analyses included RMSD, PCA, free energy landscape mapping, dynamic cross-correlation matrices, covariance analysis, and MM-PBSA binding energy calculations. Six lead compounds demonstrated stable binding, favorable energetics, and consistent interactions with key regulatory residues THR23, ARG29, and VAL109, highlighting their potential as promising anti-virulence agents against A. baumannii.