High-throughput computational screening for identification of potential hits against bacterial Acriflavine resistance protein B (AcrB) efflux pump

Abstract

Antibiotic resistance is a pressing global health challenge, driven in part by the remarkable efflux capabilities of efflux pump in AcrB (Acriflavine Resistance Protein B) protein in Gram-negative bacteria. In this study, a multi-approached computational screening strategy encompassing molecular docking, In silico absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis, druglikeness assessment, molecular dynamics simulations and density functional theory studies was employed to identify novel hits capable of acting against AcrB-mediated antibiotic resistance. Ligand library was acquired from the COCONUT database. Performed computational analyses unveiled four promising hit molecules (CNP0298667, CNP0399927, CNP0321542 and CNP0269513). Notably, CNP0298667 exhibited the highest negative binding affinity of −11.5 kcal/mol, indicating a possibility of strong potential to disrupt AcrB function. Importantly, all four hits met stringent druglikeness criteria and demonstrated favorable in silico ADMET profiles, underscoring their potential for further development. MD simulations over 100 ns revealed that the CNP0321542-4DX5 and CNP0269513-4DX5 complexes formed robust and stable interactions with the AcrB efflux pump. The identified hits represent a promising starting point for the design and optimization of novel therapeutics aimed at combating AcrB-mediated antibiotic resistance in Gram-negative bacteria. Communicated by Ramaswamy H. Sarma