Identification of new potential inhibitors of quorum sensing through a specialized multi‐level computational approach

Abstract

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self‐produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum‐sensing (QS), an important process of cell‐to‐cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum‐sensing, using CviR—the quorum‐sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein‐ligand docking (with 7 different docking programs/scoring functions), receptor‐based virtual screening, molecular dynamic simulations, and free energy cal-culations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target‐specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA‐ Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repur-posing towards QS inhibition.