ArticleScientific reports2026
Multimodal computational discovery of MvfR inhibitors targeting quorum sensing in multi-drug-resistant Pseudomonas aeruginosa.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pseudomonas aeruginosa is a major global health concern due to its multidrug resistance (MDR), necessitating the urgent development of novel therapeutic strategies. Understanding the molecular basis of resistance in clinical isolates is critical for designing next-generation antimicrobials. This study analysed recent clinical isolates of P. aeruginosa obtained from the NCBI for their resistance gene and virulence factor profiles. Among the virulence-associated targets, MvfR, a key transcriptional regulator of quorum sensing and biofilm formation, was prioritized based on its functional relevance. AI modelling of MvfR identified from the genome analysis was performed, followed by molecular docking against library of compounds, phylogenetic comparisons to compare with previously identified homologs, ADMET-profiling, 500 ns molecular dynamics (MD) simulations, binding free energy, and Density Functional Theory (DFT). Genes critical for antimicrobial resistance, drug targeting, and virulence factors were identified across multiple databases. The antimicrobial resistance genes and receptors revealed key resistance mechanisms, including antibiotic-inactivating enzymes, efflux pumps, quorum sensing, and alterations in cell wall charge or permeability. Notably, (S)-1-(2-(difluoromethyl)-1 H-benzo[d]imidazol-5-yl)-3-(2-hydroxy-2-(pyridin-4-yl)ethyl)urea exhibited the highest docking score against MvfR. DFT and MD simulations over 500 ns demonstrated stability of the top ligands, supported by favourable molecular stability parameters such as RMSD, SASA, RMSF, and Rg plots. Furthermore, the top-ranking ligands satisfied Lipinski's rule of five, suggesting favourable drug-like properties. This study provides an integrated computational characterization of MvfR in recent P. aeruginosa isolates and identifies genetic variations that may influence disease manifestation. It further demonstrates an integrative computational strategy to accelerate discovery of promising antimicrobial agents against multidrug-resistant bacteria.
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