Computational structure-based evaluation of antimicrobial peptides against OXA-51 β-lactamase in carbapenem-resistant...

Antimicrobial peptides (AMPs) constitute a promising class of next-generation therapeutics, exhibiting broad-spectrum efficacy and a diminished propensity for inducing resistance. Recent strides in computational biology have facilitated the rational design and high-throughput screening of AMPs tailored to target specific resistance mechanisms. In this study, we employed a structure-guided computational pipeline to identify and prioritize AMPs with inhibitory potential against OXA-51 β-lactamase, a pivotal enzyme contributing to carbapenem resistance in Acinetobacter baumannii. A comprehensive dataset comprising 300 AMPs, 250 of natural origin, and 50 synthetically engineered was curated through meta-analytical approaches. These peptides were systematically filtered based on key parameters, including physicochemical attributes, predicted toxicity, proteolytic stability, and aqueous solubility. Subsequent molecular docking analyses enabled the identification of eight high-affinity candidates, with AMP219 (NRC12), emerging as the top performer, exhibiting a binding energy of -214.98 kcal/mol. To further validate the binding stability and dynamic behavior of the AMP219 with the OXA-51 complex, a 300-nanosecond molecular dynamics simulation (MDS) was conducted. The results revealed sustained intermolecular interactions, persistent hydrogen bonding, and notable conformational rearrangements within the enzyme's active site, underscoring the peptide's inhibitory potential. Collectively, these findings emphasize the utility of integrative computational strategies in accelerating peptide-based drug discovery and provide a robust foundation for subsequent experimental validation against multidrug-resistant pathogens.