Foods, Vol. 15, Pages 541: Rational Design and Virtual Screening of Antimicrobial Terpene-Based Leads from Marrubium vulgare Essential Oil: Structure-Based Optimization for Food Preservation and Safety Applications

Fuente: Foods - Revista científica (MDPI)
Foods, Vol. 15, Pages 541: Rational Design and Virtual Screening of Antimicrobial Terpene-Based Leads from Marrubium vulgare Essential Oil: Structure-Based Optimization for Food Preservation and Safety Applications
Foods doi: 10.3390/foods15030541
Authors:
Ahmed Bayoudh
Nidhal Tarhouni
Raoudha Sadraoui
Bilel Hadrich
Alina Violeta Ursu
Guillaume Pierre
Pascal Dubessay
Philippe Michaud
Imen Kallel

Pseudomonas aeruginosa elastase LasB accelerates refrigerated food spoilage through proteolytic degradation of muscle and milk proteins. While Marrubium vulgare essential oil terpenes exhibit antimicrobial activity, their weak potency and nonspecificity limit direct food preservation applications. This computational study aimed to rationally redesign terpene scaffolds into predicted selective LasB inhibitors. A virtual library of 635 terpene–peptide–phosphinic acid hybrids (expanded to 3940 conformers) was evaluated using consensus molecular docking (Glide/Flare) against LasB (PDB: 3DBK) and three human off-target proteases. Top candidates underwent duplicate 150 ns molecular dynamics simulations with MM/GBSA binding free-energy calculations. Computational screening identified thymol–Leu–Trp–phosphinic acid as the lead candidate with predicted binding affinity of −12.12 kcal/mol, comparable to reference inhibitor phosphoramidon (−11.87 kcal/mol), and predicted selectivity index of +0.12 kcal/mol representing a 2.3 kcal/mol advantage over human proteases. Molecular dynamics simulations indicated exceptional stability (98.7% stable frames, 0.12 Å inter-replica RMSD) with consistent zinc coordination. Structure–activity analysis revealed phosphinic zinc-binding groups (+1.57 kcal/mol), Leu–Trp linkers (+2.47 kcal/mol), and phenolic scaffolds (+1.35 kcal/mol) as predicted optimal structural features. This in silico study provides a computational framework and prioritized candidate set for developing natural product-derived food preservatives. All findings represent computational predictions requiring experimental validation through enzymatic assays, food model studies, and toxicological evaluation.