De Novo Antimicrobial Peptide Design via Physicochemical Optimization: Targeting ESKAPE Pathogens
Antimicrobial resistance threatens modern medicine, demanding novel therapeutics. This study develops a computational framework for de novo design of antimicrobial peptides (AMPs) targeting ESKAPE pathogens (Enterococcus, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacteriaceae) using genetic algorithm optimization. Design constraints utilize real amino acid properties (Kyte-Doolittle hydrophobicity, charge at pH 7.4, amphipathicity) and structure-activity relationships from >3000 known AMPs in the APD3 database. Genetic algorithm optimization over 50 generations with 100-peptide populations yields peptides with optimal properties: net charge +5 to +8, amphipathicity >0.40, hydrophobic fraction 40-60%. Designed peptides achieve 70-90% predicted efficacy scores against ESKAPE organisms compared to benchmark peptides (LL-37, Magainin-2, Cecropin A). Pareto front analysis reveals charge-amphipathicity trade-offs: peptides with +7 charge and amphipathicity 0.45 show optimal predicted activity. Model predictions correlate well with known AMP activity mechanisms (helical structure formation, membrane permeabilization). The framework generalizes to design peptides for any target organism by modulating selection pressures. Our optimized sequences, including helical wheel projections and detailed property profiles, provide candidate leads for chemical synthesis and in vitro validation against resistant ESKAPE strains.