Machine Learning-Based Discovery of Antimicrobial Peptides and Their Antibacterial Activity Against Staphylococcus aureus

The escalating crisis of antibiotic resistance, particularly concerning foodborne pathogens such as Staphylococcus aureus and its biofilm contamination, has emerged as a major global challenge to food safety and public health. Biofilm formation significantly enhances the pathogen’s resistance to environmental stresses and disinfectants, underscoring the urgent need for novel antimicrobial agents. In this study, we isolated Bacillus strain B673 from the saline–alkali environment of Xinjiang, conducted whole-genome sequencing, and applied antiSMASH analysis to identify ribosomally synthesized and post-translationally modified peptide (RiPP) gene clusters. By integrating an LSTM-Attention-BERT deep learning framework, we screened and predicted nine novel antimicrobial peptide sequences. Using a SUMO-tag fusion tandem strategy, we achieved efficient soluble expression in an E. coli system, and the purified products exhibited remarkable inhibitory activity against Staphylococcus aureus (MIC = 3.13 μg/mL), with inhibition zones larger than those of the positive control. Molecular docking and dynamic simulations demonstrated that the peptides can stably bind to MurE, a key enzyme in cell wall synthesis, with negative binding free energy, suggesting an antibacterial mechanism via MurE inhibition. This study provides promising candidate molecules for the development of anti-drug-resistant agents and establishes an integrated research framework for antimicrobial peptides, spanning gene mining, intelligent screening, efficient expression, and mechanistic elucidation.