Based on computer simulation and experimental verification: mining and characterizing novel antimicrobial peptides from soil microbiome.

Antimicrobial peptides (AMPs) show great promise for enhancing food safety and extending shelf life, but traditional screening methods are complex and costly. To address these issues, we developed a deep learning-based prediction pipeline to identify potential AMPs from soil metagenomic data, achieving high accuracy (92.71 %) and precision (91.29 %). Based on model scoring, surface charge, and Hemopred and ToxinPred screenings, we identified nine candidate peptides. Peptide P4 (GTAWRWHYRARS) showed the best binding affinity to MrkH in molecular docking studies and was validated through molecular dynamics simulations. The chemically synthesized P4 demonstrated significant antimicrobial activity against Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus, indicating its potential as an effective alternative to traditional food antimicrobial agents. This study highlights the effectiveness of our integrated prediction pipeline for discovering new AMPs.