Discovery of antimicrobial peptides from Bacillus genomes against phytopathogens with deep learning models

Antimicrobial peptides (AMPs) have emerged as a potential novel class of antimicrobial agents due to their broad microbial targeting and low resistance risks. Although AMPs have limited applications in agriculture, their potential to replace chemical pesticides could address food security and environmental concerns. Members of Bacillus sp., abundant in soil and plant microbiomes, are recognized as important sources of AMPs for their resistance and strong antimicrobial properties, making them ideal candidates for biocontrol in sustainable agriculture. To harness this potential, this study employed deep learning models to predict AMPs derived from Bacillus genomes, aiming to identify candidates with high activity against phytopathogens. Subsequently, the antimicrobial efficacy of selected candidate AMPs was experimentally validated. More than 6700 Bacillus genomes were collected to identify a broad range of short peptides (10–100 amino acids), which were analyzed using advanced deep learning models, including BERT, Mamba, CNN-LSTM, and CNN-Attention. These models demonstrated enhanced predictive accuracy and reliability over existing methods, and resulted in 4,993,389 potential AMPs from Bacillus genomes. Among these AMPs, two high-confidence AMPs (cAMP_1 and cAMP_2) were selected by cross-validation, and their structural stability and activity were evaluated and verified by molecular dynamics simulations and experimental assays, respectively. Both of them exhibited antimicrobial activity against Escherichia coli, Staphylococcus aureus, and various common agricultural fungal and bacterial pathogens. This high-throughput deep learning pipeline successfully uncovered novel AMPs from Bacillus genomes, which underscored the efficiency of deep learning models in identifying functional peptides. This approach could accelerate the discovery of potential AMPs for biocontrol applications in plant disease management, contributing to sustainable agriculture and reduced dependency on traditional antibiotics.