In silico mining of bovine RNAs uncovers novel amps with potent activity against gram-negative pathogens

Antimicrobial peptides (AMPs) are vital components of the innate immune system, offering a promising alternative to conventional antibiotics. This study utilized in silico approaches to identify putative AMPs from publicly available bovine transcriptomic data. Using a curated pipeline integrating transcriptome annotation, open reading frame (ORF) prediction, and AMP-specific bioinformatics tools, we screened and predicted candidate sequences with antimicrobial potential. Further structural and functional validation was conducted using physicochemical analysis, motif identification, and homology modeling. Several peptides with strong AMP characteristics were identified, particularly rich in arginine and cysteine residues, indicating probable efficacy against gram-negative pathogens. These findings provide a foundation for future in vitro validation and potential therapeutic application of bovine-derived AMPs.