Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Mucus Proteome.

Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and traditionally challenging infections. However, traditional drug discovery methods face challenges such as time-consuming processes and high costs. Therefore, leveraging machine learning techniques to expedite the discovery of marine AMPs holds significant promise. Our study applies machine learning to develop marine AMPs, focusing on mucus rich in antimicrobial components. We conducted proteome sequencing of mucous proteins, used the iAMPCN model for peptide activity prediction, and evaluated the antimicrobial, hemolytic, and cytotoxic capabilities of six peptides. Proteomic analysis identified 4490 proteins, yielding about 43,000 peptides (8-50 amino acids). Peptide ranking based on length, hydrophobicity, and charge assessed antimicrobial potential, predicting 23 biological activities. Six peptides, distinguished by their high relative scores and promising biological activities, were chosen for bactericidal assay. Peptides P1 to P4 showed antimicrobial activity against , with P2 and P4 being particularly effective. All peptides inhibited growth. P2 and P4 also exhibited significant anti- effects, while P1 and P3 were non-cytotoxic to HEK293T cells at detectable concentrations. Minimal hemolytic activity was observed for all peptides even at high concentrations. This study highlights the potent antimicrobial properties of naturally occurring oyster mucus peptides, emphasizing their low cytotoxicity and lack of hemolytic effects. Machine learning accurately predicted biological activity, showcasing its potential in peptide drug discovery.