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

ABSTRACT: 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 Crassostrea gigas mucus rich in antimicrobial components. We conducted proteome sequencing of C. gigas 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 protei

SUBMITTER: Song J 

PROVIDER: S-EPMC11432763 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature