AI-driven antimicrobial peptide characterization unveils novel motifs for drug design.

Antibiotics have been developed to effectively target and eliminate bacteria, but the rise in antimicrobial resistance (AR) complicates the treatment of certain infections. To address this issue, researchers have explored antimicrobial peptides (AMPs) that disrupt bacterial membranes. A promising method for this exploration is motif-based analysis, which identifies hidden patterns in AMPs to better understand their mechanism of action. While existing methods rely on expert knowledge, incorporating topic models can enhance analysis by revealing the contextual relationships between sequence elements. This is complemented by a data analytics tool designed to analyze AMP motifs and their biochemical properties. Such integration allows for the extraction of valuable motifs and the development of a robust data analytics module for predicting membrane activity. Additionally, we evaluated the biological relevance of motifs by extracting biochemical features, making structural predictions via Evolutionary Scale Modeling (ESM). Our results indicate that topic model-derived motifs are strongly associated with antimicrobial activity and demonstrate lower minimum inhibitory concentration values and capture contextual information more effectively than traditional frequency-based motifs. We also performed a comparative analysis between the two approaches regarding motif evolution, sequence-level attributes, and entropy measures, ultimately contributing to ongoing efforts to combat AR.