Classifying Antimicrobial and Multifunctional Peptides with Bayesian Network Models

TL;DR

This paper applies Bayesian network models to classify antimicrobial peptides, integrating chemical knowledge and sequence motifs to improve interpretability and prediction accuracy, and exploring factors influencing peptide activity.

Contribution

It introduces a novel Bayesian network approach for peptide activity prediction that embeds chemical knowledge without complex derivations, enhancing interpretability and flexibility.

Findings

Achieved 94% accuracy in predicting antimicrobial activity

Identified background amino acid distribution as influential in activity

Demonstrated potential for dual antimicrobial and antifouling peptides

Abstract

Bayesian network models are finding success in characterizing enzyme-catalyzed reactions, slow conformational changes, predicting enzyme inhibition, and genomics. In this work, we apply them to statistical modeling of peptides by simultaneously identifying amino acid sequence motifs and using a motif-based model to clarify the role motifs may play in antimicrobial activity. We construct models of increasing sophistication, demonstrating how chemical knowledge of a peptide system may be embedded without requiring new derivation of model fitting equations after changing model structure. These models are used to construct classifiers with good performance (94% accuracy, Matthews correlation coefficient of 0.87) at predicting antimicrobial activity in peptides, while at the same time being built of interpretable parameters. We demonstrate use of these models to identify peptides that are potentially both antimicrobial and antifouling, and show that the background distribution of amino acids could play a greater role in activity than sequence motifs do. This provides an advancement in the type of peptide activity modeling that can be done and the ease in which models can be constructed.