Classifying Antimicrobial and Multifunctional Peptides with Bayesian Network Models
This work provides an incremental advancement in peptide activity modeling, potentially aiding in the design of antimicrobial and multifunctional peptides for biomedical applications.
The researchers tackled the problem of predicting antimicrobial activity in peptides by applying Bayesian network models to identify sequence motifs and build interpretable classifiers, achieving 94% accuracy and a Matthews correlation coefficient of 0.87.
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.