A Structure-Informed Graph Neural Network for Short Antimicrobial Peptide Discovery and Validation.
TL;DR
GW-AMP demonstrated robust and balanced classification performance in both cross-validation and an independent test set, outperforming several established AMP prediction models, and indicated that secondary-structure features and amphipathic distribution are closely linked to peptide potency and selectivity.
Abstract
Short antimicrobial peptides (AMPs) are promising anti-infective agents due to their broad-spectrum antimicrobial activity, low likelihood of inducing resistance, and relative ease of synthesis and optimization. In this study, we developed GW-AMP, a residue-level graph neural network model that integrates sequence-derived descriptors with predicted structural information to facilitate the discovery of short AMPs. GW-AMP demonstrated robust and balanced classification performance in both cross-validation and an independent test set, outperforming several established AMP prediction models. Guided by model predictions, candidate peptides were selected and experimentally evaluated for antibacterial activity and hemolysis, confirming GW-AMP's effectiveness in identifying short AMPs with favorable activity and biocompatibility. Circular dichroism analysis further indicated that secondary-structure features and amphipathic distribution are closely linked to peptide potency and selectivity. Among the validated candidates, peptide 3 exhibited potent antibacterial activity, low hemolysis, and favorable in vivo efficacy and safety, supporting its potential as a lead compound for anti-infective development.