SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification

TL;DR

This paper introduces SGAC, a graph neural network framework that uses structural peptide information and advanced learning techniques to improve antimicrobial peptide classification, especially under class imbalance.

Contribution

The paper presents a novel GNN-based framework, SGAC, integrating structure-aware features and imbalance handling methods for enhanced AMP classification.

Findings

SGAC outperforms existing methods on benchmark datasets.

Structural features significantly improve classification accuracy.

Imbalance techniques enhance model robustness and generalization.

Abstract

Classifying Antimicrobial Peptides (AMPs) from the vast collection of peptides derived from metagenomic sequencing offers a promising avenue for combating antibiotic resistance. However, most existing AMP classification methods rely primarily on sequence-based representations and fail to capture the spatial structural information critical for accurate identification. Although recent graph-based approaches attempt to incorporate structural information, they typically construct residue- or atom-level graphs that introduce redundant atomic details and increase structural complexity. Furthermore, the class imbalance between the small number of known AMPs and the abundant non-AMPs significantly hinders predictive performance. To address these challenges, we employ lightweight OmegaFold to predict the three-dimensional structures of peptides and construct peptide graphs using C {\alpha} atoms to capture their backbone geometry and spatial topology. Building on this representation, we propose the Spatial GNN-based AMP Classifier (SGAC), a novel framework that leverages Graph Neural Networks (GNNs) to extract structural features and generate discriminative graph representations. To handle class imbalance, SGAC incorporates Weight-enhanced Contrastive Learning to cluster structurally similar peptides and separate dissimilar ones through adaptive weighting, and applies Weight-enhanced Pseudo-label Distillation to generate high-confidence pseudo labels for unlabeled samples, achieving balanced and consistent representation learning. Experiments on publicly available AMP and non-AMP datasets demonstrate that SGAC significantly achieves state-of-the-art performance compared to baselines.