SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation

Abstract Therapeutic peptides show many biological activities and are now widely viewed as promising candidates for new drug development. Accurate functional annotation of therapeutic peptides is still difficult. This difficulty comes from their short sequence length, strong structural flexibility, and the presence of multiple biological functions within a single peptide.Here, we introduce Structure-Aware Multi-Label Therapeutic Peptide Predictor (SA-MTP), a structure-aware framework designed for multifunctional annotation of therapeutic peptides. SA-MTP combines pretrained protein language models with a graph attention network to capture sequence semantics and probabilistic structural features. Input-dependent structure-aware graphs are constructed to describe conformational variation, which is especially common in short peptides. Benchmarking experiments across 15 therapeutic function categories were conducted using datasets. The results show that SA-MTP achieves better performance than existing methods across several evaluation metrics, including accuracy, F1-score, and Matthews correlation coefficient.