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

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ID: 319587
2026
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Abstract
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.
Reference Key
openalex_W7167270649 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wenping Yu, Zhewen Li, Wei Xu, Yuanzhang Zhao, Nan Sun
Journal Briefings in bioinformatics
Year 2026
DOI
10.1093/bib/bbag361
URL
https://doi.org/10.1093/bib/bbag361
Keywords Keywords not found

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