Authors
Xin-Yu Li, Yang Yu, Li-Ping Liu, Meng-Ze Du, Qiang Tang, Juan-Juan Kang
Published in
iScience. Volume 29. Issue 10. Pages 117680. Oct 16, 2026. Epub Sep 29, 2026.
Abstract
Neuropeptides are key signaling molecules that regulate the nervous and endocrine systems, and their maturation depends on precise proteolytic cleavage of precursor proteins. However, residue-level annotation remains essential yet experimentally labor intensive, with limited dedicated computational tools. Here, we present DeepNPA, a residue-level annotation framework that integrates ProtBert, a pretrained protein language model, with focal loss for imbalance-aware optimization. DeepNPA was trained and evaluated on a non-redundant Swiss-Prot-derived dataset annotated using the BIO scheme. On a held-out test dataset, DeepNPA achieved strong performance, with an accuracy of 0.953, an F1-score of 0.927, and an AUPRC of 0.965. On an independent benchmark dataset, DeepNPA outperformed NeuroCS and NeuroPred, achieving an ACC of 0.969 and Matthews correlation coefficient (MCC) of 0.666. External validation further demonstrated its ability to identify neuropeptide regions and their associated cleavage sites. In addition, we provided an online prediction platform to facilitate neuropeptide precursor annotation and cleavage site analysis.
PMID:
42861395
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.
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