Abstract
BACKGROUND: Diabetes is a global metabolic disease that urgently calls for the development of new and effective therapeutic agents. Anti-diabetic peptides (ADPs) have emerged as a research hotspot due to their therapeutic potential and natural safety, representing a promising class of functional peptides for diabetic management. However, conventional computational approaches for ADPs prediction mainly rely on manually extracted sequence features. These methods often lack generalizability and perform poorly on short peptides, thereby hindering effective ADPs discovery. RESULTS: In this study, we introduce a fine-tuning strategy of large-scale pre-trained protein language models (PLMs) for ADPs prediction, enabling automated extraction of discriminative semantic representations. We established the most comprehensive ADPs dataset to date, comprising 899 rigorously curated non-redundant ADPs and 67 newly collected potential candidates. Based on three model construction strategies, we developed 11 candidate models. Among them, BertADP (a fine-tuned ProtBert model) demonstrated superior performance in the independent test set, outperforming existing ADPs prediction tools with an overall accuracy of 0.955, sensitivity of 1.000, and specificity of 0.910. Notably, BertADP exhibited remarkable sequence length adaptability, maintaining stable performance across both standard and short peptide sequences. CONCLUSIONS: BertADP represents the first PLMs-based intelligent prediction tool for ADPs, whose exceptional identification capability will significantly accelerate anti-diabetic drug development and facilitate personalized therapeutic strategies, thereby enhancing precision diabetes management. Furthermore, the proposed approach provides a generalizable framework that can be extended to other bioactive peptide discovery studies, offering an innovative solution for bioactive peptide mining.