Work

ProjFusNet: deep neural network for peptide precursor prediction using projection-fused protein language model and structural features

Year: 2025

Type: article

Abstract: Peptide precursors, as the source molecules of bioactive peptides, play essential roles in neuroregulation, immune defense, and drug development. Their accurate identification is crucial for elucidating mechanisms of life regulation and developing novel therapeutics. However, the complexity and diversity of peptide precursor sequences pose significant challenges to prediction tasks. Existing methods predominantly rely on sequence features or structural features, hindering the full exploitation of complementary information between modalities and consequently limiting prediction performance. We introduce ProjFusNet, a deep learning framework that integrates evolutionary-scale protein sequence representations from ESM-2 with structural features via a projected multimodal fusion strategy. A bidirectional LSTM is further employed to model the complex interactions between sequence and structure. In rigorous five-fold cross-validation, ProjFusNet demonstrates improved performance across key metrics, including ACC, SN, AUC, SP, and MCC, compared to single-feature models. This study introduces ProjFusNet, a novel deep learning framework to integrate evolutionary-scale sequence representations (ESM-2) and protein structural features via a projected multimodal fusion strategy for peptide precursor prediction. This approach overcomes the limitations of existing methods that rely solely on single modalities, enabling the effective exploitation of complementary information and achieving higher accuracy and robustness across key metrics. By providing a powerful tool for the efficient and accurate identification of peptide precursors, ProjFusNet has the potential to contribute to neuropharmacology research and peptide-based drug development. Future work should focus on further validating the model’s generalizability and exploring its application in broader contexts.

Source: Journal of Cheminformatics

DOI: 10.1186/s13321-025-01117-x