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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月19日

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  • Jinjin Li,
  • Fang Fang,
  • Changhang Lin,
  • Hua Shi,
  • Feifei Cui,
  • Zilong Zhang,
  • Leyi Wei

摘要

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.