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<p>The discovery of neuropeptides offers numerous opportunities for identifying novel drugs and targets to treat a variety of diseases. While various computational methods have been proposed, there remains potential for further performance improvement. In this work, we introduce NeuroPpred-MSN, an innovative and efficient neuropeptide prediction model that leverages multi-feature fusion and Siamese networks. To comprehensively represent the information of neuropeptides, the peptide sequences are encoded by four encoding schemes (token embedding, word2vec embedding, protein language embedding, and handcrafted features). Then, the token embedding and word2vector embedding are fed to a Siamese network channel. In the other channel of the model, peptide sequences and their secondary structure sequences are fed into ProtT5-XL-UniRef50 model to generate the embedding features, while handcrafted encoding techniques are used to extract the physicochemical information. Then the two kinds of features are fused and fed into a bidirectional gated recurrent unit (Bi-GRU) network for further processing. Ultimately, the outputs of the two channels are integrated into a fully connected layer, thereby facilitating the generation of the final prediction. The results on the independent test set indicate that NeuroPpred-MSN exhibits superior predictive performance, with an area under the receiver operating characteristic curve (AUROC) of 98.3%, exceeding the performance of other state-of-the-art predictors. Specifically, compared to other optimal results, this model exhibits improvements of 1.52% in accuracy (ACC), 1.52% in F1 score (F1), 3.2% in Matthews correlation coefficient (MCC), and 1.55% in AUROC. The model was further evaluated on imbalanced datasets, where it achieved the highest values in AUROC, ACC, MCC, sensitivity (SN), and F1, further demonstrating its robustness and generalization. The model can be accessed at the following GitHub repository: <a href="https://github.com/wenjean/NeuroPpred-MSN">https://github.com/wenjean/NeuroPpred-MSN</a>.</p> Graphical Abstract <p></p>

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NeuroPpred-MSN: A Neuropeptide Prediction Model Based on Multi-feature Fusion and Siamese Networks

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • Jian Wen,
  • Minyu Chen,
  • Yongqi Shen,
  • Honghong Wang,
  • Zhuoyu Wei,
  • Lichuan Gu,
  • Xiaolei Zhu

摘要

The discovery of neuropeptides offers numerous opportunities for identifying novel drugs and targets to treat a variety of diseases. While various computational methods have been proposed, there remains potential for further performance improvement. In this work, we introduce NeuroPpred-MSN, an innovative and efficient neuropeptide prediction model that leverages multi-feature fusion and Siamese networks. To comprehensively represent the information of neuropeptides, the peptide sequences are encoded by four encoding schemes (token embedding, word2vec embedding, protein language embedding, and handcrafted features). Then, the token embedding and word2vector embedding are fed to a Siamese network channel. In the other channel of the model, peptide sequences and their secondary structure sequences are fed into ProtT5-XL-UniRef50 model to generate the embedding features, while handcrafted encoding techniques are used to extract the physicochemical information. Then the two kinds of features are fused and fed into a bidirectional gated recurrent unit (Bi-GRU) network for further processing. Ultimately, the outputs of the two channels are integrated into a fully connected layer, thereby facilitating the generation of the final prediction. The results on the independent test set indicate that NeuroPpred-MSN exhibits superior predictive performance, with an area under the receiver operating characteristic curve (AUROC) of 98.3%, exceeding the performance of other state-of-the-art predictors. Specifically, compared to other optimal results, this model exhibits improvements of 1.52% in accuracy (ACC), 1.52% in F1 score (F1), 3.2% in Matthews correlation coefficient (MCC), and 1.55% in AUROC. The model was further evaluated on imbalanced datasets, where it achieved the highest values in AUROC, ACC, MCC, sensitivity (SN), and F1, further demonstrating its robustness and generalization. The model can be accessed at the following GitHub repository: https://github.com/wenjean/NeuroPpred-MSN.

Graphical Abstract