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<p>Anti-inflammatory peptides (AIPs) have emerged as potential therapeutic candidates for managing various inflammatory disorders, but their computational identification remains challenging. We propose AIP-TranLAC, a novel deep learning framework that integrates Transformer-based embedding, bidirectional long short-term memory (Bi-LSTM), multi-head attention, and convolutional neural network (CNN) to classify AIPs accurately. Our model achieves superior performance on benchmark and independent test datasets, demonstrating significant improvements over existing methods. The hybrid architecture effectively captures local and global sequence patterns, while interpretability analyses reveal critical amino acid residues. With robust performance on imbalanced data and open-source availability, AIP-TranLAC provides a powerful tool for accelerating therapeutic peptide discovery and inflammation research. For reproducibility purposes, we have released the codebase, trained models, and all supporting data on GitHub (<a href="https://github.com/Renjingyi123/AIP-TranLAC">https://github.com/Renjingyi123/AIP-TranLAC</a>).</p> Graphical Abstract <p></p>

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AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides

  • 期刊论文

  • 发表日期：

    2025年8月19日

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  • Shengli Zhang,
  • Jingyi Ren

摘要

Anti-inflammatory peptides (AIPs) have emerged as potential therapeutic candidates for managing various inflammatory disorders, but their computational identification remains challenging. We propose AIP-TranLAC, a novel deep learning framework that integrates Transformer-based embedding, bidirectional long short-term memory (Bi-LSTM), multi-head attention, and convolutional neural network (CNN) to classify AIPs accurately. Our model achieves superior performance on benchmark and independent test datasets, demonstrating significant improvements over existing methods. The hybrid architecture effectively captures local and global sequence patterns, while interpretability analyses reveal critical amino acid residues. With robust performance on imbalanced data and open-source availability, AIP-TranLAC provides a powerful tool for accelerating therapeutic peptide discovery and inflammation research. For reproducibility purposes, we have released the codebase, trained models, and all supporting data on GitHub (https://github.com/Renjingyi123/AIP-TranLAC).

Graphical Abstract