用深度学习+可解释AI识别蛋白质功能基团，准确率91.8%
XAI-Driven Deep Learning for Protein Sequence Functional Group Classification
- 用k-mer编码+CNN捕捉序列局部与长程依赖
- 最高验证准确率达91.8%，识别出关键催化残基
- 结合Grad-CAM等技术揭示生物有意义的序列特征
蛋白质执行关键生物功能，其序列的准确分类对理解结构-功能关系、酶机制和分子互作至关重要。本研究提出一种基于深度学习的框架，用于从蛋白质数据银行（PDB）中提取的蛋白质序列进行功能基团分类。实现了四种架构：卷积神经网络（CNN）、双向长短期记忆网络（BiLSTM）、CNN-BiLSTM混合模型及带注意力机制的CNN。各模型均采用k-mer整数编码以捕捉局部与长程依赖。其中，CNN在验证集上达到最高准确率91.8%，表明局部基序检测的有效性。应用梯度类激活图（Grad-CAM）和集成梯度法等可解释人工智能技术，解释模型预测并识别出具有生物学意义的序列基序。这些发现的基序富含组氨酸、天冬氨酸、谷氨酸和赖氨酸，常见于转移酶的催化与金属结合区域。结果表明，深度学习模型可挖掘出功能相关的生化特征，弥合了蛋白质序列分析中预测准确性与生物学可解释性之间的鸿沟。
原文摘要 · Abstract (English)
Proteins perform essential biological functions, and accurate classification of their sequences is critical for understanding structure-function relationships, enzyme mechanisms, and molecular interactions. This study presents a deep learning-based framework for functional group classification of protein sequences derived from the Protein Data Bank (PDB). Four architectures were implemented: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), CNN-BiLSTM hybrid, and CNN with Attention. Each model was trained using k-mer integer encoding to capture both local and long-range dependencies. Among these, the CNN achieved the highest validation accuracy of 91.8%, demonstrating the effectiveness of localized motif detection. Explainable AI techniques, including Grad-CAM and Integrated Gradients, were applied to interpret model predictions and identify biologically meaningful sequence motifs. The discovered motifs, enriched in histidine, aspartate, glutamate, and lysine, represent amino acid residues commonly found in catalytic and metal-binding regions of transferase enzymes. These findings highlight that deep learning models can uncover functionally relevant biochemical signatures, bridging the gap between predictive accuracy and biological interpretability in protein sequence analysis.
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