XAI-Driven Deep Learning for Protein Sequence Functional Group Classification

• URL: http://arxiv.org/abs/2511.13791v1
• Date: Sun, 16 Nov 2025 18:10:42 GMT
• Title: XAI-Driven Deep Learning for Protein Sequence Functional Group Classification
• Authors: Pratik Chakraborty, Aryan Bhargava,
• Abstract summary: This study presents a deep learning framework for functional group classification of protein sequences derived from the Protein Data Bank (PDB)<n>Four architectures were implemented: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), CNN-BiLSTM hybrid, and CNN with Attention.<n>The CNN achieved the highest validation accuracy of 91.8%, demonstrating the effectiveness of localized motif detection.
• Score: 0.7734726150561088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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