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This study introduces the innovative Protein Distance Model (PDM) for predicting structural distances between protein pairs in the Structural Classification of Proteins (SCOP) database. SCOP’s hierarchical classification presents challenges for direct prediction, so PDM predicts distances between unknown proteins and those with known classifications to infer structural categories. Incorporating NLP’s Attention mechanism and a dual-tower network, PDM extracts high-level features from two protein sequences to predict their distance. Transitioning from regression to a multiclass task improved performance, achieving over 90% AUC and Accuracy in binary classification and over 80% F1-Score and Accuracy in multiclass scenarios. PDM shows strong generalization, laying a foundation for future research in protein structural classification. The code is publicly available on GitHub .

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Automated Prediction of Protein Pair Distances Based on Deep Learning: A Novel Approach to Protein Structure Prediction in SCOP

  • 会议论文

  • 发表日期：

    2026年

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  • Duo Feng,
  • Shuaicheng Li,
  • Yue Zhang

摘要

This study introduces the innovative Protein Distance Model (PDM) for predicting structural distances between protein pairs in the Structural Classification of Proteins (SCOP) database. SCOP’s hierarchical classification presents challenges for direct prediction, so PDM predicts distances between unknown proteins and those with known classifications to infer structural categories. Incorporating NLP’s Attention mechanism and a dual-tower network, PDM extracts high-level features from two protein sequences to predict their distance. Transitioning from regression to a multiclass task improved performance, achieving over 90% AUC and Accuracy in binary classification and over 80% F1-Score and Accuracy in multiclass scenarios. PDM shows strong generalization, laying a foundation for future research in protein structural classification. The code is publicly available on GitHub .