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Abstract <p>Protein structures are fundamental to understanding their functions and interactions. With the continuous advancement of protein structure prediction methods, structure databases are rapidly expanding. Identifying the origin of protein structures is crucial for assessing the reliability of experimental resolution and computational prediction methods, as well as for guiding downstream biological research. Existing protein representation approaches often fail to capture subtle yet critical structural differences, posing challenges for precise structural traceability. To address this, we propose a structure-sensitive supervised deep learning model, <b>C</b>rystal vs <b>P</b>redicted <b>E</b>valuator for <b>Pro</b>tein Structure (<b>CPE-Pro</b>), for the representation and origin evaluation of protein structures. CPE-Pro integrates a pre-trained protein Structural Sequence Language Model (SSLM) and Geometric Vector Perceptron-Graph Neural Network (GVP-GNN) to learn structure-aware protein representations and capture structural differences, enabling accurate classification across four origins of structural data. Preliminary results indicate that, compared to large-scale protein language models trained on extensive amino acid sequences, structural sequences enriched with local structural features enable the model to capture more informative protein characteristics, thereby enhancing and refining protein representations. Future research directions include extending the architecture to additional protein structure paradigms and developing evaluation methodologies for low-pLDDT predicted structures, providing more effective tools for protein structure analysis. The code, model weights, and all relevant materials are available at <a href="https://github.com/wr1102/CPE-Pro">https://github.com/wr1102/CPE-Pro</a>.</p> Graphical Abstract <p></p>

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CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation

  • 期刊论文

  • 发表日期：

    2025年6月8日

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  • Wenrui Gou,
  • Wenhui Ge,
  • Yang Tan,
  • Mingchen Li,
  • Guisheng Fan,
  • Huiqun Yu

摘要

Abstract

Protein structures are fundamental to understanding their functions and interactions. With the continuous advancement of protein structure prediction methods, structure databases are rapidly expanding. Identifying the origin of protein structures is crucial for assessing the reliability of experimental resolution and computational prediction methods, as well as for guiding downstream biological research. Existing protein representation approaches often fail to capture subtle yet critical structural differences, posing challenges for precise structural traceability. To address this, we propose a structure-sensitive supervised deep learning model, Crystal vs Predicted Evaluator for Protein Structure (CPE-Pro), for the representation and origin evaluation of protein structures. CPE-Pro integrates a pre-trained protein Structural Sequence Language Model (SSLM) and Geometric Vector Perceptron-Graph Neural Network (GVP-GNN) to learn structure-aware protein representations and capture structural differences, enabling accurate classification across four origins of structural data. Preliminary results indicate that, compared to large-scale protein language models trained on extensive amino acid sequences, structural sequences enriched with local structural features enable the model to capture more informative protein characteristics, thereby enhancing and refining protein representations. Future research directions include extending the architecture to additional protein structure paradigms and developing evaluation methodologies for low-pLDDT predicted structures, providing more effective tools for protein structure analysis. The code, model weights, and all relevant materials are available at https://github.com/wr1102/CPE-Pro.

Graphical Abstract