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

Wenrui Gou1, Wenhui Ge1, Yang Tan1

  • 1School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.

Interdisciplinary Sciences, Computational Life Sciences
|June 8, 2025
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Summary
This summary is machine-generated.

We developed a deep learning model, CPE-Pro, to accurately determine the origin of protein structures. This approach enhances protein representation by focusing on structural features, improving reliability assessments for experimental and predicted data.

Keywords:
Deep learningOrigin evaluationProtein representationStructural sequence

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Protein structure determination is vital for understanding biological functions.
  • Expanding protein structure databases necessitate reliable methods for origin assessment.
  • Current protein representation methods struggle with subtle structural variations, impacting traceability.

Purpose of the Study:

  • To develop a novel deep learning model for protein structure origin evaluation.
  • To enhance protein representation by capturing structure-specific features.
  • To improve the assessment of experimental and predicted protein structure reliability.

Main Methods:

  • Proposed Crystal vs Predicted Evaluator for Protein Structure (CPE-Pro), a supervised deep learning model.
  • Integrated a pre-trained protein Structural Sequence Language Model (SSLM) with a Geometric Vector Perceptron-Graph Neural Network (GVP-GNN).
  • Utilized structure-aware representations focusing on local structural features for classification.

Main Results:

  • CPE-Pro accurately classifies protein structures into four origins.
  • Structure-aware representations derived from structural sequences outperform traditional sequence-based models.
  • The model effectively captures subtle structural differences crucial for origin determination.

Conclusions:

  • CPE-Pro offers a refined approach to protein structure representation and origin evaluation.
  • Enriching representations with local structural features enhances model performance.
  • Future work includes extending the model to diverse protein structure paradigms and low-confidence predictions.