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

TL;DR

CPE-Pro is a deep learning model that accurately determines the origin of protein structures, distinguishing between experimental and predicted structures, and enhances structural representation through a novel structure-sequence encoding.

Contribution

The paper introduces CPE-Pro, a structure-sensitive deep learning approach for protein origin discrimination, and a new structure-sequence based language model that improves structural feature learning.

Findings

CPE-Pro achieves accurate discrimination of protein structure origins.

Structure-sequence encoding enhances protein feature learning.

Preliminary results show improved structural representations over amino acid-based models.

Abstract

Protein structures are important for understanding their functions and interactions. Currently, many protein structure prediction methods are enriching the structure database. Discriminating the origin of structures is crucial for distinguishing between experimentally resolved and computationally predicted structures, evaluating the reliability of prediction methods, and guiding downstream biological studies. Building on works in structure prediction, We developed a structure-sensitive supervised deep learning model, Crystal vs Predicted Evaluator for Protein Structure (CPE-Pro), to represent and discriminate the origin of protein structures. CPE-Pro learns the structural information of proteins and captures inter-structural differences to achieve accurate traceability on four data classes, and is expected to be extended to more. Simultaneously, we utilized Foldseek to encode protein structures into "structure-sequences" and trained a protein Structural Sequence Language Model, SSLM. Preliminary experiments demonstrated that, compared to large-scale protein language models pre-trained on vast amounts of amino acid sequences, the "structure-sequence" enables the language model to learn more informative protein features, enhancing and optimizing structural representations. We have provided the code, model weights, and all related materials on https://github.com/GouWenrui/CPE-Pro-main.git.