2024/10/21 by Weifeng Ge, Gou, Wenrui, Yang Tan +8
Biochemistry, Genetics and Molecular Biology · #Biomolecules (q-bio.BM) #Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genetics, Bioinformatics, and Biomedical Research #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2410.15592
openalex publication_date 2024/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.