{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xia C"],"funding":["Science and Technology Commission of Shanghai Municipality","National Natural Science Foundation of China"],"pagination":["e1009986"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8982879"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(3)"],"pubmed_abstract":["Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein structures increases rapidly. In this paper, we propose an effective graph-based protein structure representation learning method, GraSR, for fast and accurate structure comparison. In GraSR, a graph is constructed based on the intra-residue distance derived from the tertiary structure. Then, deep graph neural networks (GNNs) with a short-cut connection learn graph representations of the tertiary structures under a contrastive learning framework. To further improve GraSR, a novel dynamic training data partition strategy and length-scaling cosine distance are introduced. We objectively evaluate our method GraSR on SCOPe v2.07 and a new released independent test set from PDB database with a designed comprehensive performance metric. Compared with other state-of-the-art methods, GraSR achieves about 7%-10% improvement on two benchmark datasets. GraSR is also much faster than alignment-based methods. We dig into the model and observe that the superiority of GraSR is mainly brought by the learned discriminative residue-level and global descriptors. The web-server and source code of GraSR are freely available at www.csbio.sjtu.edu.cn/bioinf/GraSR/ for academic use."],"journal":["PLoS computational biology"],"pubmed_title":["Fast protein structure comparison through effective representation learning with contrastive graph neural networks."],"pmcid":["PMC8982879"],"funding_grant_id":["20S11902100","61725302","62073219"],"pubmed_authors":["Xia Y","Pan X","Shen HB","Feng SH","Xia C"],"additional_accession":[]},"is_claimable":false,"name":"Fast protein structure comparison through effective representation learning with contrastive graph neural networks.","description":"Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein structures increases rapidly. In this paper, we propose an effective graph-based protein structure representation learning method, GraSR, for fast and accurate structure comparison. In GraSR, a graph is constructed based on the intra-residue distance derived from the tertiary structure. Then, deep graph neural networks (GNNs) with a short-cut connection learn graph representations of the tertiary structures under a contrastive learning framework. To further improve GraSR, a novel dynamic training data partition strategy and length-scaling cosine distance are introduced. We objectively evaluate our method GraSR on SCOPe v2.07 and a new released independent test set from PDB database with a designed comprehensive performance metric. Compared with other state-of-the-art methods, GraSR achieves about 7%-10% improvement on two benchmark datasets. GraSR is also much faster than alignment-based methods. We dig into the model and observe that the superiority of GraSR is mainly brought by the learned discriminative residue-level and global descriptors. The web-server and source code of GraSR are freely available at www.csbio.sjtu.edu.cn/bioinf/GraSR/ for academic use.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-05T00:27:57.329Z","creation":"2025-04-05T00:27:57.329Z"},"accession":"S-EPMC8982879","cross_references":{"pubmed":["35324898"],"doi":["10.1371/journal.pcbi.1009986"]}}