{"doi":"10.1371/journal.pcbi.1007411","title":"Detecting distant-homology protein structures by aligning deep neural-network based contact maps","abstract":null,"journal":"PLOS Computational Biology","year":2019,"id":589234,"datarank":2.2462583482167644,"base_score":4.060443010546419,"endowment":4.060443010546419,"self_citation_contribution":0.6090664515819629,"citation_network_contribution":1.6371918966348014,"self_endowment_contribution":0.6090664515819629,"citer_contribution":1.6371918966348014,"corpus_percentile":null,"corpus_rank":null,"citation_count":57,"citer_count":47,"citers_with_citation_signal":37,"citers_with_endowment":37,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":512433,"name":"Qiqige Wuyun","orcid":"0000-0002-7228-903X","position":1,"is_corresponding":false},{"id":274982,"name":"Yang Li","orcid":"0000-0001-9612-2704","position":2,"is_corresponding":false},{"id":639039,"name":"S. M. Mortuza","orcid":"0000-0003-3432-1497","position":3,"is_corresponding":false},{"id":121996,"name":"Chengxin Zhang","orcid":"0000-0001-7290-1324","position":4,"is_corresponding":false},{"id":114797,"name":"Robin Pearce","orcid":"0000-0001-6402-734X","position":5,"is_corresponding":false},{"id":1507554,"name":"Jishou Ruan","orcid":null,"position":6,"is_corresponding":false},{"id":287631,"name":"Yang Zhang","orcid":"0000-0002-2739-1916","position":7,"is_corresponding":false},{"id":529892,"name":"Wei Zheng","orcid":"0000-0002-2984-9003","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Detecting distant-homology protein structures by aligning deep neural-network based contact maps","abstract":"Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.","is_dataset_classified":null,"base_score":4.060443010546419,"endowment":4.060443010546419,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31622328","pmcid":"PMC6818797","openalex_id":"https://openalex.org/W2980805969","authors":[],"funders":[{"funder_name":"National Institute of General Medical Sciences","grant_id":"GM083107","title":null},{"funder_name":"National Institute of General Medical Sciences","grant_id":"GM116960","title":null},{"funder_name":"National Institute of Allergy and Infectious Diseases","grant_id":"AI134678","title":null},{"funder_name":"National Science Foundation","grant_id":"DBI1564756","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM116960","title":null},{"funder_name":"NIAID NIH HHS","grant_id":"R01 AI134678","title":null},{"funder_name":"NCI NIH HHS","grant_id":"T32 CA140044","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM083107","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01GM116960-04","title":"Template-based docking refinement approach to protein-protein structure modeling"},{"funder_name":"National Institutes of Health","grant_id":"5R01AI134678-03","title":"Structure-based functional annotation of microbial genomes"},{"funder_name":"National Science Foundation","grant_id":"1901191","title":"III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction"},{"funder_name":"National Institutes of Health","grant_id":"1R01GM083107-01A1","title":"I-TASSER based atomic-level protein structure prediction"}],"total_grants":12,"fwci":3.3972,"citation_percentile":0.93579234,"influential_citations":0,"citation_trend":[{"year":2020,"count":12},{"year":2021,"count":19},{"year":2022,"count":10},{"year":2023,"count":6},{"year":2024,"count":5},{"year":2025,"count":4},{"year":2026,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007411&type=printable","host_type":"journal"},{"url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007411&type=printable","host_type":"publisher"},{"url":"http://dx.plos.org/10.1371/journal.pcbi.1007411","host_type":"publisher"},{"url":"https://doi.org/10.1371/journal.pcbi.1007411","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31622328","host_type":"repository"},{"url":"https://doaj.org/article/211d83aec3014eb39919f47c99cdee3b","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6818797","host_type":"repository"},{"url":"https://figshare.com/articles/dataset/Detecting_distant-homology_protein_structures_by_aligning_deep_neural-network_based_contact_maps/9999278","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC6818797","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC6818797?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1371/journal.pcbi.1007411","host_type":""},{"url":"https://dx.doi.org/10.1371/journal.pcbi.1007411","host_type":""}],"fields_of_study":["Protein Structure and Dynamics","Computational Drug Discovery Methods","Enzyme Structure and Function","0301 basic medicine","0206 medical engineering","02 engineering and technology","03 medical and health sciences","Algorithms","Amino Acid Sequence","Computational Biology","Databases, Protein","Models, Biological","Nerve Net","Protein Conformation","Proteins","Sequence Alignment","Sequence Analysis, Protein","Software","Structural Homology, Protein"],"mesh_terms":["Algorithms","Amino Acid Sequence","Models, Biological","Nerve Net","Protein Conformation","Proteins","Software","Sequence Alignment","Computational Biology","Sequence Analysis, Protein","Databases, Protein","Structural Homology, Protein"],"keywords":["Template","Threading (protein sequence)","Protein superfamily","Protein structure prediction","Computer science","Convolutional neural network","Homology (biology)","Homology modeling","Protein Data Bank (RCSB PDB)","Structural alignment","Protein structure","Copying","Artificial neural network","Computational biology","CASP","Biological system","Sequence alignment","Artificial intelligence","Biology","Peptide sequence","Genetics","Amino acid","QH301-705.5","Protein Conformation","Proteins","Models, Biological","Sequence Analysis, Protein","Structural Homology, Protein","Amino Acid Sequence","Biology (General)","Nerve Net","Databases, Protein","Algorithms","Software","Research Article"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-23T14:57:11.602383Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}