{"doi":"10.1101/275487","title":"Towards region-specific propagation of protein functions","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Motivation</jats:title>\n                  <jats:p>Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction by testing on both human and yeast proteomes. We compare region-level predictive performance of our method against that of a whole-protein baseline method using a held-out dataset of proteins with structurally-verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into site-specific and whole-protein terms and select prediction methods for different classes of GO terms.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Availability</jats:title>\n                  <jats:p>\n                    The code is freely available at:\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/ek1203/region_spec_func_pred\">https://github.com/ek1203/region_spec_func_pred</jats:ext-link>\n                  </jats:p>\n                </jats:sec>","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":null,"id":17027,"datarank":0.20896253238260828,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.04417068908239183,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.04417068908239183,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"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":476,"name":"Richard Bonneau","orcid":"0000-0003-4354-7906","position":1,"is_corresponding":false},{"id":122329,"name":"Da Chen Emily Koo","orcid":"0000-0001-9379-4548","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"30304483","pmcid":null,"openalex_id":"https://openalex.org/W2790515430","authors":[],"funders":[{"funder_name":"National Science Foundation","grant_id":"0929338","title":"Arabidopsis 2010: Nitrogen Networks in Plants"},{"funder_name":"National Institutes of Health","grant_id":"2R01GM032877-25A1","title":"A systems approach to regulatory networks controlling N-assimilation"},{"funder_name":"National Science Foundation","grant_id":"1412232","title":"Prospecting for Resources: A Systems Integration of Local and Systemic Nutrient Signaling"},{"funder_name":"National Science Foundation","grant_id":"0922738","title":"Genomics of Comparative Seed Evolution"},{"funder_name":"National Science Foundation","grant_id":"1355462","title":"EAGER: Modeling Protein Degradation - Evaluation of Strategies and Targets"},{"funder_name":"National Science Foundation","grant_id":"1339362","title":"NutriNet: A Network Inspired Approach to Improving Nutrient Use Efficiency (NUE) in Crop Plants"},{"funder_name":"National Science Foundation","grant_id":"1158273","title":"A Systems Approach to the NPK Nutriome and its Effect on Biomass"}],"total_grants":7,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2020,"count":1}],"oa_status":"green","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2018/03/02/275487.full.pdf","host_type":"repository"},{"url":"https://academic.oup.com/bioinformatics/article-pdf/35/10/1737/28639970/bty834.pdf","host_type":"HYBRID"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2018/03/02/275487.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/275487","host_type":"publisher"},{"url":"https://doi.org/10.1101/275487","host_type":"repository"},{"url":"https://doi.org/10.1093/bioinformatics/bty834","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/30304483","host_type":""},{"url":"http://dx.doi.org/10.1093/bioinformatics/bty834","host_type":""},{"url":"https://dx.doi.org/10.1093/bioinformatics/bty834","host_type":""},{"url":"https://dx.doi.org/10.1101/275487","host_type":""},{"url":"http://dx.doi.org/10.1101/275487","host_type":""}],"fields_of_study":["Machine Learning in Bioinformatics","Protein Structure and Dynamics","Genomics and Phylogenetic Studies","Computer Science","Medicine","Biology","0301 basic medicine","03 medical and health sciences","0206 medical engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["UniProt","Protein function prediction","Function (biology)","Categorization","Computer science","Computational biology","Proteome","Protein domain","Domain (mathematical analysis)","Protein function","Source code","Annotation","Spurious relationship","Machine learning","Artificial intelligence","Data mining","Bioinformatics","Biology","Mathematics","Genetics","Binding Sites","Proteins","Molecular Sequence Annotation","Amino Acid Sequence","Original Papers"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-02T16:05:01.058070Z","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":[]}