{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T17:37:46Z","timestamp":1791135466994,"version":"4.1.0"},"reference-count":29,"publisher":"Wiley","issue":"18","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["physoc.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Physiology"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec><jats:title>Abstract<\/jats:title><jats:p>Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic\u2010based network models of cardiac hypertrophy, mechano\u2010signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano\u2010signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well\u2010conserved pathways, they performed worse at recovering tissue\u2010specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca<jats:sup>2+<\/jats:sup>\/calmodulin\u2010dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling.\n<jats:boxed-text content-type=\"graphic\" position=\"anchor\"><jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/tjp15612-gra-0001-m.png\"><jats:alt-text>image<\/jats:alt-text><\/jats:graphic><\/jats:boxed-text><\/jats:p><\/jats:sec><jats:sec><jats:title>Key points<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Protein interaction databases are used to recover signalling interactions from previously developed network models.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>The five protein interaction databases benchmarked recovered well\u2010conserved pathways, but did poorly at recovering tissue\u2010specific pathways and transcriptional regulation, indicating the importance of manual curation.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>We identify new signalling interactions not previously used in the network models, including a role for Ca<jats:sup>2+<\/jats:sup>\/calmodulin\u2010dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1113\/jp284616","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T12:39:55Z","timestamp":1684413595000},"page":"4529-4542","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models"],"prefix":"10.1113","volume":"602","author":[{"given":"Matthew W.","family":"Van de Graaf","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering University of Virginia  Charlottesville VA USA"},{"name":"Children's National Hospital  Washington DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taylor G.","family":"Eggertsen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Virginia  Charlottesville VA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela C.","family":"Zeigler","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Virginia  Charlottesville VA USA"},{"name":"Yale New Haven Hospital  New Haven CT USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip M.","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Virginia  Charlottesville VA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9464-8374","authenticated-orcid":false,"given":"Jeffrey J.","family":"Saucerman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Virginia  Charlottesville VA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2008-9-s2-s10"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkq1039"},{"key":"e_1_2_5_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr625"},{"key":"e_1_2_5_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkt1102"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2021.100492"},{"key":"e_1_2_5_7_1","doi-asserted-by":"publisher","DOI":"10.15252\/msb.202211036"},{"key":"e_1_2_5_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1478-3975\/aaffa1"},{"key":"e_1_2_5_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt099"},{"issue":"1","key":"e_1_2_5_10_1","first-page":"D148","article-title":"miRTarBase 2020: updates to the experimentally validated microRNA\u2010target interaction 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