Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

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dc.contributor.authorLaw, Jeffrey N.en
dc.contributor.authorAkers, Kyleen
dc.contributor.authorTasnina, Nureen
dc.contributor.authorDella Santina, Catherine M.en
dc.contributor.authorDeutsch, Shayen
dc.contributor.authorKshirsagar, Meghanaen
dc.contributor.authorKlein-Seetharaman, Judithen
dc.contributor.authorCrovella, Marken
dc.contributor.authorRajagopalan, Padmavathyen
dc.contributor.authorKasif, Simonen
dc.contributor.authorMurali, T. M.en
dc.date.accessioned2022-02-18T21:30:04Zen
dc.date.available2022-02-18T21:30:04Zen
dc.date.issued2021-12-01en
dc.date.updated2022-02-18T21:30:00Zen
dc.description.abstractBACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.en
dc.description.versionPublished versionen
dc.format.extent12 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN giab082 (Article number)en
dc.identifier.doihttps://doi.org/10.1093/gigascience/giab082en
dc.identifier.eissn2047-217Xen
dc.identifier.issn2047-217Xen
dc.identifier.issue12en
dc.identifier.orcidMurali, T [0000-0003-3688-4672]en
dc.identifier.other6489124 (PII)en
dc.identifier.pmid34966926en
dc.identifier.urihttp://hdl.handle.net/10919/108754en
dc.identifier.volume10en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000743901400006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectLife Sciences & Biomedicineen
dc.subjectBiologyen
dc.subjectnetwork propagationen
dc.subjectinterpretable machine learningen
dc.subjectprovenance tracingen
dc.subjectSARS-CoV-2en
dc.subjectCOVID-19en
dc.subjectvirus-host protein interaction networksen
dc.subjectUNFOLDED PROTEINen
dc.subjectCELL-SURFACEen
dc.subjectPREDICTIONen
dc.subjectGRP78en
dc.subjectCOVID-19en
dc.subjectSARS-CoV-2en
dc.subject.meshHumansen
dc.subject.meshProteinsen
dc.subject.meshAlgorithmsen
dc.subject.meshProtein Interaction Mapsen
dc.subject.meshCOVID-19en
dc.subject.meshSARS-CoV-2en
dc.titleInterpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2en
dc.title.serialGigaScienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2021-11-28en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Chemical Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten

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