Protein Function Prediction Using GO Similarity-based Heterogeneous Network Propagation

Protein Function Prediction Using GO Similarity-based Heterogeneous Network Propagation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Protein Function Prediction Using GO Similarity-based Heterogeneous Network Propagation Sai Hu, Bihai Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5882035/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 May, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Protein function prediction serves as a fundamental cornerstone in bioinformatics, offering critical insights into the intricate biological processes and molecular mechanisms that form the basis of life. Precise annotation of protein functions is indispensable for unraveling disease mechanisms, identifying drug targets, and propelling forward synthetic biology applications. Nevertheless, this task remains complex due to the diverse characteristics of multi-omics data and the hierarchical structure of Gene Ontology (GO) annotations. Results: To tackle these challenges, we have developed an innovative approach that seamlessly integrates the topological structure of protein-protein interaction networks, a wide array of biological data including protein domain profile and protein complex information, and Gene Ontology into a heterogeneous network based on GO similarity. This integrated network encapsulates the multifaceted relationships between proteins and their functional annotations, setting the stage for a comprehensive protein function prediction framework. Building on this heterogeneous network, we have devised a protein function prediction method named GOHPro, which leverages the strength of network propagation algorithms. To evaluate the effectiveness of GOHPro, we conducted rigorous experiments on two model organisms, yeast and human, using the most up-to-date GO annotations dataset and the CAFA3 dataset. Our method was compared against several state-of-the-art approaches, and the results unequivocally showed that GOHPro outperforms its competitors, highlighting its superiority in predicting protein functions. The code and dataset of GOHPro are freely available at https://github.com/husaiccsu/GOHPro . Conclusions: The proposed GOHPro method, which seamlessly combines multi-omics data, protein interaction networks, and GO annotations within a heterogeneous network framework, significantly enhances protein function prediction accuracy. The outstanding performance of GOHPro in our experiments underscores its potential as a powerful tool for annotating protein functions, facilitating a more profound understanding of biological processes and contributing to advancements in bioinformatics and computational biology. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Physical sciences/Mathematics and computing Protein function prediction Gene Ontology (GO) heterogeneous network network propagation multi-omics data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Mar, 2025 Reviews received at journal 28 Feb, 2025 Reviews received at journal 20 Feb, 2025 Reviewers agreed at journal 14 Feb, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviewers invited by journal 24 Jan, 2025 Editor assigned by journal 24 Jan, 2025 Editor invited by journal 24 Jan, 2025 Submission checks completed at journal 23 Jan, 2025 First submitted to journal 22 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5882035","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":407492860,"identity":"6929aef4-6441-45fa-830b-0bb8ae427e29","order_by":0,"name":"Sai Hu","email":"","orcid":"","institution":"Changsha University","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"","lastName":"Hu","suffix":""},{"id":407492861,"identity":"5b095b05-df00-4822-8ac2-31305001f2a3","order_by":1,"name":"Bihai Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACA2YeBoYEhgMMDOyNjQ8/kKaF53CzsQRRWhh4QBRQi0R6mwAPMVrM2XkPfnjw5448v+TDNgYJBjs53QYCWiyb+ZIlEnieGc6cndj2oIAh2djsACGHHeYxkEiQOMy44XZiu4EEw4HEbURoMf6RYHDYfsPNg20SPERqMZNISDicuOEGI5FaLJt5zCwSDhxOntmTCAxkAyL8Ys5/xvjmjz+HbfvZjz98+KHCTo6gFnR3kqZ8FIyCUTAKRgEOAACAh0RTA6sWYwAAAABJRU5ErkJggg==","orcid":"","institution":"Changsha University","correspondingAuthor":true,"prefix":"","firstName":"Bihai","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-01-22 15:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5882035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5882035/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04933-1","type":"published","date":"2025-05-31T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83782976,"identity":"59fdeb99-5b34-46ba-8793-ad60f6025681","added_by":"auto","created_at":"2025-06-02 16:09:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":835157,"visible":true,"origin":"","legend":"","description":"","filename":"GOHProSH.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5882035/v1_covered_19800e24-79b5-4005-a4fe-c4af253b87cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protein Function Prediction Using GO Similarity-based Heterogeneous Network Propagation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Protein function prediction, Gene Ontology (GO), heterogeneous network, network propagation, multi-omics data","lastPublishedDoi":"10.21203/rs.3.rs-5882035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5882035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eProtein function prediction serves as a fundamental cornerstone in bioinformatics, offering critical insights into the intricate biological processes and molecular mechanisms that form the basis of life. Precise annotation of protein functions is indispensable for unraveling disease mechanisms, identifying drug targets, and propelling forward synthetic biology applications. Nevertheless, this task remains complex due to the diverse characteristics of multi-omics data and the hierarchical structure of Gene Ontology (GO) annotations.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eTo tackle these challenges, we have developed an innovative approach that seamlessly integrates the topological structure of protein-protein interaction networks, a wide array of biological data including protein domain profile and protein complex information, and Gene Ontology into a heterogeneous network based on GO similarity. This integrated network encapsulates the multifaceted relationships between proteins and their functional annotations, setting the stage for a comprehensive protein function prediction framework. Building on this heterogeneous network, we have devised a protein function prediction method named GOHPro, which leverages the strength of network propagation algorithms. To evaluate the effectiveness of GOHPro, we conducted rigorous experiments on two model organisms, yeast and human, using the most up-to-date GO annotations dataset and the CAFA3 dataset. Our method was compared against several state-of-the-art approaches, and the results unequivocally showed that GOHPro outperforms its competitors, highlighting its superiority in predicting protein functions. The code and dataset of GOHPro are freely available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/husaiccsu/GOHPro\u003c/span\u003e\u003cspan address=\"https://github.com/husaiccsu/GOHPro\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThe proposed GOHPro method, which seamlessly combines multi-omics data, protein interaction networks, and GO annotations within a heterogeneous network framework, significantly enhances protein function prediction accuracy. The outstanding performance of GOHPro in our experiments underscores its potential as a powerful tool for annotating protein functions, facilitating a more profound understanding of biological processes and contributing to advancements in bioinformatics and computational biology.\u003c/p\u003e","manuscriptTitle":"Protein Function Prediction Using GO Similarity-based Heterogeneous Network Propagation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 18:15:17","doi":"10.21203/rs.3.rs-5882035/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-01T16:29:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-28T09:26:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-20T21:30:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267453934498092479614875171327793218348","date":"2025-02-14T09:07:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277492410600842919826865981348394374721","date":"2025-01-29T20:42:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323396697758834638897518241700143130245","date":"2025-01-29T20:19:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-24T20:02:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-24T17:49:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-24T16:01:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-23T12:35:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-22T15:08:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"252e30d3-b0a4-4edb-bb54-03ad615646c7","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43446450,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":43446451,"name":"Biological sciences/Genetics"},{"id":43446452,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-06-02T16:03:33+00:00","versionOfRecord":{"articleIdentity":"rs-5882035","link":"https://doi.org/10.1038/s41598-025-04933-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-31 15:57:54","publishedOnDateReadable":"May 31st, 2025"},"versionCreatedAt":"2025-01-28 18:15:17","video":"","vorDoi":"10.1038/s41598-025-04933-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-04933-1","workflowStages":[]},"version":"v1","identity":"rs-5882035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5882035","identity":"rs-5882035","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}