Enhancing Protein-Protein Interaction Site Prediction through Hybrid Transformer-OGF-AGNN Architecture: A Study on Yeast Proteome

Enhancing Protein-Protein Interaction Site Prediction through Hybrid Transformer-OGF-AGNN Architecture: A Study on Yeast Proteome | 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 Research Article Enhancing Protein-Protein Interaction Site Prediction through Hybrid Transformer-OGF-AGNN Architecture: A Study on Yeast Proteome Merry K. P, Angelina Geetha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7204917/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract Predicting correct proteins interact (PPI) is key for understanding how cells function and for creating new drugs. Standard techniques are usually not able to model the many links between the sequence, structure, and network traits present in protein interactions. A Hybrid Transformer-OGF-AGNN architecture that merges three distinct approaches: (1) the Transformer network to detect long range patterns, (2) Object-Graph Fusion (OGF) to represent both structural and functional info, and (3) Attention-Guided Graph Neural Networks (AGNN) to make use of the interaction between proteins. The model was built and assessed using data from the Saccharomyces cerevisiae proteome, which includes 6,049 proteins, each with verified interaction sites. The test data demonstrated superior performance of the proposed architecture, achieving an F 1 -score of 0.847 (precision: 0.823, recall: 0.872), compared to baseline methods: DeepPPISP (F 1 : 0.782), SPRINGS (F 1 : 0.756), and PPI-Pred (F 1 : 0.729). Cross-validation results indicated broad applicability with minimal variance (σ 2 = 0.003). Notably, 93.2% of predicted interaction sites (1,247 out of 1,323) were experimentally validated. The Hybrid Transformer-OGF-AGNN architecture makes PPI site prediction better by combining different biological data. The better coverage in yeast proteome suggests its capabilities may be applied more widely in fields like structural biology and finding targets for drugs. Graph Neural Networks (GNN) Optimized Gated Fusion (OGF) Adaptive Graph Neural Networks (AGNN) Transformer Architecture Self-attention Mechanism Yeast Proteome Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviews received at journal 23 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor invited by journal 07 Oct, 2025 Editor assigned by journal 13 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 12 Aug, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7204917","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534611680,"identity":"4e2e27cb-99bc-49f4-b962-ea8833883c54","order_by":0,"name":"Merry K. 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Standard techniques are usually not able to model the many links between the sequence, structure, and network traits present in protein interactions. A Hybrid Transformer-OGF-AGNN architecture that merges three distinct approaches: (1) the Transformer network to detect long range patterns, (2) Object-Graph Fusion (OGF) to represent both structural and functional info, and (3) Attention-Guided Graph Neural Networks (AGNN) to make use of the interaction between proteins. The model was built and assessed using data from the Saccharomyces cerevisiae proteome, which includes 6,049 proteins, each with verified interaction sites. The test data demonstrated superior performance of the proposed architecture, achieving an F\u003csub\u003e1\u003c/sub\u003e-score of 0.847 (precision: 0.823, recall: 0.872), compared to baseline methods: DeepPPISP (F\u003csub\u003e1\u003c/sub\u003e: 0.782), SPRINGS (F\u003csub\u003e1\u003c/sub\u003e: 0.756), and PPI-Pred (F\u003csub\u003e1\u003c/sub\u003e: 0.729). Cross-validation results indicated broad applicability with minimal variance (σ\u003csup\u003e2\u003c/sup\u003e = 0.003). Notably, 93.2% of predicted interaction sites (1,247 out of 1,323) were experimentally validated. The Hybrid Transformer-OGF-AGNN architecture makes PPI site prediction better by combining different biological data. The better coverage in yeast proteome suggests its capabilities may be applied more widely in fields like structural biology and finding targets for drugs.\u003c/p\u003e","manuscriptTitle":"Enhancing Protein-Protein Interaction Site Prediction through Hybrid Transformer-OGF-AGNN Architecture: A Study on Yeast Proteome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 13:23:46","doi":"10.21203/rs.3.rs-7204917/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-27T10:48:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T07:23:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T16:27:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273617954794487989989547689687399379807","date":"2025-10-21T00:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257225341685008915470870919320946592493","date":"2025-10-17T01:18:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T09:25:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T07:16:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319361393159906125899653080515955602742","date":"2025-10-15T19:24:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11933712136315375296002211308127732357","date":"2025-10-15T09:54:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153184209979809809966986999940487702721","date":"2025-10-15T09:01:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235320949781315845810281247486454547103","date":"2025-10-14T22:07:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300973840835648595773995261156302981170","date":"2025-10-14T17:27:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329905672434054078308857393014533663482","date":"2025-10-14T17:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-14T17:21:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T13:44:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T08:56:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T11:56:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2025-08-12T10:50:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3e944c0-d19f-436d-849d-2f6f153e215e","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-09T14:38:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 13:23:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7204917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7204917","identity":"rs-7204917","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}