Multi-Functional Peptide Discover with Amino Acid-level Fusion of Sequence Information and Structure Feature

Multi-Functional Peptide Discover with Amino Acid-level Fusion of Sequence Information and Structure Feature | 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 Multi-Functional Peptide Discover with Amino Acid-level Fusion of Sequence Information and Structure Feature Zhiqiang Liang, Qiyu Wang, Xuhui Liao, Liwei Xiao, Junjie Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9105469/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Multifunctional peptide identification is a challenging task due to the complex sequence-structure-function relationships. Existing methods often rely on single-modal features, limiting their ability to comprehensively model the intricate interplay between sequence and structural information. In this study, we propose MFPep-AAF, a novel amino acid (AA)-level multimodal fusion framework for multi-functional peptide identification by integrating sequential information and structural feature. MFPep-AAF harnesses a cross-modal attention mechanism to dynamically fuse AA-level semantics from a fine-tuned protein language model and AA-level structural constraints from a graph attention network. This fine-grained fusion strategy enables the model to effectively capture both local residue interactions and global sequence-structure relationships for functional prediction. Experimental results on benchmark datasets demonstrate that MFPep-AAF achieves state-of-the-art performance in terms of absolute true metric. These results underscore the advantages of integrating multimodal features, providing a robust and reliable framework for multifunctional peptide prediction. multifunctional peptides protein language model graph attention network multimodal feature fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Editor invited by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 31 Mar, 2026 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. 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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-9105469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617355535,"identity":"1ae8042c-84a6-4a40-b945-e564148435e9","order_by":0,"name":"Zhiqiang Liang","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Liang","suffix":""},{"id":617355536,"identity":"a4c96028-5b89-4952-ba27-a697c46405ba","order_by":1,"name":"Qiyu Wang","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiyu","middleName":"","lastName":"Wang","suffix":""},{"id":617355538,"identity":"80758640-af45-4c08-af8e-28432a11f56c","order_by":2,"name":"Xuhui Liao","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuhui","middleName":"","lastName":"Liao","suffix":""},{"id":617355540,"identity":"39c73b6e-0c77-4f6b-bc2b-45382f632656","order_by":3,"name":"Liwei Xiao","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Liwei","middleName":"","lastName":"Xiao","suffix":""},{"id":617355543,"identity":"14504160-cdc1-4aac-a6ea-1c9b0cce3e6f","order_by":4,"name":"Junjie Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACxgYGBoMEBgYgYj4GIhkYDhCvhS2NISGBCC0wAFTLYwamCGphnpF7oOBBhV0ev3TPtwcPfzDI8d1IYPxcgM9hM/ISDBLOJBdLzjm73QDoMGPJGwnM0jPwaskxMEhsO5C44UbuNgmgFiAjgY2Zh6CWfwcS99/IeQbSUk+klgagLRI5bCAtCQYEtfS8MTBIOJacOONGmplEQpqE4cwzD5ul8WkxbM8xM/xRY5fYPyP5meQPGxt5vuPJBz/j1dLAwGaAxJdggEQvHiAPjJoHeFWMglEwCkbBKAAAXrVOu6AUvVcAAAAASUVORK5CYII=","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-12 13:39:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9105469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9105469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403591,"identity":"38999ed7-df01-4760-8146-de5ba2a7ea4f","added_by":"auto","created_at":"2026-04-08 09:14:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2386829,"visible":true,"origin":"","legend":"","description":"","filename":"MFPepAAF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9105469/v1_covered_4bcf69d5-fb47-4e1c-b05a-c9398fca7c6a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Functional Peptide Discover with Amino Acid-level Fusion of Sequence Information and Structure Feature","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"multifunctional peptides, protein language model, graph attention network, multimodal feature fusion","lastPublishedDoi":"10.21203/rs.3.rs-9105469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9105469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Multifunctional peptide identification is a challenging task due to the complex sequence-structure-function relationships. 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