Protein language model embeddings enable proteome-wide discovery of plant defense gene networks across species

Protein language model embeddings enable proteome-wide discovery of plant defense gene networks across species | 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 Protein language model embeddings enable proteome-wide discovery of plant defense gene networks across species Sara Behnamian, Naghmeh Boyouk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9187511/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Identifying the full complement of defense genes across plant proteomes remains challenging, particularly for species with incomplete functional annotations. Here we present PlantDefenseESM, a computational pipeline that leverages protein language model embeddings to discover defense gene networks at the proteome scale without requiring species-specific training or curated gene ontology databases. We generated 1,280-dimensional embeddings for all proteins in the proteomes of Arabidopsis thaliana (48,207 proteins), Oryza sativa (42,575), and Vitis vinifera (40,632) using ESM-2, a transformer-based model pre-trained on 250 million protein sequences. Defense candidates were identified by cosine similarity to category centroids defined by 33 experimentally validated anchor proteins spanning six functional classes: NBS-LRR resistance proteins, pathogenesis-related proteins, receptor-like kinases, defense signaling components, antimicrobial enzymes, and hypersensitive response regulators. A multi-tier selection strategy combining percentile-based and rank-based approaches identified 2,807, 2,442, and 2,354 moderate-tier candidates in A. thaliana, O. sativa, and V. vinifera, respectively. Independent validation against RefSeq functional annotations confirmed 3.35–4.22-fold enrichment of defense-annotated proteins among candidates (Fisher's exact test, p < 10⁻¹⁹⁹ in all species). Notably, 55–59% of candidates across all three species lacked any existing defense annotation, representing putative novel defense genes. Cross-species comparison revealed a conserved category hierarchy with lineage-specific expansions consistent with known biology, including expanded cell death machinery in grapevine and receptor-like kinase families in rice. The pipeline is species-agnostic, requires only a reference proteome as input, and provides a scalable framework for defense gene discovery in any sequenced plant genome. protein language model ESM-2 plant innate immunity defense gene discovery proteome-wide classification NBS-LRR novel gene prediction Arabidopsis thaliana Oryza sativa Vitis vinifera Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 21 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. 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-9187511","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612496212,"identity":"198f9d58-7cea-48f6-bf9e-f6bd2c3f8e7a","order_by":0,"name":"Sara Behnamian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie2PMUvDQBSA36NQpzpnCf0LBxlUCM1fyRE4FyMFwbkgnEula4Lgb3jFJeOFgIuxXQ/ikqVbwG46iF5W8YxuDvctD4738b4DcDj+K7iAuJ8KIOwnMw+A2S8V8UfFUA0r0+un9gWL8Pzo4JGrt2IbseaKMJfg54vvFVafBh7W4uJkmVJ5Uzecnh/muJYQ3FquMFPvoaw4qZTURDYx02cMWwn8zha22o1eUX5w2nZUvstNNKiAFmNzRXHSKVUTqZB6xYRxa5jejY95nRilo8qXCc+1mJfZxgts35+uxEjvi5kJS+/3nZxFhzpZt8vL0M+UrcwQf30wy94P+w6Hw+EY4hPhV2sf2ZGabwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Copenhagen","correspondingAuthor":true,"prefix":"","firstName":"Sara","middleName":"","lastName":"Behnamian","suffix":""},{"id":612496213,"identity":"828100bb-3cdf-419e-b96f-b2c5b204b68d","order_by":1,"name":"Naghmeh Boyouk","email":"","orcid":"","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Naghmeh","middleName":"","lastName":"Boyouk","suffix":""}],"badges":[],"createdAt":"2026-03-21 18:23:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9187511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9187511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904308,"identity":"44f2a463-ceba-4627-b92f-492b4adcf1ca","added_by":"auto","created_at":"2026-04-01 10:07:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":912333,"visible":true,"origin":"","legend":"","description":"","filename":"PlantDefenseESMMethods20260325.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9187511/v1_covered_8490c8d2-91eb-481f-9601-322a23930fff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protein language model embeddings enable proteome-wide discovery of plant defense gene networks across species","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"protein language model, ESM-2, plant innate immunity, defense gene discovery, proteome-wide classification, NBS-LRR, novel gene prediction, Arabidopsis thaliana, Oryza sativa, Vitis vinifera","lastPublishedDoi":"10.21203/rs.3.rs-9187511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9187511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Identifying the full complement of defense genes across plant proteomes remains challenging, particularly for species with incomplete functional annotations. 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A multi-tier selection strategy combining percentile-based and rank-based approaches identified 2,807, 2,442, and 2,354 moderate-tier candidates in A. thaliana, O. sativa, and V. vinifera, respectively. Independent validation against RefSeq functional annotations confirmed 3.35–4.22-fold enrichment of defense-annotated proteins among candidates (Fisher's exact test, p \u003c 10⁻¹⁹⁹ in all species). Notably, 55–59% of candidates across all three species lacked any existing defense annotation, representing putative novel defense genes. Cross-species comparison revealed a conserved category hierarchy with lineage-specific expansions consistent with known biology, including expanded cell death machinery in grapevine and receptor-like kinase families in rice. 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