A hybrid deep learning framework for predicting the protein-protein interaction between virus and host

A hybrid deep learning framework for predicting the protein-protein interaction between virus and host | 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 A hybrid deep learning framework for predicting the protein-protein interaction between virus and host Lei Deng, Wenjuan Nie, Jiaojiao Zhao, Jingpu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-506156/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Viral infection and diseases are caused by various viruses involved in the protein-protein interaction (PPI) between virus and host, which are a threat to human health. Studying the virus-host PPI is beneficial to apprehending the mechanism of viral infection and developing new treatment drugs. Although several computational methods for predicting the virus-host PPI have been proposed, most of them are supported by the machine learning algorithms, making the hidden high-level feature difficult to be extracted. Results: We proposed a novel hybrid deep learning framework combined with four CNN layers and LSTM to predict the virus-host PPI only using protein sequence information. CNN can extract the nonlinear position-related features of protein sequence, and LSTM can obtain the long-term relevant information. L1-regularized logistic regression is applied to eliminate the noise and redundant information. Our model achieved the best performance on the benchmark dataset and independent set compared with other existing methods. Conclusion: Our method, through the hybrid deep neural network, is useful for predicting virus-host PPI using protein sequence alone, and achieved the best prediction performance compared with other existing methods, which is promising on the virus-host PPI prediction Bioinformatics protein-protein interaction Convolution neural network LSTM Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-506156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":31581495,"identity":"713f364d-c22c-4545-bd5f-21d188047be6","order_by":0,"name":"Lei Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3LsQrCMBCA4QuFdolmDRT0FSJO4svYxS4KTk4qlcJNSteKL6GbYyWgS3HO4KBvoJsOglW7J26C+eEuBO4DsNl+sCqQiBdv7fN1DYhbkuY3BOBFgsicePFUXTfjMElyAZehBLaMNIRu49Yi3/dT1RMkPUjgx0xDeIB+BXf9FafCqaAEwTsaUj+j/8BdKFgunIcR4QR9gqOOgJ5wiBGhQdyaY9ZIVXewnR1CypWGMG9/Vnec1Fki16fbsF1jqYaUyffOiqFG90UT00ObzWb7x54DoT/GhIK2QwAAAABJRU5ErkJggg==","orcid":"","institution":"Central South University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Deng","suffix":""},{"id":31581496,"identity":"5aed4bfb-1a37-4323-a80d-61ea118fccc1","order_by":1,"name":"Wenjuan Nie","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjuan","middleName":"","lastName":"Nie","suffix":""},{"id":31581497,"identity":"0c1d29f1-7bec-4986-980c-408b95f7e33b","order_by":2,"name":"Jiaojiao Zhao","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiaojiao","middleName":"","lastName":"Zhao","suffix":""},{"id":31581498,"identity":"fb463a04-a56b-49d9-9340-765bbd65d538","order_by":3,"name":"Jingpu Zhang","email":"","orcid":"","institution":"Henan University of Urban Construction","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingpu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2021-05-08 08:59:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-506156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-506156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13641939,"identity":"9994e9c4-4120-4647-a548-54d28a4bb3ed","added_by":"auto","created_at":"2021-09-17 09:05:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":355647,"visible":true,"origin":"","legend":"","description":"","filename":"Ahybriddeeplearningframeworkforpredictingtheproteinproteininteractionbetweenvirusandhost.pdf","url":"https://assets-eu.researchsquare.com/files/rs-506156/v1_covered.pdf"},{"id":10110528,"identity":"5c172e9d-679b-4123-843c-8577d2339c8a","added_by":"auto","created_at":"2021-06-08 12:38:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":351885,"visible":true,"origin":"","legend":"","description":"","filename":"Ahybriddeeplearningframeworkforpredictingtheproteinproteininteractionbetweenvirusandhost.pdf","url":"https://assets-eu.researchsquare.com/files/rs-506156/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A hybrid deep learning framework for predicting the protein-protein interaction between virus and host","fulltext":[{"header":"Full Text","content":"\u003cp\u003eDue to technical limitations, full-text HTML conversion of this manuscript could not be completed. 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Studying the virus-host PPI is beneficial to apprehending the mechanism of viral infection and developing new treatment drugs. Although several computational methods for predicting the virus-host PPI have been proposed, most of them are supported by the machine learning algorithms, making the hidden high-level feature difficult to be extracted. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe proposed a novel hybrid deep learning framework combined with four CNN layers and LSTM to predict the virus-host PPI only using protein sequence information. CNN can extract the nonlinear position-related features of protein sequence, and LSTM can obtain the long-term relevant information. L1-regularized logistic regression is applied to eliminate the noise and redundant information. 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