CryoNeFEN: High-resolution reconstruction of cryo-EM structures using neural field network

CryoNeFEN: High-resolution reconstruction of cryo-EM structures using neural field network | 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 CryoNeFEN: High-resolution reconstruction of cryo-EM structures using neural field network Manhua Liu, Yue Huang, Zhu Chengguang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3824277/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jul, 2024 Read the published version in Nature Machine Intelligence → Version 1 posted You are reading this latest preprint version Abstract The elucidation of three-dimensional (3D) structures is crucial to unraveling protein function and illuminating mechanisms in structural biology. Cryogenic electron microscopy (cryo-EM) single-particle analysis provides direct measurements to determine the structures of macromolecules. However, the main challenge is reconstructing high-resolution 3D structures from extremely noisy and randomly oriented 2D projection images. Most existing methods primarily involve the optimization of multiple 2D slices in the Fourier domain but ignore the anisotropy among these slices, thus limiting the reconstruction of high-frequency structures. In this paper, we propose a cryo-EM neural field reconstruction network (cryoNeFEN), a new 3D spatial domain optimization paradigm, that learns a directional isotropic representation of the cryo-EM structure by mapping spatial coordinates to corresponding density values. We qualitatively and quantitatively evaluate cryoNeFEN on four experimental datasets. The results demonstrate the improved directional isotropy and 3D density resolution beyond the limits of existing algorithms in homogeneous reconstruction and resolve the missing elements of SARS-CoV-2 distinctly in heterogeneous reconstruction. Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopy Physical sciences/Mathematics and computing/Computer science Deep-learning Cryo-EM reconstruction 3D reconstruction Neural fields Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 12 Jul, 2024 Read the published version in Nature Machine Intelligence → 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-3824277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267423417,"identity":"fe578ce1-31c1-46bd-a7c5-d5b732a3621d","order_by":0,"name":"Manhua Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACCeaGAwwVDMxgDg9xWhiBWs4wMPOQpIWBsQ2qmigturMbGw/zzqtjt5dIYHzwto1B3pyQFrM7BxsO8247zMwjkcBsOLeNwXBnAyEtNxJBWg6AtLBJ87YxJBgcIErLnDqQFvbfJGhpYAbbwky0loNzjgH9cuZhs+SccxKGGwhrST784U1NXTJ7e/LBD2/KbOQJ2gIDyQwMwAgCRhOR6oHAjnilo2AUjIJRMOIAADnnPl0OJhkEAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6496-670X","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Manhua","middleName":"","lastName":"Liu","suffix":""},{"id":267423418,"identity":"e80ab173-d282-4fb5-b620-13d3c1f51f8e","order_by":1,"name":"Yue Huang","email":"","orcid":"https://orcid.org/0009-0006-5698-4223","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Huang","suffix":""},{"id":267423419,"identity":"e1fcccfd-9ded-42bf-b698-140eeaf2895f","order_by":2,"name":"Zhu Chengguang","email":"","orcid":"https://orcid.org/0000-0002-7737-4225","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Chengguang","suffix":""}],"badges":[],"createdAt":"2023-12-30 15:00:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3824277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3824277/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42256-024-00870-2","type":"published","date":"2024-07-12T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60209474,"identity":"02f7c8a3-b5cf-4c53-a1ad-b47dc0d1c509","added_by":"auto","created_at":"2024-07-13 07:05:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24310363,"visible":true,"origin":"","legend":"Article File","description":"","filename":"manuscript0113.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3824277/v1_covered_63255917-330a-4631-82ea-047cb750ab12.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"CryoNeFEN: High-resolution reconstruction of cryo-EM structures using neural field network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep-learning, Cryo-EM reconstruction, 3D reconstruction, Neural fields","lastPublishedDoi":"10.21203/rs.3.rs-3824277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3824277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The elucidation of three-dimensional (3D) structures is crucial to unraveling protein function and illuminating mechanisms in structural biology. Cryogenic electron microscopy (cryo-EM) single-particle analysis provides direct measurements to determine the structures of macromolecules. However, the main challenge is reconstructing high-resolution 3D structures from extremely noisy and randomly oriented 2D projection images. Most existing methods primarily involve the optimization of multiple 2D slices in the Fourier domain but ignore the anisotropy among these slices, thus limiting the reconstruction of high-frequency structures. In this paper, we propose a cryo-EM neural field reconstruction network (cryoNeFEN), a new 3D spatial domain optimization paradigm, that learns a directional isotropic representation of the cryo-EM structure by mapping spatial coordinates to corresponding density values. We qualitatively and quantitatively evaluate cryoNeFEN on four experimental datasets. The results demonstrate the improved directional isotropy and 3D density resolution beyond the limits of existing algorithms in homogeneous reconstruction and resolve the missing elements of SARS-CoV-2 distinctly in heterogeneous reconstruction.","manuscriptTitle":"CryoNeFEN: High-resolution reconstruction of cryo-EM structures using neural field network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-17 06:48:03","doi":"10.21203/rs.3.rs-3824277/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-machine-intelligence","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natmachintell","sideBox":"Learn more about [Nature Machine Intelligence](http://www.nature.com/natmachintell/)","snPcode":"","submissionUrl":"","title":"Nature Machine Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e5ceac81-5605-4ef1-bcd9-9ff6d9698021","owner":[],"postedDate":"January 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28173898,"name":"Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopy"},{"id":28173899,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2024-07-13T07:05:52+00:00","versionOfRecord":{"articleIdentity":"rs-3824277","link":"https://doi.org/10.1038/s42256-024-00870-2","journal":{"identity":"nature-machine-intelligence","isVorOnly":false,"title":"Nature Machine Intelligence"},"publishedOn":"2024-07-12 04:00:00","publishedOnDateReadable":"July 12th, 2024"},"versionCreatedAt":"2024-01-17 06:48:03","video":"","vorDoi":"10.1038/s42256-024-00870-2","vorDoiUrl":"https://doi.org/10.1038/s42256-024-00870-2","workflowStages":[]},"version":"v1","identity":"rs-3824277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3824277","identity":"rs-3824277","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}