Discrete Geometry Chemistry Chemical Shift Predictor: Ca Chemical Shifts in Small Peptides

Discrete Geometry Chemistry Chemical Shift Predictor: Ca Chemical Shifts in Small Peptides | 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 Discrete Geometry Chemistry Chemical Shift Predictor: Ca Chemical Shifts in Small Peptides Caio Lima Firme This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8886018/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 Predicting NMR Cα chemical shifts from peptide structures typically requires computationally expensive quantum mechanical calculations or extensive empirical databases. The discrete geometry chemistry (DGC) paradigm offers an alternative: molecular properties approximated through geometric abstraction and minimal parameterization. This work demonstrates that Cα shifts in small peptides (6–60 residues) can be predicted using only backbone Cα coordinates and residue physicochemical properties. A linear ridge regression model with 27 features—12 geometric descriptors (k-nearest neighbor distances, radius of gyration, local density) and 15 physicochemical parameters (hydrophobicity, volume, secondary structure propensities)—achieves mean absolute error of 2.65 ppm across 585 residues from 21 NMR structures. The model generalizes well to ordered peptides (MAE = 1.92 ppm) but fails on proline/glycine-rich or disordered structures (MAE = 4.03 ppm). With microsecond-scale inference requiring only arithmetic operations, this zero-cost framework addresses 36,652 Protein Data Bank peptides lacking experimental Cα shift data, enabling rapid screening for peptide design and NMR assignment validation. Organic Chemistry Mathematical and Theoretical Biology Theoretical Computer Science Discrete Geometry Chemistry Peptides Chemical Shifts NMR Full Text Additional Declarations The authors declare no competing interests. Supplementary Files DGCpeptideschemicalShiftssupplementarymaterial.docx 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. 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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-8886018","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591627113,"identity":"8112efdd-3978-4f12-a0c4-a5784a56aa95","order_by":0,"name":"Caio Lima Firme","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBAC9mYog42B+QCQkpAhqIXnMFwLWwJICw9hLQcQTAMwSVgLO+/TDR8Y7OT52Hs+v7pRY8HDwH746Aa8WpjZzW7OYEg2bOM5u8065xjQYTxpaTfwabFnZmO7zcNwgLFNInebcQ4bUIsEjxleLTwgLX8YDti3SeQ8M875R6wWBoYDiUAtzI9z24jUcrPHIDm5jeeYGXNunwQPGyG/8PAfY7vxo8LOdn578+PPOd/q5PjZDx/DqwUCwDHCwCYBJgkrRwDmD6SoHgWjYBSMgpEDAJuEPJDe9SvSAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5203-6262","institution":"Universidade Federal do Rio Grande do Norte","correspondingAuthor":true,"prefix":"","firstName":"Caio","middleName":"Lima","lastName":"Firme","suffix":""}],"badges":[],"createdAt":"2026-02-15 12:46:08","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8886018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8886018/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102908571,"identity":"707ac16c-9b38-462f-ac29-d3c79eaf4082","added_by":"auto","created_at":"2026-02-18 09:50:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26098,"visible":true,"origin":"","legend":"","description":"","filename":"DGCpeptideschemicalShiftssupplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8886018/v1/518748ab69e4939d6fc64f25.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDiscrete Geometry Chemistry Chemical Shift Predictor: Ca Chemical Shifts in Small Peptides\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"No 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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Discrete Geometry Chemistry, Peptides, Chemical Shifts, NMR","lastPublishedDoi":"10.21203/rs.3.rs-8886018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8886018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting NMR Cα chemical shifts from peptide structures typically requires computationally expensive quantum mechanical calculations or extensive empirical databases. The discrete geometry chemistry (DGC) paradigm offers an alternative: molecular properties approximated through geometric abstraction and minimal parameterization. This work demonstrates that Cα shifts in small peptides (6\u0026ndash;60 residues) can be predicted using only backbone Cα coordinates and residue physicochemical properties. A linear ridge regression model with 27 features\u0026mdash;12 geometric descriptors (k-nearest neighbor distances, radius of gyration, local density) and 15 physicochemical parameters (hydrophobicity, volume, secondary structure propensities)\u0026mdash;achieves mean absolute error of 2.65 ppm across 585 residues from 21 NMR structures. The model generalizes well to ordered peptides (MAE\u0026thinsp;=\u0026thinsp;1.92 ppm) but fails on proline/glycine-rich or disordered structures (MAE\u0026thinsp;=\u0026thinsp;4.03 ppm). With microsecond-scale inference requiring only arithmetic operations, this zero-cost framework addresses 36,652 Protein Data Bank peptides lacking experimental Cα shift data, enabling rapid screening for peptide design and NMR assignment validation.\u003c/p\u003e","manuscriptTitle":"Discrete Geometry Chemistry Chemical Shift Predictor: Ca Chemical Shifts in Small Peptides","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 09:49:55","doi":"10.21203/rs.3.rs-8886018/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08a54b95-2100-4135-a11e-c8d54df8c3b3","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62955749,"name":"Organic Chemistry"},{"id":62955750,"name":"Mathematical and Theoretical Biology"},{"id":62955751,"name":"Theoretical Computer Science"}],"tags":[],"updatedAt":"2026-02-18T09:49:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 09:49:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8886018","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8886018","identity":"rs-8886018","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}