Web server DDfit: a new scheme to process PFG NMR diffusion data with improved precision

Web server DDfit: a new scheme to process PFG NMR diffusion data with improved precision | 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 Web server DDfit: a new scheme to process PFG NMR diffusion data with improved precision Vladislav A. Salikov, Olga O. Lebedenko, Nikolai R. Skrynnikov, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7807553/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Journal of Biomolecular NMR → Version 1 posted 7 You are reading this latest preprint version Abstract In this communication we describe a new scheme to process the data from stimulated echo protein diffusion experiments. For a series of gradient-encoded proton spectra \(\:{f}_{k}\left(\omega\:\right)\) considered over the selected spectral region \(\:({\omega\:}_{left},{\omega\:}_{right})\) , we build a model to approximate the unique (protein-dependent) shape of the spectrum. Taking a cue from the optimal filtration theory, \(\:{f}_{model}\left(\omega\:\right)\) is constructed as the intensity-weighted combination of \(\:{f}_{k}\left(\omega\:\right)\) . The so obtained \(\:{f}_{model}\left(\omega\:\right)\) is then used to fit the individual spectra \(\:{f}_{k}\left(\omega\:\right)\) , thus providing highly accurate estimates for the integral signal intensities that are subsequently used for Stejskal-Tanner-type analyses. This algorithm has been implemented as a part of a new web server, named DDfit ( https://ddfit.bio-nmr.spbu.ru/ ). The server accepts spectrometer data from the standard stimulated and double-stimulated echo experiments by Bruker, as well as custom-designed experiments. The server is easy to use, with data processing taking no more than several seconds. Our tests using simulated as well as experimental data found that DDfit determines protein diffusion coefficients with both accuracy and precision, offering several-fold improvement in precision compared to other processing schemes. protein diffusion PFG NMR stimulated echo spectral fitting signal-to-noise optimization baseline correction Full Text Additional Declarations No competing interests reported. Supplementary Files ddfitv34SI.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Journal of Biomolecular NMR → Version 1 posted Editorial decision: Revision requested 22 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 08 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 08 Oct, 2025 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-7807553","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533278805,"identity":"7469e898-a002-464d-8c07-9fb579f93096","order_by":0,"name":"Vladislav A. 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"protein diffusion, PFG NMR, stimulated echo, spectral fitting, signal-to-noise optimization, baseline correction","lastPublishedDoi":"10.21203/rs.3.rs-7807553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7807553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this communication we describe a new scheme to process the data from stimulated echo protein diffusion experiments. For a series of gradient-encoded proton spectra \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{k}\\left(\\omega\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e considered over the selected spectral region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:({\\omega\\:}_{left},{\\omega\\:}_{right})\\)\u003c/span\u003e\u003c/span\u003e, we build a model to approximate the unique (protein-dependent) shape of the spectrum. Taking a cue from the optimal filtration theory, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{model}\\left(\\omega\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e is constructed as the intensity-weighted combination of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{k}\\left(\\omega\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e. The so obtained \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{model}\\left(\\omega\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e is then used to fit the individual spectra \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{k}\\left(\\omega\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e, thus providing highly accurate estimates for the integral signal intensities that are subsequently used for Stejskal-Tanner-type analyses. This algorithm has been implemented as a part of a new web server, named DDfit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ddfit.bio-nmr.spbu.ru/\u003c/span\u003e\u003cspan address=\"https://ddfit.bio-nmr.spbu.ru/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The server accepts spectrometer data from the standard stimulated and double-stimulated echo experiments by Bruker, as well as custom-designed experiments. The server is easy to use, with data processing taking no more than several seconds. Our tests using simulated as well as experimental data found that DDfit determines protein diffusion coefficients with both accuracy and precision, offering several-fold improvement in precision compared to other processing schemes.\u003c/p\u003e","manuscriptTitle":"Web server DDfit: a new scheme to process PFG NMR diffusion data with improved precision","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 12:33:13","doi":"10.21203/rs.3.rs-7807553/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-22T08:43:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T09:20:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165988723671975290738146620508052367634","date":"2025-10-14T08:17:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-14T08:07:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T13:52:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T13:52:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Biomolecular NMR","date":"2025-10-08T11:41:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-biomolecular-nmr","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jnmr","sideBox":"Learn more about [Journal of Biomolecular NMR](http://link.springer.com/journal/10858)","snPcode":"10858","submissionUrl":"https://submission.nature.com/new-submission/10858/3","title":"Journal of Biomolecular NMR","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d2690e74-78a5-4700-8d11-9c6aeff90de2","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:08:44+00:00","versionOfRecord":{"articleIdentity":"rs-7807553","link":"https://doi.org/10.1007/s10858-026-00487-0","journal":{"identity":"journal-of-biomolecular-nmr","isVorOnly":false,"title":"Journal of Biomolecular NMR"},"publishedOn":"2026-02-17 15:57:32","publishedOnDateReadable":"February 17th, 2026"},"versionCreatedAt":"2025-10-28 12:33:13","video":"","vorDoi":"10.1007/s10858-026-00487-0","vorDoiUrl":"https://doi.org/10.1007/s10858-026-00487-0","workflowStages":[]},"version":"v1","identity":"rs-7807553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7807553","identity":"rs-7807553","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}