Proteoform Search from Protein Database with Top-Down Mass Spectra: Algorithms and Evaluation

Proteoform Search from Protein Database with Top-Down Mass Spectra: Algorithms and Evaluation | 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 Proteoform Search from Protein Database with Top-Down Mass Spectra: Algorithms and Evaluation Lusheng Wang, Kunyi Li, Ming Li, Baozhen Shan, Lei Xin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5429797/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Nature Computational Science → Version 1 posted You are reading this latest preprint version Abstract In this paper, we propose a new search algorithm for proteoform identification that computes the largest size error correction alignments between a protein mass graph (PMG) and a spectrum mass graph (SMG). We also design a filtering algorithm. The combined method uses the filtering algorithm to get some candidates and then apply the search algorithm to report the final results. Our exact searching method is 3.9 to 9.0 times faster than the popular methods such as TopMG and TopPIC. Our combined method can further speed up the running time of our method sTopMG by 6.2 times without affecting the search accuracy. Lots of search methods have been developed in the past decade. The search results reported by various methods are known to be significantly different. In literature, there is no top-down mass spectra dataset with known corresponding protein sequences. There is no tool for generating simulated top-down mass spectra with input protein sequences, either. Though there are many published papers to compare and evaluate various search methods, they all use some kind of indirect measures since there is no real or simulated dataset of top-down spectra with known corresponding true protein sequences. Thus, in some sense, the accuracy of existing search methods is somewhat uncertain. Here we develop a pipeline for generating simulated top-down spectra based on input protein sequences with modifications. To our knowledge, this is the first tool to generate simulated top-down mass spectra in a reasonable way indicated by an interesting measure, match gap distribution. Experiments on simulated datasets show that our combined method has of 95% accuracy, while the best existing methods have accuracy far below this. To further evaluation the performance of the existing methods, we generate a set of 55 real top-down spectra from 3 domains of a known antibody. The real dataset shows that our new method has 94.2% accuracy using deconvolution method FLASHDeconv, which is consistent with the accuracy of the simulated data. Biological sciences/Computational biology and bioinformatics/Software Biological sciences/Computational biology and bioinformatics/Protein analysis/Protein sequence analyses Proteoform identification search algorithms filtering algorithm top-down spectra simulator Full Text Additional Declarations There is NO Competing Interest. Supplementary Files 100simms2.txt Dataset 1 Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Nature Computational Science → 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-5429797","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":405184244,"identity":"9aa5a20b-ea58-40f2-9a65-133dd647f2cf","order_by":0,"name":"Lusheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACPgYGNgTvA4MEhMHDwJAggUMLG7IWxhkMEhKkaWEGqiRCi0T6swcfd9QyGBw/e/i1TY1FncGNBMYHb9sY8iQbcGnJMTeceeY4g8GZvDTrnGMSEkAtzIZz2xiKpXHaksMmzdt2jMHsQI6ZcW4DWAtIhCFxHh6HSf8FaTn/xszYEqKF/Td+LQlm0oxtNQxmN3KMHzNCbWEGaZmNSwvPGzPJ3rYDPPY33pgx9hyTkJx55mGz5JxzEokzcXifnz39mcTPtjo5yf4c4w8/aur4+Y4nH/zwpswmccYBHNZAwGEesCNBTIUDjCDjccUKHNSBCOYPIFIeh3tGwSgYBaNg5AIA2ZhWEIggvUQAAAAASUVORK5CYII=","orcid":"","institution":"City University of Hong Kong","correspondingAuthor":true,"prefix":"","firstName":"Lusheng","middleName":"","lastName":"Wang","suffix":""},{"id":405184245,"identity":"f13431cf-da56-425e-990a-2953ed58e63a","order_by":1,"name":"Kunyi Li","email":"","orcid":"","institution":"Department of Computer Science, City University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Kunyi","middleName":"","lastName":"Li","suffix":""},{"id":405184246,"identity":"cb79104a-a58b-4047-88d8-4968ea5c1fbb","order_by":2,"name":"Ming Li","email":"","orcid":"","institution":"University of Waterloo","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Li","suffix":""},{"id":405184247,"identity":"b6d62888-853f-4a51-94ed-fef2c55d24d4","order_by":3,"name":"Baozhen Shan","email":"","orcid":"","institution":"Bioinformatics Solutions Inc","correspondingAuthor":false,"prefix":"","firstName":"Baozhen","middleName":"","lastName":"Shan","suffix":""},{"id":405184248,"identity":"57e3a4fc-10c4-4630-87b5-1f6d219cae10","order_by":4,"name":"Lei Xin","email":"","orcid":"","institution":"Bioinformatics Solutions Inc","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xin","suffix":""}],"badges":[],"createdAt":"2024-11-11 07:50:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5429797/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5429797/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43588-025-00880-z","type":"published","date":"2025-10-03T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92761541,"identity":"e46118af-1ee2-4889-b594-86df97d87896","added_by":"auto","created_at":"2025-10-04 07:08:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1836852,"visible":true,"origin":"","legend":"Article File","description":"","filename":"sTopMGManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5429797/v1_covered_dee724e6-3238-4aa6-a9a1-6f18b3d981a5.pdf"},{"id":74401622,"identity":"7cbea08e-0d3b-48da-a76f-d934ef6ebe27","added_by":"auto","created_at":"2025-01-22 03:21:12","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1111081,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"100simms2.txt","url":"https://assets-eu.researchsquare.com/files/rs-5429797/v1/9c41ddb5f76526683d57fa35.txt"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Proteoform Search from Protein Database with Top-Down Mass Spectra: Algorithms and Evaluation","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":"Proteoform identification, search algorithms, filtering algorithm, top-down spectra simulator","lastPublishedDoi":"10.21203/rs.3.rs-5429797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5429797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, we propose a new search algorithm for proteoform identification that computes the largest size error correction alignments between a protein mass graph (PMG) and a spectrum mass graph (SMG). We also design a filtering algorithm. The combined method uses the filtering algorithm to get some candidates and then apply the search algorithm to report the final results. Our exact searching method is 3.9 to 9.0 times faster than the popular methods such as TopMG and TopPIC. Our combined method can further speed up the running time of our method sTopMG by 6.2 times without affecting the search accuracy. Lots of search methods have been developed in the past decade. The search results reported by various methods are known to be significantly different. In literature, there is no top-down mass spectra dataset with known corresponding protein sequences. There is no tool for generating simulated top-down mass spectra with input protein sequences, either. Though there are many published papers to compare and evaluate various search methods, they all use some kind of indirect measures since there is no real or simulated dataset of top-down spectra with known corresponding true protein sequences. Thus, in some sense, the accuracy of existing search methods is somewhat uncertain. Here we develop a pipeline for generating simulated top-down spectra based on input protein sequences with modifications. To our knowledge, this is the first tool to generate simulated top-down mass spectra in a reasonable way indicated by an interesting measure, match gap distribution. Experiments on simulated datasets show that our combined method has of 95% accuracy, while the best existing methods have accuracy far below this. To further evaluation the performance of the existing methods, we generate a set of 55 real top-down spectra from 3 domains of a known antibody. The real dataset shows that our new method has 94.2% accuracy using deconvolution method FLASHDeconv, which is consistent with the accuracy of the simulated data.","manuscriptTitle":"Proteoform Search from Protein Database with Top-Down Mass Spectra: Algorithms and Evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 03:21:08","doi":"10.21203/rs.3.rs-5429797/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-computational-science","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcomputsci","sideBox":"Learn more about [Nature Computational Science](http://www.nature.com/natcomputsci/)","snPcode":"","submissionUrl":"","title":"Nature Computational Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d2f80aa1-d65c-4467-b3f5-122a5473979a","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43200752,"name":"Biological sciences/Computational biology and bioinformatics/Software"},{"id":43200753,"name":"Biological sciences/Computational biology and bioinformatics/Protein analysis/Protein sequence analyses"}],"tags":[],"updatedAt":"2025-10-04T07:08:12+00:00","versionOfRecord":{"articleIdentity":"rs-5429797","link":"https://doi.org/10.1038/s43588-025-00880-z","journal":{"identity":"nature-computational-science","isVorOnly":false,"title":"Nature Computational Science"},"publishedOn":"2025-10-03 04:00:00","publishedOnDateReadable":"October 3rd, 2025"},"versionCreatedAt":"2025-01-22 03:21:08","video":"","vorDoi":"10.1038/s43588-025-00880-z","vorDoiUrl":"https://doi.org/10.1038/s43588-025-00880-z","workflowStages":[]},"version":"v1","identity":"rs-5429797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5429797","identity":"rs-5429797","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}