Dynamicasome! Comprehensive Mutational Analysis and AI-Driven Prediction of PMM2 Pathogenicity: Integrating Molecular Dynamics Simulations with Machine Learning Models

Dynamicasome! Comprehensive Mutational Analysis and AI-Driven Prediction of PMM2 Pathogenicity: Integrating Molecular Dynamics Simulations with Machine Learning Models | 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 Dynamicasome! Comprehensive Mutational Analysis and AI-Driven Prediction of PMM2 Pathogenicity: Integrating Molecular Dynamics Simulations with Machine Learning Models Thomas Caulfield, Naeyma Islam, Mathew Coban, Jessica Fuller, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5538161/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract Advances in genomic medicine have accelerated the identification of mutations in disease- associated genes, but the pathogenicity of many mutations remains unknown, hindering their use in diagnostics and clinical decision-making. Predictive AI models have been generated to combat this issue, but current tools display low accuracy when tested against functionally validated datasets. We show that integrating detailed conformational data extracted from molecular dynamics simulations (MDS) into advanced AI-based models can increase their predictive power. We carried out an exhaustive mutational analysis of the disease gene PMM2 and subjected structural models of each variant to MDS. AI models trained on this dataset outperformed existing tools when predicting the known pathogenicity of mutations. Our best performing model, a neuronal networks model, was also able to predict the pathogenicity of several PMM2 mutations currently considered of unknown significance. We believe this new model will help alleviate the burden of unknown variants in genomic medicine. Biological sciences/Computational biology and bioinformatics/Genome informatics Physical sciences/Mathematics and computing/Computational science Biological sciences/Genetics/Genomics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files 3SupplementalFigsDataAIvariantsCaulfieldIslamOct2024.pdf Dynamicasome! Comprehensive Mutational Analysis and AI-Driven Prediction of PMM2 Pathogenicity: Integrating Molecular Dynamics Simulations with Machine Learning Models Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Communications Biology → 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-5538161","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392947725,"identity":"56f68137-ff61-421d-8163-31db086947c4","order_by":0,"name":"Thomas Caulfield","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIie3RLQsCMRjA8WcceOVRq4L4GUwWRb/KHcJZ1GI1CIIGX7rgh1g662R4lrOfaNBiUtAigqBuvoAIO4yG/cvKfmzPBqDT/XEpswWEIYAFs2b4VnyvyAGexGe/EvYmgRVOimbXgnODI5oGYztY1WKL/ZSSSl59Cs4p6XmCGBFrMoJtPbmsOQFxHTVJVMcXiHAsGpjh0Su36bKSFYSHEWrAVZ4SP3AEQRa+JLdwQtqSiP0PEqAkTE18MUt/UJazZOQs9tAXs9huSUnMTp/C+ZRLY5xv1uLF7MFs7gVHt6Ak38n7PH7n915Ep9PpdB/dATPjWpVN4/69AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1055-3069","institution":"Mayo Clinic","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Caulfield","suffix":""},{"id":392947726,"identity":"3b5bd2e5-d8ca-4f77-9382-de56bd0f5638","order_by":1,"name":"Naeyma Islam","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Naeyma","middleName":"","lastName":"Islam","suffix":""},{"id":392947727,"identity":"c3ecb7be-2aa6-49be-b3d4-36309c8e4cec","order_by":2,"name":"Mathew Coban","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Mathew","middleName":"","lastName":"Coban","suffix":""},{"id":392947728,"identity":"d656c9d1-f1db-4d88-a656-ce9ac8d51dda","order_by":3,"name":"Jessica Fuller","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Fuller","suffix":""},{"id":392947729,"identity":"2c7991b8-66c1-4ae0-afa7-5fa7e99c97c3","order_by":4,"name":"Caleb Weber","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Caleb","middleName":"","lastName":"Weber","suffix":""},{"id":392947730,"identity":"2d2a6af6-ee22-4ec4-b8c9-0827717bbcb1","order_by":5,"name":"Rohit Chitale","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Chitale","suffix":""},{"id":392947731,"identity":"29c5b7ee-3324-4d29-9a11-597f5ada7a63","order_by":6,"name":"Benjamin Jussila","email":"","orcid":"https://orcid.org/0000-0001-9764-0482","institution":"In Vivo Biosystems, INC","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Jussila","suffix":""},{"id":392947732,"identity":"751b46fd-bebd-46b6-9e67-d7dadf07eea2","order_by":7,"name":"Trisha Brock","email":"","orcid":"","institution":"In Vivo Biosystems, INC","correspondingAuthor":false,"prefix":"","firstName":"Trisha","middleName":"","lastName":"Brock","suffix":""},{"id":392947733,"identity":"35e94df4-ce12-4804-92b0-34f74da3c607","order_by":8,"name":"Cui Tao","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2024-11-27 22:55:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5538161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5538161/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42003-025-08334-y","type":"published","date":"2025-07-07T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86221521,"identity":"d332e286-73d3-4118-9f3d-7a8e4f799be3","added_by":"auto","created_at":"2025-07-08 07:10:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2008267,"visible":true,"origin":"","legend":"Article File","description":"","filename":"2NATGENE2024AIvariantsTAOCAULFIELDv2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5538161/v1_covered_e12be060-5425-4b01-9209-db751f259c16.pdf"},{"id":74631121,"identity":"a978fb25-8989-4443-8a0d-a97c30642d8d","added_by":"auto","created_at":"2025-01-24 07:49:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1844267,"visible":true,"origin":"","legend":"Dynamicasome! Comprehensive Mutational Analysis and AI-Driven Prediction of PMM2 Pathogenicity: Integrating Molecular Dynamics Simulations with Machine Learning Models","description":"","filename":"3SupplementalFigsDataAIvariantsCaulfieldIslamOct2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5538161/v1/a1cbfd1395ba360f24a397ce.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Dynamicasome! Comprehensive Mutational Analysis and AI-Driven Prediction of PMM2 Pathogenicity: Integrating Molecular Dynamics Simulations with Machine Learning Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5538161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5538161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Advances in genomic medicine have accelerated the identification of mutations in disease- associated genes, but the pathogenicity of many mutations remains unknown, hindering their use in diagnostics and clinical decision-making. Predictive AI models have been generated to combat this issue, but current tools display low accuracy when tested against functionally validated datasets. We show that integrating detailed conformational data extracted from molecular dynamics simulations (MDS) into advanced AI-based models can increase their predictive power. We carried out an exhaustive mutational analysis of the disease gene PMM2 and subjected structural models of each variant to MDS. AI models trained on this dataset outperformed existing tools when predicting the known pathogenicity of mutations. Our best performing model, a neuronal networks model, was also able to predict the pathogenicity of several PMM2 mutations currently considered of unknown significance. We believe this new model will help alleviate the burden of unknown variants in genomic medicine.","manuscriptTitle":"Dynamicasome! Comprehensive Mutational Analysis and AI-Driven Prediction of PMM2 Pathogenicity: Integrating Molecular Dynamics Simulations with Machine Learning Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 07:49:52","doi":"10.21203/rs.3.rs-5538161/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"221261f2-5ceb-4aeb-948b-f626845220d1","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41894629,"name":"Biological sciences/Computational biology and bioinformatics/Genome informatics"},{"id":41894630,"name":"Physical sciences/Mathematics and computing/Computational science"},{"id":41894631,"name":"Biological sciences/Genetics/Genomics"}],"tags":[],"updatedAt":"2025-07-08T07:10:14+00:00","versionOfRecord":{"articleIdentity":"rs-5538161","link":"https://doi.org/10.1038/s42003-025-08334-y","journal":{"identity":"communications-biology","isVorOnly":false,"title":"Communications Biology"},"publishedOn":"2025-07-07 04:00:00","publishedOnDateReadable":"July 7th, 2025"},"versionCreatedAt":"2025-01-24 07:49:52","video":"","vorDoi":"10.1038/s42003-025-08334-y","vorDoiUrl":"https://doi.org/10.1038/s42003-025-08334-y","workflowStages":[]},"version":"v1","identity":"rs-5538161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5538161","identity":"rs-5538161","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}