Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16

Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16 | 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 Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16 Jianlin Cheng, Jian Liu, Pawan Neupane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6845168/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract AlphaFold2 and AlphaFold3 have revolutionized protein structure prediction by enabling high-accuracy tertiary structure predictions for most single-chain proteins (monomers). However, obtaining high-quality predictions for some hard protein targets with shallow or noisy multiple sequence alignments (MSAs) and complicated multi-domain architectures remains challenging. Here, we present MULTICOM4, an integrative protein structure prediction system that uses diverse MSA generation, large-scale model sampling, and an ensemble model quality assessment (QA) strategy of combining individual QA methods to improve model generation and ranking of AlphaFold2 and AlphaFold3. In the 16th Critical Assessment of Techniques for Protein Structure Prediction (CASP16), our predictors built on MULTICOM4 ranked among the top performers out of 120 predictors in tertiary structure prediction and outperformed a standard AlphaFold3 predictor. The average TM-score of our best performing predictor MULTCOM’s top-1 prediction for 84 CASP16 domain is 0.902. It achieved high accuracy (TM-score > 0.9) for 73.8% of the 84 domains and correct fold predictions (TM-score > 0.5) for 97.6% domains in terms of top-1 prediction. In terms of bestof-top-5 prediction, it predicted correct folds for all the domains. The results show that MSA engineering through the use of different protein sequence databases, alignment tools, and domain segmentation as well as extensive model sampling are the key to generate accurate and correct structural models. Additionally, using multiple complementary QA methods and model clustering can improve the robustness and reliability of model ranking. Biological sciences/Computational biology and bioinformatics/Protein structure predictions Biological sciences/Computational biology and bioinformatics/Protein analysis AlphaFold protein structure prediction protein model quality assessment deep learning Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 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-6845168","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472968084,"identity":"5ff4b253-ff0d-4d33-9e7c-5b155e2aeb78","order_by":0,"name":"Jianlin Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCRDBIyHHwAzh8wCxAVFajEnVwsCQ2IAkhl8L/+zmYw+/yFikr21nf/zhY9s9GQb25m0SeC25cyzdWIZHInfbYR4zyZltxTwMPMfK8GoxkMgxk5aAaGFj5m1L4GEAihDQkv8NpCXd7DD7489gLfJvCGnJYZP8wCORYHaYwUAaYgsPfi0SN9LMpIGBbAj2y4xzCTxsPGnFFvi08M9Ifib5s6dO3uz88ccfPpQl2POzH954A58WEGDm7UHisRFSDgKMP34Qo2wUjIJRMApGLAAAWOY8ybNhuQUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0305-2853","institution":"University of Missouri","correspondingAuthor":true,"prefix":"","firstName":"Jianlin","middleName":"","lastName":"Cheng","suffix":""},{"id":472968085,"identity":"5ed5a074-7da9-4809-b593-c70d626ba361","order_by":1,"name":"Jian Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Liu","suffix":""},{"id":472968086,"identity":"a66d378b-990d-4545-9b97-691e159d0337","order_by":2,"name":"Pawan Neupane","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pawan","middleName":"","lastName":"Neupane","suffix":""}],"badges":[],"createdAt":"2025-06-08 02:50:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6845168/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6845168/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42003-025-08960-6","type":"published","date":"2025-11-17T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96154898,"identity":"49470bb3-31c7-4f17-9ae9-540141b66acb","added_by":"auto","created_at":"2025-11-18 08:16:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11069006,"visible":true,"origin":"","legend":"Article File","description":"","filename":"CASP16TS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6845168/v1_covered_56189380-b021-48a6-9f79-b0e4fc5015b8.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in\r\nCASP16","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":"AlphaFold, protein structure prediction, protein model quality assessment, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-6845168/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6845168/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlphaFold2 and AlphaFold3 have revolutionized protein structure prediction by enabling high-accuracy tertiary structure predictions for most single-chain proteins (monomers). However, obtaining high-quality predictions for some hard protein targets with shallow or noisy multiple sequence alignments (MSAs) and complicated multi-domain architectures remains challenging. Here, we present MULTICOM4, an integrative protein structure prediction system that uses diverse MSA generation, large-scale model sampling, and an ensemble model quality assessment (QA) strategy of combining individual QA methods to improve model generation and ranking of AlphaFold2 and AlphaFold3. In the 16th Critical Assessment of Techniques for Protein Structure Prediction (CASP16), our predictors built on MULTICOM4 ranked among the top performers out of 120 predictors in tertiary structure prediction and outperformed a standard AlphaFold3 predictor. The average TM-score of our best performing predictor MULTCOM’s top-1 prediction for 84 CASP16 domain is 0.902. It achieved high accuracy (TM-score \u0026gt; 0.9) for 73.8% of the 84 domains and correct fold predictions (TM-score \u0026gt; 0.5) for 97.6% domains in terms of top-1 prediction. In terms of bestof-top-5 prediction, it predicted correct folds for all the domains. The results show that MSA engineering through the use of different protein sequence databases, alignment tools, and domain segmentation as well as extensive model sampling are the key to generate accurate and correct structural models. 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