Robust RNA structure refinement by a nucleobase-centric sampling algorithm coupled with a backbone rotameric and quantum-mechanical-energy-scaled base-base knowledge-based potential.

Robust RNA structure refinement by a nucleobase-centric sampling algorithm coupled with a backbone rotameric and quantum-mechanical-energy-scaled base-base knowledge-based potential. | 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 Robust RNA structure refinement by a nucleobase-centric sampling algorithm coupled with a backbone rotameric and quantum-mechanical-energy-scaled base-base knowledge-based potential. Yaoqi Zhou, Peng Xiong, Ruibo Wu, Jian Zhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-136920/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 Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because native structures are often not the global minimum of existing approximate energy scores. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base-base, base-oxygen and oxygen-oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4,000 quantum-mechanical calculations were performed to reweight base-base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Computational Biology Bioinformatics Molecular Biology Knowledge-based Energy Function Base-base Statistical Potentials Indirect Interactions Near-native RNA Models Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF. Additional Declarations There is NO Competing Interest. 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. 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-136920","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":7611285,"identity":"f5646890-f47f-495f-a64e-13551d611d6c","order_by":0,"name":"Yaoqi Zhou","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9958-5699","institution":"Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yaoqi","middleName":"","lastName":"Zhou","suffix":""},{"id":7611286,"identity":"95f19b91-de16-4e2a-99b1-2658a50c76bb","order_by":1,"name":"Peng Xiong","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Xiong","suffix":""},{"id":7611287,"identity":"b0b07859-661e-41cc-a94d-ca2a8d26eb14","order_by":2,"name":"Ruibo Wu","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruibo","middleName":"","lastName":"Wu","suffix":""},{"id":7611288,"identity":"7da1c3b1-4f0f-4c00-9b97-2af26b4cef52","order_by":3,"name":"Jian Zhan","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhan","suffix":""}],"badges":[],"createdAt":"2020-12-28 02:30:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-136920/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-136920/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4761597,"identity":"828d1fb7-d40a-4141-9ed2-84c3b9cbab20","added_by":"auto","created_at":"2021-01-06 20:42:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214717,"visible":true,"origin":"","legend":"(A) Six-dimensional base-base statistical potentials with relative positions defined by rij and rji vectors along with the rotational angle ωij. (B) A schematic illustration of the orientation dependence before and after Quantum Mechanical (QM) energy scaling. (C) This QM scaling is based on the correlation between the statistical energy scores of hydrogen-bonded base pairs (-ln Phb) and QM calculations. Phb is the probability of hydrogen-bonded base pairs. (D) The distribution of OP around nucleobase C and the distribution of O4’ around the nucleobase A as labelled. (E) Backbone rotamers defined according to various torsion and improper angles that control the ribose (χ, ν) and phosphate (ε, ζ) backbone. Dihedral angles α, β, γ, bond angles θPOC, θOCC and bond length of C5’O5’ were required to calculate the internal energy. (F). Nucleobase-centric fold-tree (NuTree) algorithm for refinement by defining bases as the nodes and locally or globally connected bases as the edges, illustrated by the GCAA tetraloop.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-136920/v1/c307f2670174f4f4d304c80a.png"},{"id":4761514,"identity":"39e6b74d-652a-453d-956d-6d55c93cc03f","added_by":"auto","created_at":"2021-01-06 20:39:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62244,"visible":true,"origin":"","legend":"(A) Refining Rosetta-SWM motif models by BRiQ improves the majority of those model structures with RMSD\u003c2Å as demonstrated by RMSD comparison (Lowest RMSD of top 1% before refinement on Y-axis versus after refinement on X-axis). (B) Fixing all native base pairs with random initial conformations for all other regions and then folding the remaining structure leads to more accurate motif models for the majority of the motifs than refining Rosetta-SWM models that have pre-assigned, partially fixed base pairs (Lowest RMSD of top 1% BRiQ-refined models on Y-axis versus lowest RMSD of top 1% BRiQ models with fixed native base pairs on X-axis). ","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-136920/v1/9897a66233d2bf3a5259ed29.png"},{"id":4761665,"identity":"feb6b769-0533-460e-a316-c9ff676f32dd","added_by":"auto","created_at":"2021-01-06 20:45:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":462486,"visible":true,"origin":"","legend":"Energy versus RMSD values of the conformations sampled by (1) Rosetta-SWM, (2) refinement of Rosetta-SWM conformations by BRiQ, (3) structure prediction by BRiQ with all native base pairs fixed for (A) CG-helix, (B) GCAA_tetra loop, (C) UUCG_tetra loop, and (D) j55a_P4P6_fixed. Structure alignment of native (blue) to the best in top 1% predicted models by Rosetta-SWM (panel 4) and BRiQ refinement (panel 5) in the bottom panel. ","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-136920/v1/006e6a5c9d361f0941a28610.png"},{"id":4761511,"identity":"f258858f-5f6c-4eb5-819c-2c337e41515c","added_by":"auto","created_at":"2021-01-06 20:39:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105584,"visible":true,"origin":"","legend":"BRiQ refinement achieves consistent improvement over the best-submitted models for RNA puzzles containing model structures with RMSD \u003c4Å (A), over all RNA puzzles in conformational sampling (B), over FARFAR2 models for 10 out of 12 RNA puzzles in top 1% (C) and top 5% (D) predicted models. ","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-136920/v1/3f45a970c78d08263ac403a5.png"},{"id":4761596,"identity":"b0d1320a-bebf-45af-88c5-d607696784e3","added_by":"auto","created_at":"2021-01-06 20:42:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":395622,"visible":true,"origin":"","legend":"Energy versus RMSD values of the conformations sampled by (1) FARFAR2, (2) refinement of FARFAR2 conformations by BRiQ for (A) RNA Puzzle 4 and (B) RNA Puzzle 18. Structure alignment of native (blue) to the best in top 1% predicted models by FARFAR2 (3) and BRiQ refinement (4) in the bottom panel. 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Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because native structures are often not the global minimum of existing approximate energy scores. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base-base, base-oxygen and oxygen-oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4,000 quantum-mechanical calculations were performed to reweight base-base statistical potentials for minimizing possible effects of indirect interactions. 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