Co-folding, the Future of Docking – Prediction of Allosteric and Orthosteric Ligands

Co-folding, the Future of Docking – Prediction of Allosteric and Orthosteric Ligands | 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 Co-folding, the Future of Docking – Prediction of Allosteric and Orthosteric Ligands Eva Nittinger, Özge Yoluk, Alessandro Tibo, Gustav Olanders, Christian Tyrchan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6526650/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 In drug discovery understanding protein structures is essential for comprehending their functions and interactions with drugs. Traditional methods like X-ray crystallography and cryo-electron microscopy have been used to solve these structures. Recently, computational biology has seen a breakthrough with deep learning algorithms capable of predicting protein structures based on amino acid sequences. These methods have now evolved into predicting protein-ligand interactions from sequence – co-folding methods. Despite the great advancement in the field during the last year, there are still open challenges. Here, we focus on the prediction of allosteric binding sites, using a dataset of 17 orthosteric/allosteric ligand sets. Three different co-folding methods – NeuralPLexer, RoseTTAFold All-Atom and Boltz-1 – were used to predict both allosteric and orthosteric ligands. Using PoseBusters, the ligand quality was checked, with about 90% of ligands predicted by Boltz-1 passing the default quality criteria. NeuralPLexer and RoseTTAFold All-Atom still showing high quality drawbacks. The orthosteric ligands were well placed. However, instead of the allosteric pocket these deep learning approaches generally favor the orthosteric site, which is the one most represented in the training data. Biological sciences/Drug discovery/Medicinal chemistry/Structure-based drug design Biological sciences/Computational biology and bioinformatics/Protein structure predictions Full Text Additional Declarations There is NO Competing Interest. Supplementary Files TargetLigandCoFoldingSI.pdf Supporting Information 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. 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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-6526650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451190341,"identity":"f0b864cf-90ba-49dc-a143-19bb164ff00b","order_by":0,"name":"Eva 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