Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex

Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex | 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 Biological Sciences - Article Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex Haifeng Chen, Junjie Zhu, Sören von Bülow, Hongyi Liu, Kresten Lindorff-Larsen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9036053/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Proteins execute cellular functions through a structural continuum ranging from stable, folded domains to highly dynamic intrinsically disordered regions (IDRs). Conformational ensembles represent the set of three-dimensional structures a protein adopts under a specific set of conditions, and underlie essential processes from catalysis to complex signaling networks. While deep learning has revolutionized structure prediction, capturing the distinct conformational diversity of folded and disordered regions—especially within multidomain proteins and large assemblies—remains a fundamental challenge. Here we introduce IDPFold2, a generative framework that models the heterogenous protein thermodynamics by integrating a Mixture-of-Experts architecture into the flow matching framework. By routing residues from different regions to specialized expert networks, IDPFold2 accurately predicts conformational ensembles for folded domains, IDRs and multidomain proteins. IDPFold2 outperforms state-of-the-art methods in capturing key functional states and fitting the experimental observations across local and global scales. Furthermore, we describe an extension of IDPFold2 to protein assemblies, deciphering the complex binding modes of IDRs within large macromolecular complexes, providing a generalizable tool for exploring the dynamic proteome. Biological sciences/Computational biology and bioinformatics/Protein structure predictions Biological sciences/Computational biology and bioinformatics/Machine learning Conformational ensembles Disordered proteins Multidomain proteins Protein thermodynamics Protein assemblies Full Text Additional Declarations There is NO Competing Interest. Supplementary Files IDPFold2SI260305initialsubmission.docx Supplementary File Cite Share Download PDF Status: Under Review 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. 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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-9036053","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":604021372,"identity":"d09f24fa-05ec-4d37-bef2-e005c8b9b989","order_by":0,"name":"Haifeng 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