Modeling Alternative Conformational States in CASP16

Modeling Alternative Conformational States in CASP16 | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL PROTEINS: Structure, Function, and Bioinformatics This is a preprint and has not been peer reviewed. Data may be preliminary. 4 September 2025 V1 Latest version Share on Modeling Alternative Conformational States in CASP16 Authors : Namita Dube , Theresa A. Ramelot 0000-0002-0335-1573 , Tiburon L. Benavides , Yuanpeng Janet Huang 0000-0002-3374-786X , John Moult 0000-0002-3012-2282 , Andriy Kryshtafovych 0000-0001-5066-7178 , and Gaetano Montelione 0000-0002-9440-3059 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175700739.98292576/v1 Published Proteins: Structure, Function, and Bioinformatics Version of record Peer review timeline 263 views 244 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain–linker–domain (D–L–D) targets assessed against statistical models from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein–DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score >0.75). However, with the exception of one protein–ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality based selection. While the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein–DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score <0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains a critical frontier for structural biology. Supplementary Material File (casp16_ensembles-nosi.vfinal.docx) Download 12.89 MB Information & Authors Information Version history V1 Version 1 04 September 2025 Peer review timeline Published Proteins: Structure, Function, and Bioinformatics Version of Record 28 Oct 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection PROTEINS: Structure, Function, and Bioinformatics Keywords alphafold2 casp conformational dynamics deep learning multi-state modeling prediction nucleic acids protein structure prediction Authors Affiliations Namita Dube Rensselaer Polytechnic Institute Department of Chemistry and Chemical Biology View all articles by this author Theresa A. Ramelot 0000-0002-0335-1573 Rensselaer Polytechnic Institute Department of Chemistry and Chemical Biology View all articles by this author Tiburon L. Benavides Rensselaer Polytechnic Institute Department of Chemistry and Chemical Biology View all articles by this author Yuanpeng Janet Huang 0000-0002-3374-786X Rensselaer Polytechnic Institute Department of Chemistry and Chemical Biology View all articles by this author John Moult 0000-0002-3012-2282 Institute for Bioscience & Biotechnology Research View all articles by this author Andriy Kryshtafovych 0000-0001-5066-7178 University of California Davis Genome Center View all articles by this author Gaetano Montelione 0000-0002-9440-3059 [email protected] Rensselaer Polytechnic Institute Department of Chemistry and Chemical Biology View all articles by this author Metrics & Citations Metrics Article Usage 263 views 244 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Namita Dube, Theresa A. Ramelot, Tiburon L. Benavides, et al. Modeling Alternative Conformational States in CASP16. Authorea . 04 September 2025. DOI: https://doi.org/10.22541/au.175700739.98292576/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Rachael C. Kretsch, Elisa Posani, Eugene F. Baulin, Janusz M. Bujnicki, Giovanni Bussi, Thomas E. Cheatham, Shi‐Jie Chen, Arne Elofsson, Masoud Amiri Farsani, Olivia N. Fisher, M. Michael Gromiha, Ayush Gupta, Michiaki Hamada, K. Harini, Gang Hu, David Huang, Junichi Iwakiri, Anika Jain, Yuki Kagaya, Daisuke Kihara, Sebastian Kmiecik, Sowmya Ramaswamy Krishnan, Ikuo Kurisaki, Olivier Languin‐Cattoën, Jun Li, Shanshan Li, Karim Malekzadeh, Tsukasa Nakamura, Wentao Ni, Chandran Nithin, Michael Z. Palo, Joon Hong Park, Smita P. Pilla, Simón Poblete, Fabrizio Pucci, Pranav Punuru, Anouka Saha, Kengo Sato, Ambuj Srivastava, Genki Terashi, Emilia Tugolukova, Jacob Verburgt, Qiqige Wuyun, Gül H. Zerze, Kaiming Zhang, Sicheng Zhang, Wei Zheng, Yuanzhe Zhou, Wah Chiu, David A. Case, Rhiju Das, Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16 , Proteins: Structure, Function, and Bioinformatics, 94 , 1, (381-402), (2025). https://doi.org/10.1002/prot.70079 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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