{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T21:40:25Z","timestamp":1789508425429,"version":"build-2803163510"},"posted":{"date-parts":[[2025,10,7]]},"group-title":"Biophysics","reference-count":51,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["R01GM083960"],"award-info":[{"award-number":["R01GM083960"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["U19AI171110"],"award-info":[{"award-number":["U19AI171110"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R35GM145238"],"award-info":[{"award-number":["R35GM145238"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,10,7]]},"abstract":"<jats:label>1<\/jats:label>\n                <jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  An atomic structure model of a protein can be computed from a diffraction pattern of its crystal. While most crystallographic studies produce a single set of atomic coordinates, the billions of protein molecules in a crystal sample many conformational modes during data collection. As a result, a \u201cmulti-state\u201d model that depicts these conformations could reproduce the X-ray data better than a single conformation, and thus likely be more accurate. Computing such a multistate model is challenging due to a lower data-to-parameter ratio than that for single-state modeling. To address this challenge, additional information could be considered, such as X-ray datasets collected for the same system under distinct experimental conditions (\n                  <jats:italic>eg<\/jats:italic>\n                  , temperature, ligands, mutations, and pressure). Here, we develop, benchmark, and illustrate MultiXray: Bayesian multi-state multi-condition modeling for X-ray crystallography. The input information is several X-ray datasets collected under distinct conditions and a molecular mechanics force field. The model consists of an independent coordinate set for each of several states and the weight of each state under each condition. A Bayesian posterior model density quantifies the match of the model with all X-ray datasets and the force field. A sample of models is drawn from the posterior model density using biased molecular dynamics (MD) simulations. We benchmark MultiXray on simulated CypA X-ray data. Using a second X-ray dataset improves the\n                  <jats:italic>R<\/jats:italic>\n                  <jats:sub>free<\/jats:sub>\n                  from 0.105 to 0.089. We then demonstrate MultiXray on experimental temperature-dependent data for SARS-CoV-2 Mpro. Using multiple X-ray datasets improves\n                  <jats:italic>R<\/jats:italic>\n                  <jats:sub>free<\/jats:sub>\n                  of the PDB-deposited structure from 0.253 to 0.237. MultiXray is implemented in our open-source\n                  <jats:italic>Integrative Modeling Platform<\/jats:italic>\n                  (IMP) software, relying on integration with\n                  <jats:italic>Phenix<\/jats:italic>\n                  , thus making it easily applicable to many studies.\n                <\/jats:p>","DOI":"10.1101\/2025.10.07.680994","type":"posted-content","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T02:15:19Z","timestamp":1759889719000},"source":"Crossref","is-referenced-by-count":3,"title":["Bayesian multi-state multi-condition modeling of a protein structure based on X-ray crystallography data"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9127-4485","authenticated-orcid":false,"given":"Matthew","family":"Hancock","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Holton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5080-2859","authenticated-orcid":false,"given":"James S.","family":"Fraser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul D.","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0435-6197","authenticated-orcid":false,"given":"Andrej","family":"Sali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"issue":"2","key":"2025101105500604000_2025.10.07.680994v1.1","first-page":"167","article-title":"Protein conformational substates from Xray crystallography","volume":"66","year":"1996","journal-title":"en. 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Commun"},{"issue":"2","key":"2025101105500604000_2025.10.07.680994v1.9","first-page":"227","article-title":"\u201cEffect of anisotropy and anharmonicity on protein crystallographic refinement","volume":"190","year":"1986","journal-title":"An evaluation by molecular dynamics\u201d. en. In: J. Mol. Biol"},{"issue":"3","key":"2025101105500604000_2025.10.07.680994v1.10","first-page":"1626","article-title":"Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability","volume":"119","year":"2019","journal-title":"en. In: Chem. Rev"},{"issue":"18","key":"2025101105500604000_2025.10.07.680994v1.11","first-page":"4046","article-title":"The R-factor gap in macromolecular crystallography: an untapped potential for insights on accurate structures","volume":"281","year":"2014","journal-title":"en. 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In: IUCrJ"},{"key":"2025101105500604000_2025.10.07.680994v1.48","doi-asserted-by":"crossref","unstructured":"Thomas R M Barends et al. \u201cSerial femtosecond crystallography\u201d. en. In: Nat Rev Methods Primers 2 (Aug. 2022).","DOI":"10.1038\/s43586-022-00141-7"},{"key":"2025101105500604000_2025.10.07.680994v1.49","first-page":"209","article-title":"Discovery of allosteric binding sites by crystallographic fragment screening","volume":"65","year":"2020","journal-title":"en. In: Curr. Opin. Struct. Biol"},{"issue":"15","key":"2025101105500604000_2025.10.07.680994v1.50","first-page":"4069","article-title":"Measuring and modeling diffuse scattering in protein X-ray crystallography","volume":"113","year":"2016","journal-title":"en. In: Proc. Natl. Acad. Sci. U. S. A"},{"key":"2025101105500604000_2025.10.07.680994v1.51","first-page":"1","article-title":"Introduction to diffuse scattering and data collection","volume":"688","year":"2023","journal-title":"en. In: Methods Enzymol"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2025.10.07.680994","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T09:45:46Z","timestamp":1768470346000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2025.10.07.680994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1101\/2025.10.07.680994","relation":{},"subject":[],"published":{"date-parts":[[2025,10,7]]},"subtype":"preprint"}}