{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Terwilliger TC"],"funding":["Phenix Industrial Consortium","National Institutes of Health, National Institute of General Medical Sciences","Lawrence Berkeley National Laboratory","Wellcome Trust","NIGMS NIH HHS"],"pagination":["234-244"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9986801"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["79(Pt 3)"],"pubmed_abstract":["Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C<sup>α</sup> atoms matching those in the deposited models within 2 Å. Predictions from th"],"journal":["Acta crystallographica. Section D, Structural biology"],"pubmed_title":["Accelerating crystal structure determination with iterative AlphaFold prediction."],"pmcid":["PMC9986801"],"funding_grant_id":["DE-AC02-05CH11231","GM063210","P01 GM063210","R35 GM131883","209407/Z/17/Z"],"pubmed_authors":["Poon BK","Terwilliger TC","Richardson JS","Adams PD","Croll TI","Oeffner RD","Williams CJ","Afonine PV","McCoy AJ","Liebschner D","Read RJ"],"additional_accession":[]},"is_claimable":false,"name":"Accelerating crystal structure determination with iterative AlphaFold prediction.","description":"Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C<sup>α</sup> atoms matching those in the deposited models within 2 Å. Predictions from th","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2026-05-29T05:30:23.147Z","creation":"2025-02-18T23:44:33.824Z"},"accession":"S-EPMC9986801","cross_references":{"pubmed":["36876433"],"doi":["10.1107/S205979832300102X"]}}