PT  - JOURNAL ARTICLE
AU  - Thomas C. Terwilliger
AU  - Pavel V. Afonine
AU  - Dorothee Liebschner
AU  - Tristan I. Croll
AU  - Airlie J. McCoy
AU  - Robert D. Oeffner
AU  - Christopher J. Williams
AU  - Billy K. Poon
AU  - Jane S. Richardson
AU  - Randy J. Read
AU  - Paul D. Adams
TI  - Accelerating crystal structure determination with iterative AlphaFold prediction
AID  - 10.1101/2022.11.18.517112
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.11.18.517112
4099  - http://biorxiv.org/content/early/2022/11/18/2022.11.18.517112.short
4100  - http://biorxiv.org/content/early/2022/11/18/2022.11.18.517112.full
AB  - Experimental structure determination can be accelerated with AI-based structure prediction methods such as AlphaFold. Here we present an automatic procedure requiring only sequence information and crystallographic data 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 our procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. We applied this procedure to X-ray data for 215 structures released by the Protein Data Bank in a recent 6-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2Å. Predictions from our iterative template-guided prediction procedure were more accurate than those obtained without templates. We suggest a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization.Competing Interest StatementThe authors have declared no competing interest.