Accelerating crystal structure determination with iterative AlphaFold prediction.
Repository Usage Stats
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 Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.
Published Version (Please cite this version)
Terwilliger, Thomas C, Pavel V Afonine, Dorothee Liebschner, Tristan I Croll, Airlie J McCoy, Robert D Oeffner, Christopher J Williams, Billy K Poon, et al. (2023). Accelerating crystal structure determination with iterative AlphaFold prediction. Acta crystallographica. Section D, Structural biology, 79(Pt 3). pp. 234–244. 10.1107/s205979832300102x Retrieved from https://hdl.handle.net/10161/29456.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
3D structure of macromolecules; molecular graphics; protein folding and design; all-atom contacts; x-ray crystallography; structure validation.
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.