Browsing by Author "Terwilliger, Thomas C"

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    Accelerating crystal structure determination with iterative AlphaFold prediction.
    (Acta crystallographica. Section D, Structural biology, 2023-03) Terwilliger, Thomas C; Afonine, Pavel V; Liebschner, Dorothee; Croll, Tristan I; McCoy, Airlie J; Oeffner, Robert D; Williams, Christopher J; Poon, Billy K; Richardson, Jane S; Read, Randy J; Adams, Paul D
    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.
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    Improved AlphaFold modeling with implicit experimental information.
    (Nature methods, 2022-11) Terwilliger, Thomas C; Poon, Billy K; Afonine, Pavel V; Schlicksup, Christopher J; Croll, Tristan I; Millán, Claudia; Richardson, Jane S; Read, Randy J; Adams, Paul D
    Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.