Year: 2022
Type: article
Abstract: 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.
Source: Nature Methods
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $11,390
Awards: G1017065088, G1071216263, G1079818582, Phenix: Integrated algorithms for high quality models from crystallographic and cryo-EM data, G1143651532, G1279347439, G1286236842, G286898171, G2900453181, G3083819904, G310521049, G3700687868, G382641700, G42916606, Development of likelihood-based methods in structural biology, G5657628048, G5713387433, G6410514744, G6467669079, G7354628648, G75892134, G8437677540, G969889393
OpenAlex ID: W4306888844