TY - JOUR T1 - Improved AlphaFold modeling with implicit experimental information JF - bioRxiv DO - 10.1101/2022.01.07.475350 SP - 2022.01.07.475350 AU - Thomas C. Terwilliger AU - Billy K. Poon AU - Pavel V. Afonine AU - Christopher J. Schlicksup AU - Tristan I. Croll AU - Claudia Millán AU - Jane. S. Richardson AU - Randy J. Read AU - Paul D. Adams Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/01/30/2022.01.07.475350.abstract N2 - Machine learning prediction algorithms such as AlphaFold1 and RoseTTAFold2 can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy3–6. We hypothesized that by implicitly including experimental information, 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 based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find 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 crystallographic and electron cryo-microscopy map interpretation.Competing Interest StatementThe authors have declared no competing interest. ER -