Abstract
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 Statement
The authors have declared no competing interest.
Footnotes
The paper is updated to list the most similar entry in the PDB at time of deposition of the 25 structures analyzed and to include a version of Fig. 3E using a cutoff of 2 A as suggested in an on-line review of the manuscript. Additionally another example of major improvement in a structure is shown in Fig. 1 panels G-I.
https://phenix-online.org/phenix_data/terwilliger/alphafold_with_density_2022/