# Improved AlphaFold modeling with implicit experimental information

Article · Terwilliger TC, Poon BK, Afonine PV, Schlicksup CJ, Croll TI, Millán C, Richardson JS, Read RJ, Adams PD

- Archive id: doi:10.1038/s41592-022-01645-6
- Published: 2022-10-20
- Venue: Nature methods
- DOI: https://doi.org/10.1038/s41592-022-01645-6
- PMID: 36266465
- PMCID: PMC9636017
- Citations: 127
- Source URL: https://doi.org/10.1038/s41592-022-01645-6
- PDF: https://europepmc.org/articles/PMC9636017/pdf
- Provenance: europepmc

## 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.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [AI Protein Design & Structure Prediction](https://space-kitty.com/topics/protein-design)

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