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Improved AlphaFold modeling with implicit experimental information.

Publication ,  Journal Article
Terwilliger, TC; Poon, BK; Afonine, PV; Schlicksup, CJ; Croll, TI; Millán, C; Richardson, JS; Read, RJ; Adams, PD
Published in: Nat Methods
November 2022

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.

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Published In

Nat Methods

DOI

EISSN

1548-7105

Publication Date

November 2022

Volume

19

Issue

11

Start / End Page

1376 / 1382

Location

United States

Related Subject Headings

  • Proteins
  • Protein Conformation
  • Models, Molecular
  • Machine Learning
  • Developmental Biology
  • Cryoelectron Microscopy
  • Algorithms
  • 31 Biological sciences
  • 11 Medical and Health Sciences
  • 10 Technology
 

Citation

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Terwilliger, T. C., Poon, B. K., Afonine, P. V., Schlicksup, C. J., Croll, T. I., Millán, C., … Adams, P. D. (2022). Improved AlphaFold modeling with implicit experimental information. Nat Methods, 19(11), 1376–1382. https://doi.org/10.1038/s41592-022-01645-6
Terwilliger, Thomas C., Billy K. Poon, Pavel V. Afonine, Christopher J. Schlicksup, Tristan I. Croll, Claudia Millán, Jane S. Richardson, Randy J. Read, and Paul D. Adams. “Improved AlphaFold modeling with implicit experimental information.Nat Methods 19, no. 11 (November 2022): 1376–82. https://doi.org/10.1038/s41592-022-01645-6.
Terwilliger TC, Poon BK, Afonine PV, Schlicksup CJ, Croll TI, Millán C, et al. Improved AlphaFold modeling with implicit experimental information. Nat Methods. 2022 Nov;19(11):1376–82.
Terwilliger, Thomas C., et al. “Improved AlphaFold modeling with implicit experimental information.Nat Methods, vol. 19, no. 11, Nov. 2022, pp. 1376–82. Pubmed, doi:10.1038/s41592-022-01645-6.
Terwilliger TC, Poon BK, Afonine PV, Schlicksup CJ, Croll TI, Millán C, Richardson JS, Read RJ, Adams PD. Improved AlphaFold modeling with implicit experimental information. Nat Methods. 2022 Nov;19(11):1376–1382.

Published In

Nat Methods

DOI

EISSN

1548-7105

Publication Date

November 2022

Volume

19

Issue

11

Start / End Page

1376 / 1382

Location

United States

Related Subject Headings

  • Proteins
  • Protein Conformation
  • Models, Molecular
  • Machine Learning
  • Developmental Biology
  • Cryoelectron Microscopy
  • Algorithms
  • 31 Biological sciences
  • 11 Medical and Health Sciences
  • 10 Technology