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Improving AlphaFold modeling using implicit information from experimental density maps

View ORCID ProfileThomas C. Terwilliger, View ORCID ProfileBilly K. Poon, View ORCID ProfilePavel V. Afonine, Christopher J. Schlicksup, View ORCID ProfileTristan I. Croll, View ORCID ProfileClaudia Millán, View ORCID ProfileJane. S. Richardson, View ORCID ProfileRandy J. Read, View ORCID ProfilePaul D. Adams
doi: https://doi.org/10.1101/2022.01.07.475350
Thomas C. Terwilliger
1New Mexico Consortium, Los Alamos, NM 87544, USA
2Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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  • For correspondence: tterwilliger@newmexicoconsortium.org
Billy K. Poon
3Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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  • ORCID record for Billy K. Poon
Pavel V. Afonine
3Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Christopher J. Schlicksup
3Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Tristan I. Croll
5Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
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  • ORCID record for Tristan I. Croll
Claudia Millán
5Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
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  • ORCID record for Claudia Millán
Jane. S. Richardson
6Department of Biochemistry, Duke University, Durham, North Carolina, 27710
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Randy J. Read
5Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
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Paul D. Adams
3Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
5Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
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Abstract

Machine learning prediction algorithms such as AlphaFold 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 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.

One-Sentence Summary AlphaFold modeling can be improved synergistically by including information from experimental density maps.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://phenix-online.org/phenix_data/terwilliger/alphafold_with_density_2022/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 07, 2022.
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Improving AlphaFold modeling using implicit information from experimental density maps
Thomas C. Terwilliger, Billy K. Poon, Pavel V. Afonine, Christopher J. Schlicksup, Tristan I. Croll, Claudia Millán, Jane. S. Richardson, Randy J. Read, Paul D. Adams
bioRxiv 2022.01.07.475350; doi: https://doi.org/10.1101/2022.01.07.475350
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Improving AlphaFold modeling using implicit information from experimental density maps
Thomas C. Terwilliger, Billy K. Poon, Pavel V. Afonine, Christopher J. Schlicksup, Tristan I. Croll, Claudia Millán, Jane. S. Richardson, Randy J. Read, Paul D. Adams
bioRxiv 2022.01.07.475350; doi: https://doi.org/10.1101/2022.01.07.475350

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