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Accelerating crystal structure determination with iterative AlphaFold prediction

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallo­graphic data is presented that uses AlphaFold predictions to produce an elect...

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Autores principales: Terwilliger, Thomas C., Afonine, Pavel V., Liebschner, Dorothee, Croll, Tristan I., McCoy, Airlie J., Oeffner, Robert D., Williams, Christopher J., Poon, Billy K., Richardson, Jane S., Read, Randy J., Adams, Paul D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986801/
https://www.ncbi.nlm.nih.gov/pubmed/36876433
http://dx.doi.org/10.1107/S205979832300102X
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author Terwilliger, Thomas C.
Afonine, Pavel V.
Liebschner, Dorothee
Croll, Tristan I.
McCoy, Airlie J.
Oeffner, Robert D.
Williams, Christopher J.
Poon, Billy K.
Richardson, Jane S.
Read, Randy J.
Adams, Paul D.
author_facet Terwilliger, Thomas C.
Afonine, Pavel V.
Liebschner, Dorothee
Croll, Tristan I.
McCoy, Airlie J.
Oeffner, Robert D.
Williams, Christopher J.
Poon, Billy K.
Richardson, Jane S.
Read, Randy J.
Adams, Paul D.
author_sort Terwilliger, Thomas C.
collection PubMed
description Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallo­graphic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C(α) atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.
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spelling pubmed-99868012023-03-07 Accelerating crystal structure determination with iterative AlphaFold prediction Terwilliger, Thomas C. Afonine, Pavel V. Liebschner, Dorothee Croll, Tristan I. McCoy, Airlie J. Oeffner, Robert D. Williams, Christopher J. Poon, Billy K. Richardson, Jane S. Read, Randy J. Adams, Paul D. Acta Crystallogr D Struct Biol Research Papers Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallo­graphic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C(α) atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested. International Union of Crystallography 2023-02-27 /pmc/articles/PMC9986801/ /pubmed/36876433 http://dx.doi.org/10.1107/S205979832300102X Text en © Thomas C. Terwilliger et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Research Papers
Terwilliger, Thomas C.
Afonine, Pavel V.
Liebschner, Dorothee
Croll, Tristan I.
McCoy, Airlie J.
Oeffner, Robert D.
Williams, Christopher J.
Poon, Billy K.
Richardson, Jane S.
Read, Randy J.
Adams, Paul D.
Accelerating crystal structure determination with iterative AlphaFold prediction
title Accelerating crystal structure determination with iterative AlphaFold prediction
title_full Accelerating crystal structure determination with iterative AlphaFold prediction
title_fullStr Accelerating crystal structure determination with iterative AlphaFold prediction
title_full_unstemmed Accelerating crystal structure determination with iterative AlphaFold prediction
title_short Accelerating crystal structure determination with iterative AlphaFold prediction
title_sort accelerating crystal structure determination with iterative alphafold prediction
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986801/
https://www.ncbi.nlm.nih.gov/pubmed/36876433
http://dx.doi.org/10.1107/S205979832300102X
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