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Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM
With the advent of AlphaFold, protein structure prediction has attained remarkable accuracy. These achievements resulted from a focus on single static structures. The next frontier in this field involves enhancing our ability to model conformational ensembles, not just the ground states of proteins....
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327213/ https://www.ncbi.nlm.nih.gov/pubmed/37425870 http://dx.doi.org/10.1101/2023.06.28.546963 |
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author | Wankowicz, Stephanie A. Ravikumar, Ashraya Sharma, Shivani Riley, Blake T. Raju, Akshay Hogan, Daniel W. van den Bedem, Henry Keedy, Daniel A. Fraser, James S. |
author_facet | Wankowicz, Stephanie A. Ravikumar, Ashraya Sharma, Shivani Riley, Blake T. Raju, Akshay Hogan, Daniel W. van den Bedem, Henry Keedy, Daniel A. Fraser, James S. |
author_sort | Wankowicz, Stephanie A. |
collection | PubMed |
description | With the advent of AlphaFold, protein structure prediction has attained remarkable accuracy. These achievements resulted from a focus on single static structures. The next frontier in this field involves enhancing our ability to model conformational ensembles, not just the ground states of proteins. Notably, deposited structures result from interpretation of density maps, which are derived from either X-ray crystallography or cryogenic electron microscopy (cryo-EM). These maps represent ensemble averages, reflecting molecules in multiple conformations. Here, we present the latest developments in qFit, an automated computational approach to model protein conformational heterogeneity into density maps. We present algorithmic advancements to qFit, validated by improved R(free) and geometry metrics across a broad and diverse set of proteins. Automated multiconformer modeling holds significant promise for interpreting experimental structural biology data and for generating novel hypotheses linking macromolecular conformational dynamics to function. |
format | Online Article Text |
id | pubmed-10327213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-103272132023-07-08 Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM Wankowicz, Stephanie A. Ravikumar, Ashraya Sharma, Shivani Riley, Blake T. Raju, Akshay Hogan, Daniel W. van den Bedem, Henry Keedy, Daniel A. Fraser, James S. bioRxiv Article With the advent of AlphaFold, protein structure prediction has attained remarkable accuracy. These achievements resulted from a focus on single static structures. The next frontier in this field involves enhancing our ability to model conformational ensembles, not just the ground states of proteins. Notably, deposited structures result from interpretation of density maps, which are derived from either X-ray crystallography or cryogenic electron microscopy (cryo-EM). These maps represent ensemble averages, reflecting molecules in multiple conformations. Here, we present the latest developments in qFit, an automated computational approach to model protein conformational heterogeneity into density maps. We present algorithmic advancements to qFit, validated by improved R(free) and geometry metrics across a broad and diverse set of proteins. Automated multiconformer modeling holds significant promise for interpreting experimental structural biology data and for generating novel hypotheses linking macromolecular conformational dynamics to function. Cold Spring Harbor Laboratory 2023-06-29 /pmc/articles/PMC10327213/ /pubmed/37425870 http://dx.doi.org/10.1101/2023.06.28.546963 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Wankowicz, Stephanie A. Ravikumar, Ashraya Sharma, Shivani Riley, Blake T. Raju, Akshay Hogan, Daniel W. van den Bedem, Henry Keedy, Daniel A. Fraser, James S. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM |
title | Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM |
title_full | Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM |
title_fullStr | Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM |
title_full_unstemmed | Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM |
title_short | Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM |
title_sort | uncovering protein ensembles: automated multiconformer model building for x-ray crystallography and cryo-em |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327213/ https://www.ncbi.nlm.nih.gov/pubmed/37425870 http://dx.doi.org/10.1101/2023.06.28.546963 |
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