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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....

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
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.
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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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