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Collective Dynamics Differentiates Functional Divergence in Protein Evolution
Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynam...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315450/ https://www.ncbi.nlm.nih.gov/pubmed/22479170 http://dx.doi.org/10.1371/journal.pcbi.1002428 |
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author | Glembo, Tyler J. Farrell, Daniel W. Gerek, Z. Nevin Thorpe, M. F. Ozkan, S. Banu |
author_facet | Glembo, Tyler J. Farrell, Daniel W. Gerek, Z. Nevin Thorpe, M. F. Ozkan, S. Banu |
author_sort | Glembo, Tyler J. |
collection | PubMed |
description | Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ∼2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function. |
format | Online Article Text |
id | pubmed-3315450 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33154502012-04-04 Collective Dynamics Differentiates Functional Divergence in Protein Evolution Glembo, Tyler J. Farrell, Daniel W. Gerek, Z. Nevin Thorpe, M. F. Ozkan, S. Banu PLoS Comput Biol Research Article Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ∼2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function. Public Library of Science 2012-03-29 /pmc/articles/PMC3315450/ /pubmed/22479170 http://dx.doi.org/10.1371/journal.pcbi.1002428 Text en This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Glembo, Tyler J. Farrell, Daniel W. Gerek, Z. Nevin Thorpe, M. F. Ozkan, S. Banu Collective Dynamics Differentiates Functional Divergence in Protein Evolution |
title | Collective Dynamics Differentiates Functional Divergence in Protein Evolution |
title_full | Collective Dynamics Differentiates Functional Divergence in Protein Evolution |
title_fullStr | Collective Dynamics Differentiates Functional Divergence in Protein Evolution |
title_full_unstemmed | Collective Dynamics Differentiates Functional Divergence in Protein Evolution |
title_short | Collective Dynamics Differentiates Functional Divergence in Protein Evolution |
title_sort | collective dynamics differentiates functional divergence in protein evolution |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315450/ https://www.ncbi.nlm.nih.gov/pubmed/22479170 http://dx.doi.org/10.1371/journal.pcbi.1002428 |
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