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Collective Dynamics Differentiates Functional Divergence in Protein Evolution

Overview of attention for article published in PLoS Computational Biology, March 2012
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Title
Collective Dynamics Differentiates Functional Divergence in Protein Evolution
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002428
Pubmed ID
Authors

Tyler J. Glembo, Daniel W. Farrell, Z. Nevin Gerek, M. F. Thorpe, S. Banu Ozkan

Abstract

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.

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Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 7%
France 3 3%
Canada 2 2%
Korea, Republic of 1 <1%
Italy 1 <1%
Australia 1 <1%
Portugal 1 <1%
Norway 1 <1%
United Kingdom 1 <1%
Other 2 2%
Unknown 93 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 27%
Student > Ph. D. Student 29 25%
Professor 11 10%
Student > Postgraduate 8 7%
Student > Bachelor 7 6%
Other 21 18%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 45%
Biochemistry, Genetics and Molecular Biology 26 23%
Physics and Astronomy 9 8%
Chemistry 6 5%
Engineering 4 4%
Other 10 9%
Unknown 8 7%