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Evolutionary Optimization of Protein Folding

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
Evolutionary Optimization of Protein Folding
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002861
Pubmed ID
Authors

Cédric Debès, Minglei Wang, Gustavo Caetano-Anollés, Frauke Gräter

Abstract

Nature has shaped the make up of proteins since their appearance, [Formula: see text]3.8 billion years ago. However, the fundamental drivers of structural change responsible for the extraordinary diversity of proteins have yet to be elucidated. Here we explore if protein evolution affects folding speed. We estimated folding times for the present-day catalog of protein domains directly from their size-modified contact order. These values were mapped onto an evolutionary timeline of domain appearance derived from a phylogenomic analysis of protein domains in 989 fully-sequenced genomes. Our results show a clear overall increase of folding speed during evolution, with known ultra-fast downhill folders appearing rather late in the timeline. Remarkably, folding optimization depends on secondary structure. While alpha-folds showed a tendency to fold faster throughout evolution, beta-folds exhibited a trend of folding time increase during the last [Formula: see text]1.5 billion years that began during the "big bang" of domain combinations. As a consequence, these domain structures are on average slow folders today. Our results suggest that fast and efficient folding of domains shaped the universe of protein structure. This finding supports the hypothesis that optimization of the kinetic and thermodynamic accessibility of the native fold reduces protein aggregation propensities that hamper cellular functions.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Hungary 1 <1%
Germany 1 <1%
Korea, Republic of 1 <1%
India 1 <1%
United Kingdom 1 <1%
France 1 <1%
Peru 1 <1%
Canada 1 <1%
Other 2 2%
Unknown 102 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 32%
Researcher 24 21%
Student > Bachelor 12 10%
Student > Master 10 9%
Professor 7 6%
Other 17 15%
Unknown 9 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 50%
Biochemistry, Genetics and Molecular Biology 21 18%
Computer Science 7 6%
Chemistry 7 6%
Physics and Astronomy 4 3%
Other 7 6%
Unknown 12 10%