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Comparable contributions of structural-functional constraints and expression level to the rate of protein sequence evolution

Overview of attention for article published in Biology Direct, October 2008
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Title
Comparable contributions of structural-functional constraints and expression level to the rate of protein sequence evolution
Published in
Biology Direct, October 2008
DOI 10.1186/1745-6150-3-40
Pubmed ID
Authors

Maxim Y Wolf, Yuri I Wolf, Eugene V Koonin

Abstract

Proteins show a broad range of evolutionary rates. Understanding the factors that are responsible for the characteristic rate of evolution of a given protein arguably is one of the major goals of evolutionary biology. A long-standing general assumption used to be that the evolution rate is, primarily, determined by the specific functional constraints that affect the given protein. These constrains were traditionally thought to depend both on the specific features of the protein's structure and its biological role. The advent of systems biology brought about new types of data, such as expression level and protein-protein interactions, and unexpectedly, a variety of correlations between protein evolution rate and these variables have been observed. The strongest connections by far were repeatedly seen between protein sequence evolution rate and the expression level of the respective gene. It has been hypothesized that this link is due to the selection for the robustness of the protein structure to mistranslation-induced misfolding that is particularly important for highly expressed proteins and is the dominant determinant of the sequence evolution rate.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 2 3%
United Kingdom 2 3%
United States 2 3%
Spain 2 3%
Brazil 1 2%
Sweden 1 2%
Taiwan 1 2%
Netherlands 1 2%
Switzerland 1 2%
Other 1 2%
Unknown 51 78%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 31%
Researcher 17 26%
Professor > Associate Professor 7 11%
Professor 5 8%
Student > Master 5 8%
Other 5 8%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 68%
Biochemistry, Genetics and Molecular Biology 5 8%
Chemical Engineering 2 3%
Computer Science 2 3%
Chemistry 2 3%
Other 2 3%
Unknown 8 12%