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Exploring Fold Space Preferences of New-born and Ancient Protein Superfamilies

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
Exploring Fold Space Preferences of New-born and Ancient Protein Superfamilies
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003325
Pubmed ID
Authors

Hannah Edwards, Sanne Abeln, Charlotte M. Deane

Abstract

The evolution of proteins is one of the fundamental processes that has delivered the diversity and complexity of life we see around ourselves today. While we tend to define protein evolution in terms of sequence level mutations, insertions and deletions, it is hard to translate these processes to a more complete picture incorporating a polypeptide's structure and function. By considering how protein structures change over time we can gain an entirely new appreciation of their long-term evolutionary dynamics. In this work we seek to identify how populations of proteins at different stages of evolution explore their possible structure space. We use an annotation of superfamily age to this space and explore the relationship between these ages and a diverse set of properties pertaining to a superfamily's sequence, structure and function. We note several marked differences between the populations of newly evolved and ancient structures, such as in their length distributions, secondary structure content and tertiary packing arrangements. In particular, many of these differences suggest a less elaborate structure for newly evolved superfamilies when compared with their ancient counterparts. We show that the structural preferences we report are not a residual effect of a more fundamental relationship with function. Furthermore, we demonstrate the robustness of our results, using significant variation in the algorithm used to estimate the ages. We present these age estimates as a useful tool to analyse protein populations. In particularly, we apply this in a comparison of domains containing greek key or jelly roll motifs.

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Geographical breakdown

Country Count As %
Germany 2 3%
United Kingdom 2 3%
India 1 2%
Netherlands 1 2%
China 1 2%
United States 1 2%
Unknown 57 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 28%
Researcher 10 15%
Student > Master 7 11%
Professor 6 9%
Professor > Associate Professor 4 6%
Other 14 22%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 43%
Biochemistry, Genetics and Molecular Biology 18 28%
Chemistry 4 6%
Computer Science 3 5%
Physics and Astronomy 2 3%
Other 3 5%
Unknown 7 11%