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Cleavage Entropy as Quantitative Measure of Protease Specificity

Overview of attention for article published in PLoS Computational Biology, April 2013
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Title
Cleavage Entropy as Quantitative Measure of Protease Specificity
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003007
Pubmed ID
Authors

Julian E. Fuchs, Susanne von Grafenstein, Roland G. Huber, Michael A. Margreiter, Gudrun M. Spitzer, Hannes G. Wallnoefer, Klaus R. Liedl

Abstract

A purely information theory-guided approach to quantitatively characterize protease specificity is established. We calculate an entropy value for each protease subpocket based on sequences of cleaved substrates extracted from the MEROPS database. We compare our results with known subpocket specificity profiles for individual proteases and protease groups (e.g. serine proteases, metallo proteases) and reflect them quantitatively. Summation of subpocket-wise cleavage entropy contributions yields a measure for overall protease substrate specificity. This total cleavage entropy allows ranking of different proteases with respect to their specificity, separating unspecific digestive enzymes showing high total cleavage entropy from specific proteases involved in signaling cascades. The development of a quantitative cleavage entropy score allows an unbiased comparison of subpocket-wise and overall protease specificity. Thus, it enables assessment of relative importance of physicochemical and structural descriptors in protease recognition. We present an exemplary application of cleavage entropy in tracing substrate specificity in protease evolution. This highlights the wide range of substrate promiscuity within homologue proteases and hence the heavy impact of a limited number of mutations on individual substrate specificity.

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The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Australia 1 2%
Brazil 1 2%
Unknown 60 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 24%
Researcher 9 14%
Student > Bachelor 7 11%
Student > Master 7 11%
Student > Doctoral Student 5 8%
Other 11 17%
Unknown 9 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 30%
Chemistry 13 21%
Biochemistry, Genetics and Molecular Biology 12 19%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Computer Science 2 3%
Other 4 6%
Unknown 11 17%