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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
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% |