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Bioinformatics Advance Access published online on March 4, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn084
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Predicting Proteolytic Sites in Extracellular Proteins: Only Halfway There

Yossef Kliger 1,*,# , Eyal Gofer 1,# , Assaf Wool 1, Amir Toporik 1, Avihay Apatoff 1,2 and Moshe Olshansky 1

1Compugen Ltd, 72 Pinchas Rosen, Tel Aviv 69512, Israel.
2 The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.

*To whom correspondence should be addressed. Dr. Yossef Kliger, E-mail: kliger{at}compugen.co.il


  Abstract

Motivation: Many secretory proteins are synthesized as inactive precursors that must undergo post-translational proteolysis in order to mature and become active. In the current study, we address the challenge of sequence-based discovery of proteolytic sites in secreted proteins using machine learning.

Results: The results revealed that only half of the extra-cellular proteolytic sites are currently annotated, leaving over 3600 unannotated ones. Furthermore, we have found that only 6% of the unannotated sites are similar to known proteolytic sites, whereas the remaining 94% do not share significant similarity with any annotated proteolytic site. The computational challenges in these two cases are very different. While the precision in detecting the former group is close to perfect, only a mere 22% of the latter group were detected with a precision of 80%. The applicability of the classifier is demonstrated through members of the FGF family, in which we verified the conservation of physiologically-relevant proteolytic sites in homologous proteins.

Contact: kliger{at}compugen.co.il

Associate Editor: Prof. John Quackenbush

# Equally contributed to this paper.

Received on December 13, 2008; revised on February 10, 2008; accepted on March 1, 2008

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