Affiliations: Faculty of Life Sciences, Bar-Ilan University,
Ramat-Gan 52900, Israel Faculty of Life Sciences, Bar-Ilan University,
Ramat-Gan 52900, Israel Faculty of Life Sciences, Bar-Ilan University,
Ramat-Gan 52900, Israel
Note: [] Corresponding author: Ron Unger, Faculty of Life Sciences,
Bar-Ilan University, Ramat-Gan 52900, Israel, Tel.: +972 3 531 8124, Fax: +972
3 535 1824, E-mail: ron@biocom1.ls.biu.ac.il.
Abstract: We present a system for predicting protein-protein modifications,
and demonstrate its usefulness in the field of signal transduction research.
Signal transduction is one of the most important areas of investigation in
biological research. One of the major mechanisms frequently employed by cells
to regulate signal transduction processes involves protein phosphorylation by
various kinases. As many as 1000 protein kinases and 500 protein phosphatases
in the human genome are thought to be involved in phosphorylation processes
which regulate all aspects of cell function. The complexity of such
interactions stems from the enormous number of factors and interactions, which
makes the identification of putative substrates for any given enzyme by
straightforward experimentation increasingly difficult. We present here a data
mining algorithm, based on the similarity between the modifier proteins and
between the modified proteins, and on experimental constraints. The application
presented here (PESI) focuses on substrate phosphorylation by various enzymes.
This algorithm reduces the number of substrate candidates for experimental
study by about two orders of magnitude. Moreover, this algorithm has already
yielded predictions for previously unknown substrates of the enzymes PKC ä
and PKC ç, which we have confirmed experimentally.
