PESI - An intelligent system for Prediction of Enzyme - Substrate Interactions based on experimental constraints
German Nudelman, Tamar Tennenbaum, Ramit Mehr and Ron Unger*
Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.
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 PKCd and PKCh, which we have confirmed experimentally.
Key words: Protein-protein interactions, automated systems, data mining, similarity search, phosphorylation, kinases, biological pathways