Nucleic Acids Research Advance Access published online on April 19, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn161
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Computational Biology |
Prediction of phosphotyrosine signaling networks using a scoring matrix-assisted ligand identification approach
1Department of Biochemistry and the Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, 2Department of Computer Science, University of Western Ontario, London, Ontario N6A 5C1, Canada and 3School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore
*To whom correspondence should be addressed. Tel: +1 519 8502910; Fax: +1 519 6613175; Email: sli{at}uwo.ca
Received February 26, 2008. Revised March 19, 2008. Accepted March 20, 2008.
Systematic identification of binding partners for modular domains such as Src homology 2 (SH2) is important for understanding the biological function of the corresponding SH2 proteins. We have developed a worldwide web-accessible computer program dubbed SMALI for scoring matrix-assisted ligand identification for SH2 domains and other signaling modules. The current version of SMALI harbors 76 unique scoring matrices for SH2 domains derived from screening oriented peptide array libraries. These scoring matrices are used to search a protein database for short peptides preferred by an SH2 domain. An experimentally determined cut-off value is used to normalize an SMALI score, therefore allowing for direct comparison in peptide-binding potential for different SH2 domains. SMALI employs distinct scoring matrices from Scansite, a popular motif-scanning program. Moreover, SMALI contains built-in filters for phosphoproteins, Gene Ontology (GO) correlation and colocalization of subject and query proteins. Compared to Scansite, SMALI exhibited improved accuracy in identifying binding peptides for SH2 domains. Applying SMALI to a group of SH2 domains identified hundreds of interactions that overlap significantly with known networks mediated by the corresponding SH2 proteins, suggesting SMALI is a useful tool for facile identification of signaling networks mediated by modular domains that recognize short linear peptide motifs.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.