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A motif-based profile scanning approach for genome-wide prediction of signaling pathways

Abstract

The rapid increase in genomic information requires new techniques to infer protein function and predict protein–protein interactions. Bioinformatics identifies modular signaling domains within protein sequences with a high degree of accuracy. In contrast, little success has been achieved in predicting short linear sequence motifs within proteins targeted by these domains to form complex signaling networks. Here we describe a peptide library-based searching algorithm, accessible over the World Wide Web, that identifies sequence motifs likely to bind to specific protein domains such as 14-3-3, SH2, and SH3 domains, or likely to be phosphorylated by specific protein kinases such as Src and AKT. Predictions from database searches for proteins containing motifs matching two different domains in a common signaling pathway provides a much higher success rate. This technology facilitates prediction of cell signaling networks within proteomes, and could aid in the identification of drug targets for the treatment of human diseases.

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Figure 1: Sample output from the Scansite program.
Figure 2: Statistical distribution of motif scores.
Figure 3: Coupling of intramolecular signaling modules increases the selectivity for tyrosine kinases.
Figure 4: Amino acid distribution frequencies surrounding serine, threonine, and tyrosine residues in proteomes and within mapped phosphorylation sites.

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Acknowledgements

We thank members of the Division of Signal Transduction for beta-testing the software. This work was supported by National Institutes of Health grants HL03601 (M.B.Y.), GM59281 (M.B.Y.), GM56203 (L.C.C.), a Burroughs-Wellcome Career Development Award (M.B.Y.), a Merck Genome Research Institute Award (L.C.C./M.B.Y.), a Human Frontier Long Term Fellowship (S.V.), and Telethon 60% and 40% grants (S.V.).

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Correspondence to Michael B. Yaffe.

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Yaffe, M., Leparc, G., Lai, J. et al. A motif-based profile scanning approach for genome-wide prediction of signaling pathways. Nat Biotechnol 19, 348–353 (2001). https://doi.org/10.1038/86737

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