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

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 predicti...

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Bibliographic Details
Published in:Nature biotechnology 2001-04, Vol.19 (4), p.348-353
Main Authors: Yaffe, Michael B, Leparc, German G, Lai, Jack, Obata, Toshiyuki, Volinia, Stefano, Cantley, Lewis C
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Amino Acid Motifs
Amino Acid Sequence
Amino acids
Animals
Binding sites
Bioinformatics
Biological and medical sciences
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Cattle
Cell physiology
Databases, Factual
Fundamental and applied biological sciences. Psychology
Genome
Genomes
Humans
Internet
Kinases
Life Sciences
Mice
Molecular and cellular biology
Molecular Sequence Data
Peptides
Phosphorylation
Proteins
Publishing
Rats
Serine - chemistry
Signal Transduction
Software
Threonine - chemistry
Tyrosine - chemistry
World Wide Web
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Summary: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.
ISSN:1087-0156
1546-1696
DOI:10.1038/86737