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Bioinformatics. 2009 Jan 1;25(1):14-21. doi: 10.1093/bioinformatics/btn569. Epub 2008 Nov 7.

Discovery of phosphorylation motif mixtures in phosphoproteomics data.

Author information

1
Department of Computer Science, Brown University, Toyota Technological Institute at Chicago, Chicago, IL, USA. aritz@cs.brown.edu

Abstract

MOTIVATION:

Modification of proteins via phosphorylation is a primary mechanism for signal transduction in cells. Phosphorylation sites on proteins are determined in part through particular patterns, or motifs, present in the amino acid sequence.

RESULTS:

We describe an algorithm that simultaneously discovers multiple motifs in a set of peptides that were phosphorylated by several different kinases. Such sets of peptides are routinely produced in proteomics experiments.Our motif-finding algorithm uses the principle of minimum description length to determine a mixture of sequence motifs that distinguish a foreground set of phosphopeptides from a background set of unphosphorylated peptides. We show that our algorithm outperforms existing motif-finding algorithms on synthetic datasets consisting of mixtures of known phosphorylation sites. We also derive a motif specificity score that quantifies whether or not the phosphoproteins containing an instance of a motif have a significant number of known interactions. Application of our motif-finding algorithm to recently published human and mouse proteomic studies recovers several known phosphorylation motifs and reveals a number of novel motifs that are enriched for interactions with a particular kinase or phosphatase. Our tools provide a new approach for uncovering the sequence specificities of uncharacterized kinases or phosphatases.

PMID:
18996944
PMCID:
PMC2638929
DOI:
10.1093/bioinformatics/btn569
[Indexed for MEDLINE]
Free PMC Article
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