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.
Original language | English (US) |
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Pages (from-to) | 14-21 |
Number of pages | 8 |
Journal | Bioinformatics |
Volume | 25 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2009 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Computational Mathematics
- Molecular Biology
- Biochemistry
- Statistics and Probability
- Computer Science Applications
- Computational Theory and Mathematics