Article

Schwartz, D. & Gygi, S.P. An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat. Biotechnol. 23, 1391-1398

Department of Cell Biology, 240 Longwood Ave., Harvard Medical School, Boston, Massachusetts 02115, USA.
Nature Biotechnology (Impact Factor: 41.51). 12/2005; 23(11):1391-8. DOI: 10.1038/nbt1146
Source: PubMed

ABSTRACT

With the recent exponential increase in protein phosphorylation sites identified by mass spectrometry, a unique opportunity has arisen to understand the motifs surrounding such sites. Here we present an algorithm designed to extract motifs from large data sets of naturally occurring phosphorylation sites. The methodology relies on the intrinsic alignment of phospho-residues and the extraction of motifs through iterative comparison to a dynamic statistical background. Results show the identification of dozens of novel and known phosphorylation motifs from recently published serine, threonine and tyrosine phosphorylation studies. When applied to a linguistic data set to test the versatility of the approach, the algorithm successfully extracted hundreds of language motifs. This method, in addition to shedding light on the consensus sequences of identified and as yet unidentified kinases and modular protein domains, may also eventually be used as a tool to determine potential phosphorylation sites in proteins of interest.

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    • "For motif analysis, the 10 amino acid residues (-10 to +10) on either side of a modification site were selected, and a consensus logo was generated using the iceLogo webserver [53] . We also analyzed the same sets of data using the motif-X webserver [54] to confirm the sequence preferences (S4Fig). All information for the protein annotations, the positions of acyl modifications, and their surrounding sequences are shown in S2 Table. "
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    • "To assess a potential preference for certain kinase motifs in the five phosphopeptides data sets, we subjected the hereidentified phosphorylation sites first to the motif-x algorithm (Schwartz and Gygi, 2005). A total of 132 distinct motifs could be defined among all the five proteases data sets (Table S3). "
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    • "Thus, the two studies provide different but complementary rapamycin-regulated phosphoproteomes. To determine if TORC1 controls phosphorylation of specific motifs, we analyzed the sequences surrounding the rapamycin-regulated phosphosites, with the Motif-X algorithm (Schwartz and Gygi, 2005). For the peptides "
    Dataset: 3475-3

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