An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets.
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.
Schwartz
Daniel
D
Department of Cell Biology, 240 Longwood Ave., Harvard Medical School, Boston, Massachusetts 02115, USA. dschwartz@hms.harvard.edu
Gygi
Steven P
SP
D000465:Algorithms; D020816:Amino Acid Motifs; D001709:Biotechnology / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D020407:Internet; D013058:Mass Spectrometry; D015233:Models, Statistical; D010449:Peptide Mapping; D010766:Phosphorylation; D011381:Programming Languages; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
D011506:Proteins