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MMFPh: a maximal motif finder for phosphoproteomics datasets
Author(s) -
Tuobin Wang,
Arminja N. Kettenbach,
Scott A. Gerber,
Chris BaileyKellogg
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts195
Subject(s) - phosphoproteomics , phosphorylation , motif (music) , kinase , proteome , computational biology , biology , amino acid , sequence motif , protein phosphorylation , biochemistry , bioinformatics , protein kinase a , gene , physics , acoustics
Protein phosphorylation, driven by specific recognition of substrates by kinases and phosphatases, plays central roles in a variety of important cellular processes such as signaling and enzyme activation. Mass spectrometry enables the determination of phosphorylated peptides (and thereby proteins) in scenarios ranging from targeted in vitro studies to in vivo cell lysates under particular conditions. The characterization of commonalities among identified phosphopeptides provides insights into the specificities of the kinases involved in a study. Several algorithms have been developed to uncover linear motifs representing position-specific amino acid patterns in sets of phosphopeptides. To more fully capture the available information, reduce sensitivity to both parameter choices and natural experimental variation, and develop more precise characterizations of kinase specificities, it is necessary to determine all statistically significant motifs represented in a dataset.

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