Large-scale discovery and characterization of protein regulatory motifs in eukaryotes.

TitleLarge-scale discovery and characterization of protein regulatory motifs in eukaryotes.
Publication TypeJournal Article
Year of Publication2010
AuthorsLieber DS, Elemento O, Tavazoie S
JournalPLoS One
Date Published2010 Dec 29
KeywordsAlgorithms, Amino Acid Motifs, Computational Biology, Databases, Protein, Eukaryota, Humans, Mitochondria, Phosphorylation, Protein Processing, Post-Translational, Protein Structure, Tertiary, Proteins, Proteome, Proteomics, Saccharomyces cerevisiae, Schizosaccharomyces

The increasing ability to generate large-scale, quantitative proteomic data has brought with it the challenge of analyzing such data to discover the sequence elements that underlie systems-level protein behavior. Here we show that short, linear protein motifs can be efficiently recovered from proteome-scale datasets such as sub-cellular localization, molecular function, half-life, and protein abundance data using an information theoretic approach. Using this approach, we have identified many known protein motifs, such as phosphorylation sites and localization signals, and discovered a large number of candidate elements. We estimate that ~80% of these are novel predictions in that they do not match a known motif in both sequence and biological context, suggesting that post-translational regulation of protein behavior is still largely unexplored. These predicted motifs, many of which display preferential association with specific biological pathways and non-random positioning in the linear protein sequence, provide focused hypotheses for experimental validation.

Alternate JournalPLoS ONE
PubMed ID21206902
PubMed Central IDPMC3012054
Grant ListP50 GM071508 / GM / NIGMS NIH HHS / United States
R01 HG003219 / HG / NHGRI NIH HHS / United States
1DP10D003787-01 / DP / NCCDPHP CDC HHS / United States
2R01HG003219 / HG / NHGRI NIH HHS / United States