Details:

Alternative Title:
PLoS One
Publisher's site:
http://dx.doi.org/10.1371/journal.pone.0014444
Personal Author:
Lieber, Daniel S. ; Elemento, Olivier ; Tavazoie, Saeed
Lieber, Daniel S. ; Elemento, Olivier ; Tavazoie, Saeed Less -
Description:
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.
Subject:
[+]
Algorithms
Amino Acid Motifs
Biotechnology
Computational Biology
Databases, Protein
Eukaryota
Humans
Mitochondria
Phosphorylation
Protein Processing, Post-Translational
Proteins
Protein Structure, Tertiary
Proteome
Proteomics
Research Article
Saccharomyces Cerevisiae
Schizosaccharomyces

Source:
PLoS One. 5(12).
Pubmed ID:
21206902
Pubmed Central ID:
PMC3012054
Document Type:
Journal Article
Funding:
1DP10D003787-01/DP/NCCDPHP CDC HHS/United States ; 2R01HG003219/HG/NHGRI NIH HHS/United States ; P50 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 ; P50 GM071508/GM/NIGMS NIH HHS/United States ; R01 HG003219/HG/NHGRI NIH HHS/United States Less -
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:eed9872afa47e76a832c44f4629f3b319e72d140c9fa32566e176602b79a5d69
File Type:

Supporting Files

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