Format

Send to

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2012 Apr 1;28(7):962-9. doi: 10.1093/bioinformatics/bts060. Epub 2012 Feb 1.

Predicting kinase substrates using conservation of local motif density.

Author information

1
Department of Cell and Systems Biology, University of Toronto, Toronto, Canada M5S 3G5.

Abstract

MOTIVATION:

Protein kinases represent critical links in cell signaling. A central problem in computational biology is to systematically identify their substrates.

RESULTS:

This study introduces a new method to predict kinase substrates by extracting evolutionary information from multiple sequence alignments in a manner that is tolerant to degenerate motif positioning. Given a known consensus, the new method (ConDens) compares the observed density of matches to a null model of evolution and does not require labeled training data. We confirmed that ConDens has improved performance compared with several existing methods in the field. Further, we show that it is generalizable and can predict interesting substrates for several important eukaryotic kinases where training data is not available.

AVAILABILITY AND IMPLEMENTATION:

ConDens can be found at http://www.moseslab.csb.utoronto.ca/andyl/.

CONTACT:

alan.moses@utoronto.ca

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
22302575
DOI:
10.1093/bioinformatics/bts060
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems
    Loading ...
    Support Center