Abstract
Motivation: Kinases of the eukaryotic protein kinase superfamily are key regulators of most aspects eukaryotic cellular behavior and have provided several drug targets including kinases dysregulated in cancers. The rapid increase in the number of genomic sequences has created an acute need to identify and classify members of this important class of enzymes efficiently and accurately.Results: Kinannote produces a draft kinome and comparative analyses for a predicted proteome using a single line command, and it is currently the only tool that automatically classifies protein kinases using the controlled vocabulary of Hanks and Hunter [Hanks and Hunter (1995)]. A hidden Markov model in combination with a position-specific scoring matrix is used by Kinannote to identify kinases, which are subsequently classified using a BLAST comparison with a local version of KinBase, the curated protein kinase dataset from www.kinase.com. Kinannote was tested on the predicted proteomes from four divergent species. The average sensitivity and precision for kinome retrieval from the test species are 94.4 and 96.8%. The ability of Kinannote to classify identified kinases was also evaluated, and the average sensitivity and precision for full classification of conserved kinases are 71.5 and 82.5%, respectively. Kinannote has had a significant impact on eukaryotic genome annotation, providing protein kinase annotations for 36 genomes made public by the Broad Institute in the period spanning 2009 to the present.Availability: Kinannote is freely available at http://sourceforge.net/projects/kinannote.Contact: jmgold@broadinstitute.orgSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Protein kinases are well-studied enzymes involved in the regulation of the majority of eukaryotic cellular processes
We show that Kinannote performs well on four reference kinomes broadly representative of the eukaryotic tree-of-life (Amphimedon queenslandica, Schizosaccharomyces pombe, Plasmodium falciparum and Giardia lamblia) and describe its use to provide kinome annotations for 36 eukaryotic genomes released by The Broad Institute from 2009 through the time of publication
In non-metazoans, kinases for which the best BLAST hits against the reference database are TK group members are classified as tyrosine kinase-like (TKL) and flagged in the draft kinome table produced by Kinannote
Summary
Protein kinases are well-studied enzymes involved in the regulation of the majority of eukaryotic cellular processes. Current identification methods favor searches against protein kinase hidden Markov models (HMMs) from Pfam (Punta et al, 2012) or Kinomer (Martin et al, 2009), and searches against position-specific scoring matrices (PSSMs) from the Conserved Domain Database (Marchler-Bauer et al, 2013). These methods effectively identify average kinases but are often unable to identify novel or divergent superfamily members; classification based on Pfam and Kinomer HMMs does not exceed the group level. We present Kinannote, an ePK identification and classification package that leverages a protein kinase HMM and similarity with known kinases to produce a high-quality draft kinome for a given gene set with a single command.
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