This site needs JavaScript to work properly. Please enable it to take advantage of the complete set of features!
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log in
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Oct 1;29(19):2387-94.
doi: 10.1093/bioinformatics/btt419. Epub 2013 Jul 31.

Kinannote, a computer program to identify and classify members of the eukaryotic protein kinase superfamily

Affiliations

Kinannote, a computer program to identify and classify members of the eukaryotic protein kinase superfamily

Jonathan M Goldberg et al. Bioinformatics. .

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.

PubMed Disclaimer

Figures

Fig. 1.
Fig. 1.
Algorithm used by Kinannote to produce a draft kinome from a predicted proteome. Computational steps are numbered and sequence sets are indicated by letters. The aggregate of sets comprising the draft kinome are indicated by the shaded area. Unclassified kinases may be species-specific or members of novel families; twilight hits are recorded but are not part of the draft kinome. ePKs, eukaryotic protein kinase superfamily members; aPKs, atypical protein kinases
Fig. 2.
Fig. 2.
Receiver-operator characteristic (ROC) curves for identification of protein kinases in reference kinomes by Kinannote. The cutoff score (HMM score for kinase identification, Table 1) of results from a search of the comprehensive predicted gene set against a protein kinase hidden Markov model (Fig. 1.1, cutoff applied at Fig. 1.5) was varied from stringent (left side of range) to permissive (right side of range). Blue, S.pombe; orange, A.queenslandica; green, C. G.lamblia; purple, P.falciparum. The upper left corner of the plot is expanded in the inset. Points representing the optimum setting of −66 are circled. TPR, true positive rate; FDR, false-discovery rate
Fig. 3.
Fig. 3.
ROC analysis to identify optimum settings for classification by Kinannote and compare classification methods. Circles: effect on classification of varying the number of consistent consecutive top BLAST hits of candidate kinases against the reference database. The BLAST search is described in Figure 1.3, and the criterion is applied at Figure 1.7. The number of consistent hits required for classification ranged from 5 (left side of range) to 1 (right side of range). Partial classification is indicated by half-filled symbols, and full classification is indicated by filled symbols. The points obtained at three consistent consecutive hits are indicated with boxes. This setting was chosen as the default value for Kinannote because it provides a good compromise between sensitivity and precision. Diamonds: effect on classification of varying the E-value cutoff from the BLAST search (Fig. 1.6) from 1 ×ばつ 10−100 (left side of range) to 1 (right side of range). As aforementioned, partial and full classification are indicated by half-filled and filled symbols, respectively. The test genomes are A, S.pombe; B, A.queenslandica; C, G.lamblia; and D, P.falciparum. TPR, true-positive rate; FDR, false-discovery rate
Fig. 4.
Fig. 4.
Performance of Kinannote and Kinomer HMMs on curated kinomes. Kinannote identification, partial classification and full classification results are shown in the second, third, and fourth columns, respectively. Kinomer identification and group-level classification are shown in the fifth and sixth columns, respectively. Curated kinase counts are shown in the first columns. For Kinannote and Kinomer, the columns are divided into TP, FN, FP categories; for the curated kinomes, the columns are divided into conserved, species-specific and unclassified categories. The colors associated with these categories are indicated in the key below the figure. The curated kinomes are from A, S.pombe; B, A.queenslandica; C, G.lamblia; and D, P.falciparum

References

    1. Altschul SF, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402. - PMC - PubMed
    1. Anamika K, et al. Analysis of the protein kinome of Entamoeba histolytica. Proteins. 2008;71:995–1006. - PubMed
    1. Artz JD, et al. The Cryptosporidium parvum kinome. BMC genomics. 2011;12:478. - PMC - PubMed
    1. Baldauf SL. The deep roots of eukaryotes. Science. 2003;300:1703–1706. - PubMed
    1. Beakes GW, et al. The evolutionary phylogeny of the oomycete "fungi". Protoplasma. 2012;249:3–19. - PubMed

Publication types

Full text links
Silverchair Information Systems full text link Silverchair Information Systems Free PMC article
Cite

AltStyle によって変換されたページ (->オリジナル) /