Literature DB >> 15970266

Kinomics: characterizing the therapeutically validated kinase space.

Michal Vieth1, Jeffrey J Sutherland, Daniel H Robertson, Robert M Campbell.   

Abstract

The annotation and visualization of medicinally relevant kinase space revealed that kinase inhibitors in the clinic are, on average, of higher molecular weight and more lipophilic than all other clinically investigated drugs. Tyrosine kinases from the vascular endothelial growth factor and epidermal growth factor receptor families are the most pursued targets. Furthermore, oncological indications account for 75% of all kinase-related clinical interest. In addition, analysis of the similarity between kinase targets with respect to sequence, selectivity and structure has revealed that kinases with > or =60% sequence identity are most likely to be inhibited by the same classes of compounds and have similar ATP-binding sites. The identification of this threshold, together with the widely accepted representation of the sequence-based kinase space, is expanding our understanding of the clinical and structural space of the kinome.

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Year:  2005        PMID: 15970266     DOI: 10.1016/S1359-6446(05)03477-X

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  34 in total

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2.  Computational Modeling of Kinase Inhibitor Selectivity.

Authors:  Govindan Subramanian; Manish Sud
Journal:  ACS Med Chem Lett       Date:  2010-07-28       Impact factor: 4.345

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4.  Enzyme microarrays assembled by acoustic dispensing technology.

Authors:  E Y Wong; S L Diamond
Journal:  Anal Biochem       Date:  2008-06-20       Impact factor: 3.365

5.  Measuring and interpreting the selectivity of protein kinase inhibitors.

Authors:  Lynette A Smyth; Ian Collins
Journal:  J Chem Biol       Date:  2009-06-06

6.  The challenge of selecting protein kinase assays for lead discovery optimization.

Authors:  Haiching Ma; Sean Deacon; Kurumi Horiuchi
Journal:  Expert Opin Drug Discov       Date:  2008-06       Impact factor: 6.098

7.  Navigating the kinome.

Authors:  James T Metz; Eric F Johnson; Niru B Soni; Philip J Merta; Lemma Kifle; Philip J Hajduk
Journal:  Nat Chem Biol       Date:  2011-02-20       Impact factor: 15.040

8.  The (un)targeted cancer kinome.

Authors:  Oleg Fedorov; Susanne Müller; Stefan Knapp
Journal:  Nat Chem Biol       Date:  2010-03       Impact factor: 15.040

9.  Kinome-wide activity modeling from diverse public high-quality data sets.

Authors:  Stephan C Schürer; Steven M Muskal
Journal:  J Chem Inf Model       Date:  2013-01-09       Impact factor: 4.956

10.  Identification of a kinase profile that predicts chromosome damage induced by small molecule kinase inhibitors.

Authors:  Andrew J Olaharski; Nina Gonzaludo; Hans Bitter; David Goldstein; Stephan Kirchner; Hirdesh Uppal; Kyle Kolaja
Journal:  PLoS Comput Biol       Date:  2009-07-24       Impact factor: 4.475

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