Literature DB >> 26501955

Comprehensive characterization of the Published Kinase Inhibitor Set.

Jonathan M Elkins1, Vita Fedele1, Marta Szklarz1, Kamal R Abdul Azeez1, Eidarus Salah1, Jowita Mikolajczyk2, Sergei Romanov2, Nikolai Sepetov2, Xi-Ping Huang3, Bryan L Roth3, Ayman Al Haj Zen4, Denis Fourches5, Eugene Muratov5, Alex Tropsha5, Joel Morris6, Beverly A Teicher6, Mark Kunkel6, Eric Polley6, Karen E Lackey7, Francis L Atkinson8, John P Overington8, Paul Bamborough9, Susanne Müller1, Daniel J Price10, Timothy M Willson10, David H Drewry10, Stefan Knapp1,11,12, William J Zuercher10.   

Abstract

Despite the success of protein kinase inhibitors as approved therapeutics, drug discovery has focused on a small subset of kinase targets. Here we provide a thorough characterization of the Published Kinase Inhibitor Set (PKIS), a set of 367 small-molecule ATP-competitive kinase inhibitors that was recently made freely available with the aim of expanding research in this field and as an experiment in open-source target validation. We screen the set in activity assays with 224 recombinant kinases and 24 G protein-coupled receptors and in cellular assays of cancer cell proliferation and angiogenesis. We identify chemical starting points for designing new chemical probes of orphan kinases and illustrate the utility of these leads by developing a selective inhibitor for the previously untargeted kinases LOK and SLK. Our cellular screens reveal compounds that modulate cancer cell growth and angiogenesis in vitro. These reagents and associated data illustrate an efficient way forward to increasing understanding of the historically untargeted kinome.

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Year:  2015        PMID: 26501955     DOI: 10.1038/nbt.3374

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  54 in total

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Journal:  Nat Biotechnol       Date:  2011-10-30       Impact factor: 54.908

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Journal:  Bioorg Med Chem Lett       Date:  2006-12-24       Impact factor: 2.823

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Journal:  Nat Chem Biol       Date:  2014-03-02       Impact factor: 15.040

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9.  5-HT2C Receptor Structures Reveal the Structural Basis of GPCR Polypharmacology.

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10.  Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets.

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