Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network.

Autor: Ung PMU; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America., Sonoshita M; Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America., Scopton AP; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America., Dar AC; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America., Cagan RL; Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America., Schlessinger A; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2019 Apr 26; Vol. 15 (4), pp. e1006878. Date of Electronic Publication: 2019 Apr 26 (Print Publication: 2019).
DOI: 10.1371/journal.pcbi.1006878
Abstrakt: Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) characterized by a transformation network activated by oncogenic dRetM955T. Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRetM955T-induced transformation. We then combined features from our hits with those of known inhibitors to create a 'hybrid' molecule with improved suppression of dRetM955T transformation. Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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