2019年4月
Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network.
PLoS Computational Biology
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- 巻
- 15
- 号
- 4
- 開始ページ
- e1006878
- 終了ページ
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1371/journal.pcbi.1006878
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.
- リンク情報
- ID情報
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- DOI : 10.1371/journal.pcbi.1006878
- PubMed ID : 31026276
- PubMed Central 記事ID : PMC6506148