Abstract
The use of machine learning to identify biological targets for natural products with anticancer properties and unknown modes of action is gaining momentum. We used machine intelligence to deconvolute the phenotypic effects of Piperlongumine (PL) and establish a link to allosteric modulation of the human transient receptor potential vanilloid 2 (hTRPV2) channel. The structure of the PL-bound full-length rat TRPV2 channel was determined by cryo-EM. PL binds to an allosteric pocket responsible for a new mode of anticancer activity against glioblastoma (GBM) in which hTRPV2 is overexpressed. Down-regulation of hTRPV2 reduces sensitivity to PL and decreases ROS production. Analysis of GBM patient samples associates hTRPV2 overexpression with tumor grade, disease progression and poor prognosis. We obtained tumor remission in a murine model of orthotopic GBM by formulating PL for sustained local therapy. Our strategy is broadly applicable and leverages data-motivated research hypotheses for the discovery of new biology and therapeutics.
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