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AI-Based Discovery and CryoEM Structural Elucidation of a K ATP Channel Pharmacochaperone.

Assmaa ElSheikhCamden M DriggersHa H TruongZhongying YangJohn AllenNiel HenriksenKatarzyna Walczewska-SzewcShow-Ling Shyng
Published in: bioRxiv : the preprint server for biology (2024)
Pancreatic K ATP channel trafficking defects underlie congenital hyperinsulinism (CHI) cases unresponsive to the K ATP channel opener diazoxide, the mainstay medical therapy for CHI. Current clinically used K ATP channel inhibitors have been shown to act as pharmacochaperones and restore surface expression of trafficking mutants; however, their therapeutic utility for K ATP trafficking impaired CHI is hindered by high-affinity binding, which limits functional recovery of rescued channels. Recent structural studies of K ATP channels employing cryo-electron microscopy (cryoEM) have revealed a promiscuous pocket where several known K ATP pharmacochaperones bind. The structural knowledge provides a framework for discovering K ATP channel pharmacochaperones with desired reversible inhibitory effects to permit functional recovery of rescued channels. Using an AI-based virtual screening technology AtomNet® followed by functional validation, we identified a novel compound, termed Aekatperone, which exhibits chaperoning effects on K ATP channel trafficking mutations. Aekatperone reversibly inhibits K ATP channel activity with a half-maximal inhibitory concentration (IC 50 ) ~ 9 μM. Mutant channels rescued to the cell surface by Aekatperone showed functional recovery upon washout of the compound. CryoEM structure of K ATP bound to Aekatperone revealed distinct binding features compared to known high affinity inhibitor pharmacochaperones. Our findings unveil a K ATP pharmacochaperone enabling functional recovery of rescued channels as a promising therapeutic for CHI caused by K ATP trafficking defects.
Keyphrases
  • healthcare
  • machine learning
  • small molecule
  • electron microscopy
  • mass spectrometry
  • binding protein
  • high throughput
  • body composition
  • deep learning
  • resistance training