Background
- RNA-binding protein which should be targeted by allosteric mechanism only to avoid unspecific off-target effects.
- Only a few known allosteric inhibitors exist.
- Two previously unknown allosteric binding pockets were identified by Receptor.AI's proprietary pocket detection AI model.
Methodology
- Virtual Screening was performed for 1.4 million compounds from stock collection.
- 1000 ranked candidate compounds were prioritized.
- 209 of them were selected for experimental validation.
Pocket 2 and Pocket 1 found by Receptor.AI
Results
- The experimental validation was performed using a high throughput p110 knockout cell-based assay.
- The criteria for a hit compound was established as a 5-fold increase in interferon induction compared to the control, with the desirable outcome being a 10-fold increase in interferon induction to surpass the efficacy of siRNA alternatives.
- 4 hit compounds with interferon inducing activities were identified.
- Hit compounds were validated by the dose-response analysis.
- Two of them exhibit comparable or superior maximal interferon induction with lower EC50 in comparison to a competing compound.
- This was achieved on a much 2.5 times smaller screening library (209 against 500 for competitors).
- Active scaffolds have been selected for further series expansion and optimization.
Dose-Response relationship for Compound 1, Compound 2 and Competitor Compound
Interferon induction observed at 25 uM for Receptor.AI hit compounds 1-4 obtained through virtual screening, a competitor small molecule, and ADAR1 siRNA
