UB-MBX-46 is a potent and selective antagonist of the human P2X7 receptor developed by structure-based drug design
Oken, A. C.; Turcu, A. L.; Tzortzini, E.; Georgiou, K.; Nagel, J.; Barniol-Xicota, M.; Kim, G.-R.; Lee, S.-D.; Nicke, A.; Kim, Y.-C.; Müller, C. E.; Kolocouris, A.; Vazquez, S.; Mansoor, S. E.
Show abstract
The P2X7 receptor is an ATP-gated ion channel that activates inflammatory pathways involved in diseases such as cancer, atherosclerosis, and neurodegeneration. However, despite the potential benefits of blocking overactive signaling, no P2X7 receptor antagonists have been approved for clinical use. Interspecies variation among existing antagonists has proven challenging, in part due to the dearth of molecular information on different receptor orthologs. Here, to identify distinct molecular features in the human receptor, we determine high-resolution cryo-EM structures of the full-length wild-type human P2X7 receptor in apo closed and ATP-bound open state conformations and draw comparisons with new and existing structures of other orthologs. We also report a cryo-EM structure of the human receptor in complex with an adamantane-based inhibitor, which we leverage, in conjunction with functional data and molecular dynamics simulations, to design a potent and selective antagonist with a unique polycyclic scaffold. Functional and structural analysis reveal how this optimized ligand, UB-MBX-46, interacts with the classical allosteric pocket of the human P2X7 receptor with picomolar potency and high selectivity, revealing its significant therapeutic potential.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Structure and mechanism of oxalate transporter OxlT in an oxalate-degrading bacterium in the gut microbiota 98%
- Structure of a lasso peptide bound ETB receptor provides insights into the mechanism of GPCR inverse agonism 98%
- Crystal structure of steroid reductase SRD5A reveals conserved steroid reduction mechanism 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.