Modulatory mechanisms of TARP γ8-selective AMPA receptor therapeutics
Zhang, D.; Lape, R.; Shaikh, S. A.; Kohegyi, B. K.; Watson, J. F.; Cais, O.; Nakagawa, T.; Greger, I. H.
Show abstract
AMPA glutamate receptors (AMPARs) mediate excitatory neurotransmission throughout the brain. Their signalling is uniquely diversified by brain region-specific auxiliary subunits, providing an opportunity for the development of selective therapeutics. AMPARs associated with TARP {gamma}8 are enriched in the hippocampus, and are targets of emerging anti-epileptic drugs. To understand their therapeutic activity, we determined cryo-EM structures of the GluA1/2-{gamma}8 receptor associated with three potent, chemically diverse drugs. We find that despite sharing a lipid-exposed and water-accessible binding pocket, drug action is differentially affected by binding-site mutants. Together with patch-clamp recordings and MD simulations we demonstrate that ligand-triggered reorganisation of the AMPAR-TARP interface contributes to modulation. Unexpectedly, one ligand (JNJ-61432059) acts bifunctionally, negatively affecting GluA1 but exerting positive modulatory action on GluA2-containing AMPARs, in a TARP stoichiometry-dependent manner. These results further illuminate the action of TARPs, demonstrate the sensitive balance between positive and negative modulatory action, and provide a mechanistic platform for development of both positive and negative selective AMPAR modulators.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Structure and dynamics of differential ligand binding in the human {rho}-type GABA(A) receptor 96%
- Transcriptional control of nucleus accumbens neuronal excitability by Retinoid X Receptor Alpha tunes sensitivity to drug rewards 94%
- Transient developmental increase of prefrontal activity alters network maturation and causes cognitive dysfunction in adult mice 94%
"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.