Structural Rewiring of IL-7R Dimerization by an Oncogenic Transmembrane Mutation Can Be Reversed by Rational Design
Wang, Q.; Chen, M.; Lasram, A.; Vihuri, S.; Chou, A. Z.; Bian, W.; Dai, Z.; Haapanen, O.; Enkavi, G.; Pollmann, C.; Vattulainen, I.; Cai, T.; Piehler, J.; Chou, J. J.
Show abstract
Mutations within the transmembrane domains (TMDs) of single-pass transmembrane receptors often cause aberrant, ligand-independent receptor signaling associated with diverse malignancies, but their mechanism of action remain largely unknown. These TMD mutations are generally not targetable as they are buried in membrane. Here, we determined the mechanism of a gain-of-function (GOF) TMD mutation of interleukin-7 receptor (IL-7R) associated with T-cell acute lymphoblastic leukemia, and addressed the possibility of directly targeting the TMD mutation by using rationally designed transmembrane helices to restore order to uncontrolled signaling. We find that the GOF mutation of IL-7R severely shifts the TMD homodimerization interface, causing the receptor to homodimerize in a geometry that activates downstream signaling independent of ligand. Designed transmembrane helices that interfere with the new interface, delivered with mRNA technology, selectively block ligand-independent but not ligand-dependent signaling. Our study provides a conceptual framework for understanding and repairing disease-causing TMD mutations of single-pass cytokine receptors.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Structural mechanism of calcium-mediated hormone recognition and Gβ interaction by the human melanocortin-1 receptor 96%
- Molecular basis of ligand recognition and activation of human V2 vasopressin receptor 96%
- Cryo-EM structures of human SID-1 transmembrane family proteins and implications for their low-pH-dependent RNA transport activity 96%
"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.