Back

Mechanism of dual pharmacological correction and potentiation of human CFTR

Wang, C.; Yang, Z.; Loughlin, B. J.; Xu, H.; Veit, G.; Vorobiev, S.; Clarke, O. B.; Jiang, F.; Li, Y.; Singh, S.; Rich, Z.; Menten, E. R.; Grassucci, R. A.; Wang, W.; Mezzell, A.; Fu, Z.; Wong, K.-H.; Wang, J.; Wetmore, D. R.; Sutton, R. B.; Brouillette, C. G.; Urbatsch, I. L.; Kappes, J. C.; Lukacs, G. L.; Frank, J.; Hunt, J. F.

2022-10-11 biophysics
10.1101/2022.10.10.510913 bioRxiv
Show abstract

Cystic fibrosis (CF) is caused by mutations in a chloride channel called the human Cystic Fibrosis Transmembrane Conductance Regulator (hCFTR). We used cryo-EM global conformational ensemble reconstruction to characterize the mechanism by which the breakthrough drug VX445 (Elexacaftor) simultaneously corrects both protein-folding and channel-gating defects caused by CF mutations. VX445 drives hCFTR molecules harboring the gating-defective G551D mutation towards the open-channel conformation by binding to a site in the first transmembrane domain. This binding interaction reverses the usual pathway of allosteric structural communication by which ATP binding activates channel conductance, which is blocked by the G551D mutation. Our ensemble reconstructions include a 3.4 [A] non-native structure demonstrating that detachment of the first nucleotide-binding domain of hCFTR is directly coupled to local unfolding of the VX445 binding site. Reversal of this unfolding transition likely contributes to its corrector activity by cooperatively stabilizing NBD1 and the transmembrane domains of hCFTR during biogenesis. SummaryCryo-EM global conformational ensemble reconstruction has been used to characterize the mechanism-of-action of a breakthrough pharmaceutical that corrects fatal protein-folding and channel-gating defects in the human cystic fibrosis transmembrane conductance regulator (CFTR).

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.