bioRxivBiophysicsPreprint
TRPP Ion Channels Function in the π-Conformation Rather Than the α-Conformation
Abstract
PKD2 (TRPP1) is a TRPP ion channel whose dysfunction causes autosomal dominant polycystic kidney disease (ADPKD). Understanding its conformational states and gating mechanism is important for elucidating its function and for structure-based drug discovery. Cryo-EM structures of WT-PKD2 contain a local {pi}-helical turn in the S6 pore-lining helix ({pi}-conformation). In contrast, a fully -helical S6 (-conformation) was observed in the gain-of-function mutant F604P-PKD2 and interpreted as the open state, leading to the proposed {pi}-to- activation mechanism. However, whether the -conformation can support function has never been directly established. Here, using molecular dynamics simulations, we investigate the functional properties of the -and {pi}-conformations of PKD2. The -conformation remains dewetted unless the lower gate is widened beyond the conformational range observed in mammalian TRP channels. By contrast, the {pi}-conformation supports pore hydration and ion conduction following a small opening of the L677 gate, with simulated conductance comparable to experimental measurements. The closely related PKD2L1 (TRPP2) channel shows a similar dependence on conformation, with a hydrated {pi}-conformation and a dewetted -conformation. Together, these findings challenge the assignment of the -conformation as the conductive state and support a model in which the {pi}-conformation represents the functionally relevant state of TRPP channels. This model suggests that activation may involve an -to-{pi} transition rather than the previously proposed {pi}-to- transition, providing a new framework for understanding TRPP channel gating. This work sheds light on the molecular basis of polycystin channel dysfunction and could facilitate the development of PKD2-targeted therapeutic strategies for ADPKD.
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- research focus: protein structure prediction
- approach: mechanistic / dynamical model
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