bioRxivBiophysicsPreprint
Accessing pore-blocker bound and alternative conductive conformations of TMEM16A using PIP2-assisted adaptive sampling
Abstract
Ion channels are promising targets for drug discovery due to their diverse physiological functions. The database of ion channel structures has grown exponentially over the last decade due to advances in structure-determination techniques. However, not all ion channel conformations have been determined, and not all druggable conformations can be modelled as thermodynamically stable under simulation conditions. This greatly limits conformation-specific drug targeting. In this study, we used an endogenous regulator of ion channels, phosphatidylinositol-4,5-bisphosphate (PIP2), as a computational tool to probe the conductive conformation of the TMEM16A calcium-activated chloride channel distinctive from the available structures. By using the PIP2-binding conformation from a coarse-grained model, followed by fluctuation-amplified specific traits (FAST) adaptive sampling in an all-atom configuration, the system transitioned from a closed state to a thermodynamically stable conductive state. The transition also highlights the importance of PIP2 in TM6 helical kink, the opening of the outer gate and the alpha helix on I551. The modelled structure also displays the experimental conductance at sub-300 mV. Using an accelerated weighted histogram (AWH), the binding sites of 1PBC, A9C, niclosamide, and Ani9 pore blockers were determined and validated against previous experimental studies. This paves the way to structure-specific drug development, as overactivation of TMEM16A is correlated with many diseases, such as pulmonary hypertension and ischemic stroke. Together, this study highlights the importance of lipids in stabilising ion channel conformations for targeted drug design and introduces a novel approach to expand the therapeutic targeting of ion channels.
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
- 0
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