Membrane potential regulates the activity of voltage-dependent ion channels via specialized voltage-sensing modules but the mechanisms involved in coupling voltage-sensor movement to pore opening remain unclear due to lack of resting state structures and robust methods to identify allosteric pathways. Here, using a newly developed interaction energy analysis, we probe the interfaces of the voltage-sensing and pore modules in the drosophila Shaker K+ channel. Our measurements reveal unexpectedly strong equilibrium gating interactions between contacts at the S4 and S5 helices in addition to those between S6 and S4–S5 linker. Network analysis of MD trajectories shows that the voltage-sensor and pore motions are linked by two distinct pathways- canonical one through the S4–S5 linker and a hitherto unknown pathway akin to rack and pinion coupling involving S4 and S5 helices. Our findings highlight the central role of the S5 helix in electromechanical transduction in the VGIC superfamily.
The expansion of computational power, better parameterization of force fields, and the development of novel algorithms to enhance the sampling of the free energy landscapes of proteins have allowed molecular dynamics (MD) simulations to become an indispensable tool to understand the function of biomolecules. The temporal and spatial resolution of MD simulations allows for the study of a vast number of processes of interest. Here, we review the computational efforts to uncover the conformational free energy landscapes of a subset of membrane proteins: ion channels, transporters and G-protein coupled receptors. We focus on the various enhanced sampling techniques used to study these questions, how the conclusions come together to build a coherent picture, and the relationship between simulation outcomes and experimental observables.
A free energy landscape estimation method based on the well-known Gaussian mixture model (GMM) is used to compare the efficiencies of thermally enhanced sampling methods with respect to regular molecular dynamics. The simulations are carried out on two binding states of calmodulin, and the free energy estimation method is compared with other estimators using a toy model. We show that GMM with cross-validation provides a robust estimate that is not subject to overfitting. The continuous nature of Gaussians provides better estimates on sparse data than canonical histogramming. We find that diffusion properties determine the sampling method effectiveness, such that diffusion-dominated apo calmodulin is most efficiently sampled by regular molecular dynamics, while holo calmodulin, with its rugged free energy landscape, is better sampled by enhanced sampling methods.
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