Benndorf, K. | bioRxiv Science

bioRxiv2020

Pathways of inter- and intrasubunit allosteric signaling in the C-linker disk and cyclic nucleotide-binding domain of HCN2 channels

Opening of hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels is controlled by membrane hyperpolarization and binding of cyclic nucleotides to the tetrameric cyclic nucleotide-binding domain (CNBD), attached to the C-linker disk (CL). Confocal patch-clamp fluorometry revealed a pronounced cooperativity of ligand binding among protomers. However, by which pathways allosteric signal transmission occurs remained elusive. Here, we investigate how changes in the structural dynamics of the CL- CNBD of mouse HCN2 upon cAMP binding relate to inter- and intrasubunit signal transmission. Applying a rigidity theory-based approach, we identify two intersubunit and one intrasubunit pathways that differ in allosteric coupling strength between cAMP binding sites or towards the CL. These predictions agree with results from electrophysiological and patch-clamp fluorometry experiments. Our results map out distinct routes within the CL-CNBD that modulate different cAMP binding responses in HCN2 channels. They signify that functionally relevant submodules may exist within and across structurally discernable subunits in HCN channels.

biophysics↗

bioRxiv2020

Bayesian Hidden Markov modeling and model selection by Kalman filtering applied to multi-dimensional data of ion channels

Inferring adequate kinetic schemes for ion channel gating from ensemble currents is a daunting task due to limited information in the data. We address this problem by using a parallelized Bayesian filter to specify hidden Markov models for current and fluorescence data. We demonstrate the flexibility of this algorithm by including different noise distributions. Our generalized Kalman filter outperforms both a classical Kalman filter and a rate equation approach when applied to patch-clamp data exhibiting realistic open-channel noise. The derived generalization also enables inclusion of orthogonal fluorescence data, making unidentifiable parameters identifiable and increasing the accuracy of the parameter estimates by an order of magnitude. By using Bayesian highest credibility volumes, we found that our approach, in contrast to the rate equation approach, yields a realistic quantification of uncertainty. Furthermore, the Bayesian filter delivers unbiased estimates for a wider range of data quality and identifies parameters which the rate equation approach does not identify. These results also demonstrate the power of assessing the validity of algorithms by Bayesian credibility volumes in general. Finally, we show that our Bayesian filter is more robust against errors induced by either analog filtering before analog-to-digital conversion or by limited time resolution of fluorescence data than a rate equation approach.

bioinformatics↗