Cyclic nucleotide-gated (CNG) ion channels of olfactory neurons are tetrameric membrane receptors that are composed of two A2 subunits, one A4 subunit, and one B1b subunit. Each subunit carries a cyclic nucleotide-binding domain in the carboxyl terminus, and the channels are activated by the binding of cyclic nucleotides. The mechanism of cooperative channel activation is still elusive. Using a complete set of engineered concatenated olfactory CNG channels, with all combinations of disabled binding sites and fit analyses with systems of allosteric models, the thermodynamics of microscopic cooperativity for ligand binding was subunit- and state-specifically quantified. We show, for the closed channel, that preoccupation of each of the single subunits increases the affinity of each other subunit with a Gibbs free energy (ΔΔG) of ∼−3.5 to ∼−5.5 kJ ⋅ mol−1, depending on the subunit type, with the only exception that a preoccupied opposite A2 subunit has no effect on the other A2 subunit. Preoccupation of two neighbor subunits of a given subunit causes the maximum affinity increase with ΔΔG of ∼−9.6 to ∼−9.9 kJ ⋅ mol−1. Surprisingly, triple preoccupation leads to fewer negative ΔΔG values for a given subunit as compared to double preoccupation. Channel opening increases the affinity of all subunits. The equilibrium constants of closed–open isomerizations systematically increase with progressive liganding. This work demonstrates, on the example of the heterotetrameric olfactory CNG channel, a strategy to derive detailed insights into the specific mutual control of the individual subunits in a multisubunit membrane receptor.

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 uncertainty quantification. Furthermore, the Bayesian filter delivers negligibly biased estimates for a wider range of data quality. For some data sets it identifies more parameters than the rate equation approach. 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.

a Microorganisms naturally move in microstructured fluids. Using the simulation method of multi-particle collision dynamics, we study in two dimensions an undulatory Taylor line swimming in a microchannel and in a cubic lattice of obstacles, which represent simple forms of a microstructured environment.In the microchannel the Taylor line swims at an acute angle along a channel wall with a clearly enhanced swimming speed due to hydrodynamic interactions with the bounding wall. While in a dilute obstacle lattice swimming speed is also enhanced, a dense obstacle lattice gives rise to geometric swimming. This new type of swimming is characterized by a drastically increased swimming speed. Since the Taylor line has to fit into the free space of the obstacle lattice, the swimming speed is close to the phase velocity of the bending wave traveling along the Taylor line. While adjusting its swimming motion within the lattice, the Taylor line chooses a specific swimming direction, which we classify by a lattice vector. When plotting the swimming velocity versus the magnitude of the lattice vector, all our data collapse on a single master curve. Finally, we also report more complex trajectories within the obstacle lattice.