Abstract
Several approaches to ion-channel gating modelling have been proposed. Although many models describe the dwell-time distributions correctly, they are incapable of predicting and explaining the long-term correlations between the lengths of adjacent openings and closings of a channel. In this paper we propose two simple random-walk models of the gating dynamics of voltage and Ca2+-activated potassium channels which qualitatively reproduce the dwell-time distributions, and describe the experimentally observed long-term memory quite well. Biological interpretation of both models is presented. In particular, the origin of the correlations is associated with fluctuations of channel mass density. The long-term memory effect, as measured by Hurst R/S analysis of experimental single-channel patch-clamp recordings, is close to the behaviour predicted by our models. The flexibility of the models enables their use as templates for other types of ion channel.
Highlights
Theoretical backgroundPotassium channels are integral proteins that enable rapid, selective transport of K? ions across the cell membrane down their electrochemical gradient (Chung et al 2007)
In this work we have proposed two random-walk models of BK channel gate dynamics with long-term memory
Their construction was motivated by Hurst analysis applied to the experimental data measured in the voltage range from -80 to 80 mV and for a Ca2? concentration of approximately 2 mM, supported by the results obtained by other authors under different experimental conditions (Bandeira et al 2008; Barbosa et al 2007; Varanda et al 2000), which suggest that intrinsic long-term memory exists in the series of subsequent single channel state dwell-times
Summary
Potassium channels are integral proteins that enable rapid, selective transport of K? ions across the cell membrane down their electrochemical gradient (Chung et al 2007). This force causes the reaction coordinate to fluctuate near the. The conformational space is divided into two parts not necessarily equal, and the TP position is chosen such that the probability of finding the gate in an open state resembles the channel open probability under the given experimental conditions (Fig. 7) Such a mechanism can be justified by considering the effect of the sensing domains (e.g. the S5–S6 linker) on the gate, where they move the part of protein responsible for gating to increase the number of accessible closed conformations and reduce the number of open ones, or the opposite. The model terms are estimated, analogously as for Model 1, by the gradient optimization technique
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