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Published on: February 8, 2011
Modeling the concentration-dependent permeation modes of the KcsA potassium ion channel
1Department of Physics, Benedictine University, Lisle, Illinois 60532, USA. pete@circle4.com
Abstract:
The potassium channel from Streptomyces lividans (KcsA) is an integral membrane protein with sequence similarity to all known potassium channels, particularly in the selectivity filter region. A recently proposed model for ion channels containing either n or (n-1) single-file ions in their selectivity filters [P. H. Nelson, J. Chem. Phys. 177, 11396 (2002)] is applied to published KcsA channel K+ permeation data that exhibit a high-affinity process at low concentrations and a low-affinity process at high concentrations [M. LeMasurier et al., J. Gen. Physiol. 118, 303 (2001)]. The kinetic model is shown to provide a reasonable first-order explanation for both the high- and low-concentration permeation modes observed experimentally. The low-concentration mode ([K+]<200 mM) has a 200-mV dissociation constant of 56 mM and a conductance of 88 pS. The high-concentration mode ([K+]>200 mM) has a 200-mV dissociation constant of 1100 mM and a conductance of 500 pS. Based on the permeation model, and x-ray analysis [J. H. Morais-Cabral et al., Nature (London) 414, 37 (2001)], it is suggested that the experimentally observed K+ permeation modes correspond to an n=3 mechanism at high concentrations and an n=2 mechanism at low concentrations. The ratio of the electrical dissociation distances for the high- and low-concentration modes is 3:2, also consistent with the proposed n=3 and n=2 modes. Model predictions for K+ channels that exhibit asymmetric current-voltage (I-V) curves are presented, and further validation of the kinetic model via molecular simulation and experiment is discussed. The qualitatively distinct I-V characteristics exhibited experimentally by Tl+, NH+4, and Rb+ ions at 100 mM concentration can also be explained using the model, but more extensive experimental tests are required for quantitative validation of the model predictions.
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