Alamethicin is an antibiotic peptide from the fungus Trichoderma viride that forms ion channels in bilayer lipid membranes. Each peptide consists of 20 amino acids that can form larger channels with the congregation of multiple monomers of the peptide. These formed ion channels have some voltage dependent characteristics when a potential is induced across the bilayer. This potential can be from an applied voltage source or from an ion concentration gradient inducing a transmembrane potential across the membrane. The peptide alamethicin can be modeled as a conductor that allows the flow of ions through the membrane. The formed channels have distinct conductance level states caused by accumulation of additional alamethicin monomers being added to an individual ion channel. The voltage dependence of the accumulation of multiple ion channels can be modeled for the average response. A probabilistic model is used to capture the statistics of the state changes of individual channels. This type of model can be summed to simulate the conductance of multiple channels within a bilayer. This work focuses on obtaining the statistic for individual ion channels and using those statistics to show that a probabilistic model of the peptide’s conductance can capture some of the dynamics seen in aggregated responses. The Nernst equation is used to estimate the transmembrane potential caused by an ion gradient of a bilayer in equilibrium. This potential is used in the model to assist in determining the current conductance states of an individual channel of the peptide in the presence of an ion gradient. This paper will show the experimental results of ion currents across a bilayer induced by membrane potentials and the ion currents induced by ion gradients. The statistics of the measurements are used in a probabilistic conductance model of the peptide alamethicin.
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Modeling the Conductance of the Peptide Alamethicin With and Without Ion Gradients
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Creasy, MA, & Leo, DJ. "Modeling the Conductance of the Peptide Alamethicin With and Without Ion Gradients." Proceedings of the ASME 2011 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASME 2011 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2. Scottsdale, Arizona, USA. September 18–21, 2011. pp. 707-713. ASME. https://doi.org/10.1115/SMASIS2011-5117
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