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Understanding how molecular events in ion channels impact neuronal excitability, as derived from the calculation of the time course of the membrane potentials, can help elucidate the mechanisms of neurological disease-linked mutations and support neuroactive drug design. Here, we propose a multiscale simulation approach which couples molecular simulations with neuronal simulations to predict the variations in membrane potential and neural spikes. We illustrate this through two examples. First, molecular dynamics simulations predict changes in current and conductance through the AMPAR neuroreceptor when comparing the wild-type protein with certain disease-associated variants. The results of these simulations inform morphologically detailed models of cortical pyramidal neurons, which are simulated using the Arbor framework to determine neural spike activity. Based on these multiscale simulations, we suggest that disease associated AMPAR variants may significantly impact neuronal excitability. In the second example, the Arbor model is coupled with coarse-grained Monte Carlo gating simulations of voltage-gated (K<sup>+</sup> and Na<sup>+</sup>) channels. The predicted current from these ion channels altered the membrane potential and, in turn, the excitation state of the neuron was updated in Arbor. The resulting membrane potential was then fed back into the Monte Carlo simulations of the voltage-gated ion channels, resulting in a bidirectional coupling of current and membrane potential. This allowed the transitions of the states of the ion channels to influence the membrane potentials and vice versa. Our Monte Carlo simulations also included the crucial, so far unexplored, effects of the composition of the lipid membrane embedding. We explored the influence of lipidic compositions only using the Monte Carlo simulations. Our combined approaches, which use several simplifying assumptions, predicted membrane potentials consistent with electrophysiological recordings and established a multiscale framework linking the atomistic perturbations to neuronal excitability.
Published in: Journal of Chemical Theory and Computation
Volume 22, Issue 2, pp. 783-793