  Single-channel electrophysiological recordings provide insights into
transmembrane ion permeation and channel gating mechanisms. The first step in
the analysis of the recorded currents involves an "idealization" process, in
which noisy raw data are classified into two discrete levels corresponding to
the open and closed states of channels. This provides valuable information on
the gating kinetics of ion channels. However, the idealization step is often
challenging in cases of currents with poor signal-to-noise ratios (SNR) and
baseline drifts, especially when the gating model of the target channel is not
identified. We report herein on a highly robust model-free idealization method
for achieving this goal. The algorithm, called AI2 (Adaptive Integrated
Approach for the Idealization of Ion Channel Currents), is composed of Kalman
filter and Gaussian Mixture Model (GMM) clustering and functions without user
input. AI2 automatically determines the noise reduction setting based on the
degree of separation between the open and closed levels. We validated the
method on pseudo-channel-current datasets which contain either computed or
experimentally recorded noise. The AI2 algorithm was then tested on actual
experimental data for biological channels including gramicidin A, a
voltage-gated sodium channel, and other unidentified channels. We compared the
idealization results with those obtained by the conventional methods, including
the 50%-threshold-crossing method.
