Two-Dimensional-Dwell-Time Analysis of Ion Channel Gating using High Performance Computing Clusters

Abstract

The power of single-channel patch-clamp recordings is widely acknowledged among ion channel enthusiasts. The method allows observing the action of a single protein complex in real time and hence the deduction of the underlying conformational changes in the ion channel protein. Commonly, recordings are modeled using hidden Markov chains, connecting open and closed states in the experimental data with protein conformations. The rates between states denote transition probabilities that, for instance, could be modified by membrane voltage or ligand binding. Preferably, the time resolution of recordings should be in the range of microseconds or below, potentially bridging Molecular Dynamic simulations and experimental patch-clamp data. Modeling algorithms have to deal with limited recording bandwidth and a very noisy background. It was previously shown that the fit of 2-Dimensional-Dwell-Time histograms (2D-fit) with simulations is very robust in that regard. Errors introduced by the low-pass filter or noise cancel out to a certain degree when comparing experimental and simulated data. In addition, the topology of models, that is, the chain of open and closed states could be inferred from 2D-histograms. However, the 2D-fit was never applied to its full potential. The reason was the extremely time-consuming and unreliable fitting process, due to the stochastic variability in the simulations. We have now solved both issues by introducing a Message Passing Interface (MPI) allowing massive parallel computing on a High Performance Computing (HPC) cluster and obtaining ensemble solutions. With the ensembles, we have optimized the fit algorithm and demonstrated how important the ranked solutions are for difficult tasks related to a noisy background, fast gating events beyond the corner frequency of the low-pass filter and topology estimation of the underlying Markov model. The fit can reliably extract events down to a signal-to-noise ratio of one and rates up to ten times higher than the filter frequency. It is even possible to identify equivalent Markov topologies. Finally, we have shown that, by combining the objective function of the 2D-fit with the deviation of the current amplitude distributions automatic determination of the current level of the conducting state is possible. It is even possible to infer the level with an apparent current reduction due to the application of the low-pass filter. Making use of an HPC cluster, the power of 2D-Dwell-Time analysis can be used to its fullest, allowing extraction of the matching Markov model from a time series with minor input of the experimenter. Additionally, we add the benefit of estimating the reliability of the results by generating ensemble solutions.