Building predictive Markov State Models of ion channel permeation from Molecular Dynamics simulations

Molecular dynamics (MD) simulation of biological processes has always been a challenging task due to the long timescales of the processes involved and the challenges associated with handling the large amount of output data. Markov State Models (MSMs) have been introduced as a powerful tool in this area of research, as they provide a mechanistically comprehensible synthesis of the large amount of MD data and, at the same time, can be used to estimate experimental properties of biological processes. Herein, we propose a method for building an MSM of ion channel permeation that directly evaluates the current flowing through the channel from the model's transition matrix (T), which is crucial for comparing simulations and experimental data. This was achieved by including in the model a flux matrix (F) that summarize information on the charge moving across the channel between each pair of states of the MSM, and that can be used in conjunction with T to predict the ion current. A procedure to drastically reduce the number of states in the MSM while preserving the estimated ion current is also proposed. Application of the method to the KcsA channel returned an MSM with 5 states with significant probability that is capable of accurately reproducing the single channel ion current from microseconds MD trajectories.