bioRxivBiophysicsPreprint
Building predictive Markov State Models of ion channel permeation from Molecular Dynamics
L. Catacuzzeno, M. V. Leonardi, F. Franciolini, C. Domene, A. Michelucci, S. Furini
Abstract
Molecular dynamics (MD) simulation of biological processes has always been a very 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 recently 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. Of the many studies on protein simulation and the MSM-assisted approach, only a few have addressed ion channel permeation and, more importantly, none of these have tried to build a model capable to predict the currents passing through the channels, which are ultimately crucial for comparing simulations with experimental results. Herein, we propose a method for building an MSM of ion channel permeation that correctly evaluates the current flowing through the channel. This was done by including in the model the definition of a flux matrix carrying information on the charge moving across the channel, suitably built to be used in conjunction with the transition matrix to predict the ion current. The proposed method is also able to drastically reduce the number of states so to obtain an MSM simple enough to be easily understood. Finally, we applied the method to the KcsA channel, obtaining a four-state MSM capable of accurately reproducing the single channel ion current from microseconds MD trajectories.
- assay: multiome
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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