Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations

Abstract

Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods have evolved into important and invaluable approaches for studying ion channels and their functions. This is mainly due to their demanding mechanism of action where a static picture of an ion channel structure is often insufficient to fully understand the underlying mechanism. Therefore, the use of computational methods is as important as chemical-biological based experimental methods for a better understanding of ion channels. This review provides an overview on a variety of computational methods and software specific to the field of ion-channels. Artificial intelligence (or more precisely machine learning) approaches are applied for the sequence-based prediction of ion channel family, or topology of the transmembrane region. In case sufficient data on ion channel modulators is available, these methods can also be applied for quantitative structureactivity relationship (QSAR) analysis. Molecular dynamics (MD) simulations combined with computational molecular design methods such as docking can be used for analysing the function of ion channels including ion conductance, different conformational states, binding sites and ligand interactions, and the influence of mutations on their function. In the absence of a three-dimensional protein structure, homology modelling can be applied to create a model of your ion channel structure of interest. Besides highlighting a wide range of successful applications, we will also provide a basic introduction to the most important computational methods and discuss best practices to get a rough idea of possible applications and risks.

Keywords: Ion channel; Topology prediction; Structure-based design; Homology modelling; Docking; Molecular dynamics simulations; Machine learning.