Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design

Abstract

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

Keywords: computer-aided drug design; cryo-EM; ion channel; machine learning; molecular dynamics.

FIGURE 1

Classification of ion channels based on activation mechanisms and structural similarities. The evolutionary relationships among these ion channels are measured by the phylogenetic tree of ion channels (Alexander et al., 2019) with representative channel structures (center). Representative ion channels selected from each class are shown with their 3D molecular structures.

FIGURE 2

Singe-particle cryo-EM workflow and relevant computational methods. Traditional methods for image processing and structural model building are shown in grey and ML-based methods in blue. Electron micrographs and particle images were visualized using cryoSPARC (Punjani et al., 2017). The density map and structural model were generated using Mol* viewer (Sehnal et al., 2021).

FIGURE 3

Computational approaches in the structure modeling, mechanistic study and drug discovery of ion channels. Several subplots were collected using Hermite (https://hermite.dp.tech), Mol* viewer (Sehnal et al., 2021), PyMOL (https://github.com/schrodinger/pymol-open-source) and the Pred-hERG web-server (Braga et al., 2015).