Astemizole-based turn-on fluorescent probes for imaging hERG potassium channel

In-situ and amplification-free imaging of hERG ion channels at single-cell level using a unique core-molecule-shell-secondary antibody SERS nanoprobe

Research on ion channels and their monoclonal antibodies plays a critical role in drug development and disease diagnosis. The current ion channel researches are often not conducted in the microenvironment for cells survival, which restricts the mechanism study of the links between the cell structure and the ion channel function. In this work, we synthesized gold core-4-mercaptobenzonitrile-sliver shell-goat anti-rabbit immunoglobulin G (Au@4-MBN@Ag@IgG) nanoparticles as surface-enhanced Raman scattering (SERS) nanoprobes for investigating the human ether-a-go-go related gene (hERG) potassium ion channel in cell membranes. This is the first attempt to study ion channels using SERS. Due to the unique core-molecule-shell structure and the silver shell of nanoprobes, strong and stable SERS signal was obtained. With the help of antibodies, the Au@4-MBN@Ag@IgG nanoprobes were captured by hERG antibodies and then bonded with hERG ion channels based on the sandwich immune response. The reporter molecule, 4-MBN, displayed a strong and sharp characteristic peak at 2233 cm^-1 in the Raman silent region. The intensity of this peak denoted the concentration of antibodies and the expression of ion channel proteins. We successfully applied this amplification-free method for in-situ imaging the distribution of the hERG ion channel on the transfected HEK293 cell surface at the single-cell level. This hERG ion channel profiling strategy promises a maneuverable tool for ion channel research.

Cryo-EM Structure of K+-Bound hERG Channel Complexed with the Blocker Astemizole

The hERG channel is a voltage-gated potassium channel involved in cardiac repolarization. Off-target hERG inhibition by drugs has become a critical issue in the pharmaceutical industry. The three-dimensional structure of the hERG channel was recently reported at 3.8-Å resolution using cryogenic electron microscopy (cryo-EM). However, the drug inhibition mechanism remains unclear because of the scarce structural information regarding the drug- and potassium-bound hERG channels. In this study, we obtained the cryo-EM density map of potassium-bound hERG channel complexed with astemizole, a well-known hERG inhibitor that increases risk of potentially fatal arrhythmia, at 3.5-Å resolution. The structure suggested that astemizole inhibits potassium conduction by binding directly below the selectivity filter. Furthermore, we propose a possible binding model of astemizole to the hERG channel and provide insights into the unusual sensitivity of hERG to several drugs.

The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis

Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure-activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction.