Dataset Information

Identification of selective inhibitors of the potassium channel Kv1.1-1.2((3)) by high-throughput virtual screening and automated patch clamp.

ABSTRACT: Two voltage-dependent potassium channels, Kv1.1 (KCNA1) and Kv1.2 (KCNA2), are found to co-localize at the juxtaparanodal region of axons throughout the nervous system and are known to co-assemble in heteromultimeric channels, most likely in the form of the concatemer Kv1.1-1.2((3)) . Loss of the myelin sheath, as is observed in multiple sclerosis, uncovers the juxtaparanodal region of nodes of Ranvier in myelinated axons leading to potassium conductance, resulting in loss of nerve conduction. The selective blocking of these Kv channels is therefore a promising approach to restore nerve conduction and function. In the present study, we searched for novel inhibitors of Kv1.1-1.2((3)) by combining a virtual screening protocol and electrophysiological measurements on a concatemer Kv1.1-1.2((3)) stably expressed in Chinese hamster ovary K1 (CHO-K1) cells. The combined use of four popular virtual screening approaches (eHiTS, FlexX, Glide, and Autodock-Vina) led to the identification of several compounds as potential inhibitors of the Kv1.1-1.2((3)) channel. From 89 electrophysiologically evaluated compounds, 14 novel compounds were found to inhibit the current carried by Kv1.1-1.2((3)) channels by more than 80 % at 10 ?M. Accordingly, the IC(50) values calculated from concentration-response curve titrations ranged from 0.6 to 6 ?M. Two of these compounds exhibited at least 30-fold higher potency in inhibition of Kv1.1-1.2((3)) than they showed in inhibition of a set of cardiac ion channels (hERG, Nav1.5, and Cav1.2), resulting in a profile of selectivity and cardiac safety. The results presented herein provide a promising basis for the development of novel selective ion channel inhibitors, with a dramatically lower demand in terms of experimental time, effort, and cost than a sole high-throughput screening approach of large compound libraries.

SUBMITTER: Wacker SJ

PROVIDER: S-EPMC3525944 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Identification of selective inhibitors of the potassium channel Kv1.1-1.2((3)) by high-throughput virtual screening and automated patch clamp.

Wacker Sören J SJ Jurkowski Wiktor W Simmons Katie J KJ Fishwick Colin W G CW Johnson A Peter AP Madge David D Lindahl Erik E Rolland Jean-Francois JF de Groot Bert L BL

ChemMedChem 20120330 10

Two voltage-dependent potassium channels, Kv1.1 (KCNA1) and Kv1.2 (KCNA2), are found to co-localize at the juxtaparanodal region of axons throughout the nervous system and are known to co-assemble in heteromultimeric channels, most likely in the form of the concatemer Kv1.1-1.2((3)) . Loss of the myelin sheath, as is observed in multiple sclerosis, uncovers the juxtaparanodal region of nodes of Ranvier in myelinated axons leading to potassium conductance, resulting in loss of nerve conduction. T ...[more]

PMID: 22473914

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Project description:BackgroundThere is only one established drug binding site on sodium channels. However, drug binding of sodium channels shows extreme promiscuity: ∼25% of investigated drugs have been found to potently inhibit sodium channels. The structural diversity of these molecules suggests that they may not share the binding site, and/or the mode of action. Our goal was to attempt classification of sodium channel inhibitors by measuring multiple properties of inhibition in electrophysiology experiments. We also aimed to investigate if different properties of inhibition correlate with specific chemical properties of the compounds.Methodology/principal findingsA comparative electrophysiological study of 35 compounds, including classic sodium channel inhibitors (anticonvulsants, antiarrhythmics and local anesthetics), as well as antidepressants, antipsychotics and neuroprotective agents, was carried out using rNav1.2 expressing HEK-293 cells and the QPatch automatic patch-clamp instrument. In the multi-dimensional space defined by the eight properties of inhibition (resting and inactivated affinity, potency, reversibility, time constants of onset and offset, use-dependence and state-dependence), at least three distinct types of inhibition could be identified; these probably reflect distinct modes of action. The compounds were clustered similarly in the multi-dimensional space defined by relevant chemical properties, including measures of lipophilicity, aromaticity, molecular size, polarity and electric charge. Drugs of the same therapeutic indication typically belonged to the same type. We identified chemical properties, which were important in determining specific properties of inhibition. State-dependence correlated with lipophilicity, the ratio of the neutral form of molecules, and aromaticity: We noticed that the highly state dependent inhibitors had at least two aromatic rings, logP>4.0, and pKa<8.0.Conclusions/significanceThe correlations of inhibition properties both with chemical properties and therapeutic profiles would not have been evident through the sole determination of IC(50); therefore, recording multiple properties of inhibition may allow improved prediction of therapeutic usefulness.

Project description:Experimental records of single molecules or ion channels from fluorescence microscopy and patch-clamp electrophysiology often include high-frequency noise and baseline fluctuations that are not generated by the system under investigation and have to be removed. Moreover, multiple channels or conductance levels can be present at a time in the data that need to be quantified to accurately understand the behavior of the system. Manual procedures for removing these fluctuations and extracting conducting states or multiple channels are laborious, prone to subjective bias, and likely to hinder the processing of often very large data sets. We introduce a maximal likelihood formalism for separating signal from a noisy and drifting background such as fluorescence traces from imaging of elementary Ca2+ release events called puffs arising from clusters of channels, and patch-clamp recordings of ion channels. Parameters such as the number of open channels or conducting states, noise level, and background signal can all be optimized using the expectation-maximization algorithm. We implement our algorithm following the Baum-Welch approach to expectation-maximization in the portable Java language with a user-friendly graphical interface and test the algorithm on both synthetic and experimental data from the patch-clamp electrophysiology of Ca2+ channels and fluorescence microscopy of a cluster of Ca2+ channels and Ca2+ channels with multiple conductance levels. The resulting software is accurate, fast, and provides detailed information usually not available through manual analysis. Options for visual inspection of the raw and processed data with key parameters are provided, in addition to a range of statistics such as the mean open probabilities, mean open times, mean close times, dwell-time distributions for different number of channels open or conductance levels, amplitude distribution of all opening events, and number of transitions between different number of open channels or conducting levels in asci format with a single click.

Dataset Information

Identification of selective inhibitors of the potassium channel Kv1.1-1.2((3)) by high-throughput virtual screening and automated patch clamp.

Publications

Identification of selective inhibitors of the potassium channel Kv1.1-1.2((3)) by high-throughput virtual screening and automated patch clamp.

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