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Molecular recognition and self-assembly special feature: Calix[4]arene-based conical-shaped ligands for voltage-dependent potassium channels.

ABSTRACT: Potassium channels are among the core functional elements of life because they underpin essential cellular functions including excitability, homeostasis, and secretion. We present here a series of multivalent calix[4]arene ligands that bind to the surface of voltage-dependent potassium channels (K(v)1.x) in a reversible manner. Molecular modeling correctly predicts the best candidates with a conical C(4) symmetry for optimal binding, and the effects on channel function are assessed electrophysiologically. Reversible inhibition was observed, without noticeable damage of the oocytes, for tetraacylguanidinium or tetraarginine members of the series with small lower rim O-substituents. Apparent binding constants were in the low micromolar range and had Hill coefficients of 1, consistent with a single site of binding. Suppression of current amplitude was accompanied by a positive shift in the voltage dependence of gating and slowing of both voltage sensor motion and channel opening. These effects are in keeping with expectations for docking in the central pore and interaction with the pore domain "turret."

SUBMITTER: Martos V

PROVIDER: S-EPMC2705557 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Molecular recognition and self-assembly special feature: Calix[4]arene-based conical-shaped ligands for voltage-dependent potassium channels.

Martos Vera V Bell Sarah C SC Santos Eva E Isacoff Ehud Y EY Trauner Dirk D de Mendoza Javier J

Proceedings of the National Academy of Sciences of the United States of America 20090512 26

Potassium channels are among the core functional elements of life because they underpin essential cellular functions including excitability, homeostasis, and secretion. We present here a series of multivalent calix[4]arene ligands that bind to the surface of voltage-dependent potassium channels (K(v)1.x) in a reversible manner. Molecular modeling correctly predicts the best candidates with a conical C(4) symmetry for optimal binding, and the effects on channel function are assessed electrophysio ...[more]

PMID: 19435843

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Project description:Voltage-gated ion channels play a central role in the generation of action potentials in the nervous system. They are selective for one type of ion - sodium, calcium, or potassium. Voltage-gated ion channels are composed of a central pore that allows ions to pass through the membrane and four peripheral voltage sensing domains that respond to changes in the membrane potential. Upon depolarization, voltage sensors in voltage-gated potassium channels (Kv) undergo conformational changes driven by positive charges in the S4 segment and aided by pairwise electrostatic interactions with the surrounding voltage sensor. Structure-function relations of Kv channels have been investigated in detail, and the resulting models on the movement of the voltage sensors now converge to a consensus; the S4 segment undergoes a combined movement of rotation, tilt, and vertical displacement in order to bring 3-4e(+) each through the electric field focused in this region. Nevertheless, the mechanism by which the voltage sensor movement leads to pore opening, the electromechanical coupling, is still not fully understood. Thus, recently, electromechanical coupling in different Kv channels has been investigated with a multitude of techniques including electrophysiology, 3D crystal structures, fluorescence spectroscopy, and molecular dynamics simulations. Evidently, the S4-S5 linker, the covalent link between the voltage sensor and pore, plays a crucial role. The linker transfers the energy from the voltage sensor movement to the pore domain via an interaction with the S6 C-termini, which are pulled open during gating. In addition, other contact regions have been proposed. This review aims to provide (i) an in-depth comparison of the molecular mechanisms of electromechanical coupling in different Kv channels; (ii) insight as to how the voltage sensor and pore domain influence one another; and (iii) theoretical predictions on the movement of the cytosolic face of the Kv channels during gating.

Project description:BackgroundStudies of the structure-function relationship in proteins for which no 3D structure is available are often based on inspection of multiple sequence alignments. Many functionally important residues of proteins can be identified because they are conserved during evolution. However, residues that vary can also be critically important if their variation is responsible for diversity of protein function and improved phenotypes. If too few sequences are studied, the support for hypotheses on the role of a given residue will be weak, but analysis of large multiple alignments is too complex for simple inspection. When a large body of sequence and functional data are available for a protein family, mature data mining tools, such as machine learning, can be applied to extract information more easily, sensitively and reliably. We have undertaken such an analysis of voltage-gated potassium channels, a transmembrane protein family whose members play indispensable roles in electrically excitable cells.ResultsWe applied different learning algorithms, combined in various implementations, to obtain a model that predicts the half activation voltage of a voltage-gated potassium channel based on its amino acid sequence. The best result was obtained with a k-nearest neighbor classifier combined with a wrapper algorithm for feature selection, producing a mean absolute error of prediction of 7.0 mV. The predictor was validated by permutation test and evaluation of independent experimental data. Feature selection identified a number of residues that are predicted to be involved in the voltage sensitive conformation changes; these residues are good target candidates for mutagenesis analysis.ConclusionMachine learning analysis can identify new testable hypotheses about the structure/function relationship in the voltage-gated potassium channel family. This approach should be applicable to any protein family if the number of training examples and the sequence diversity of the training set that are necessary for robust prediction are empirically validated. The predictor and datasets can be found at the VKCDB web site.

Dataset Information

Molecular recognition and self-assembly special feature: Calix[4]arene-based conical-shaped ligands for voltage-dependent potassium channels.

Publications

Molecular recognition and self-assembly special feature: Calix[4]arene-based conical-shaped ligands for voltage-dependent potassium channels.

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