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A theoretical model for calculating voltage sensitivity of ion channels and the application on Kv1.2 potassium channel.

ABSTRACT: Voltage sensing confers conversion of a change in membrane potential to signaling activities underlying the physiological processes. For an ion channel, voltage sensitivity is usually experimentally measured by fitting electrophysiological data to Boltzmann distributions. In our study, a two-state model of the ion channel and equilibrium statistical mechanics principle were used to test the hypothesis of empirically calculating the overall voltage sensitivity of an ion channel on the basis of its closed and open conformations, and determine the contribution of individual residues to the voltage sensing. We examined the theoretical paradigm by performing experimental measurements with Kv1.2 channel and a series of mutants. The correlation between the calculated values and the experimental values is at respective level, R(2) = 0.73. Our report therefore provides in silico prediction of key conformations and has identified additional residues critical for voltage sensing.

SUBMITTER: Yang H

PROVIDER: S-EPMC3328696 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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A theoretical model for calculating voltage sensitivity of ion channels and the application on Kv1.2 potassium channel.

Yang Huaiyu H Gao Zhaobing Z Li Ping P Yu Kunqian K Yu Ye Y Xu Tian-Le TL Li Min M Jiang Hualiang H

Biophysical journal 20120401 8

Voltage sensing confers conversion of a change in membrane potential to signaling activities underlying the physiological processes. For an ion channel, voltage sensitivity is usually experimentally measured by fitting electrophysiological data to Boltzmann distributions. In our study, a two-state model of the ion channel and equilibrium statistical mechanics principle were used to test the hypothesis of empirically calculating the overall voltage sensitivity of an ion channel on the basis of it ...[more]

PMID: 22768937

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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

A theoretical model for calculating voltage sensitivity of ion channels and the application on Kv1.2 potassium channel.

Publications

A theoretical model for calculating voltage sensitivity of ion channels and the application on Kv1.2 potassium channel.

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