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      VGIchan: prediction and classification of voltage-gated ion channels.

      Genomics, Proteomics & Bioinformatics
      Amino Acid Sequence, Calcium Channels, classification, Chloride Channels, Dipeptides, chemistry, Ion Channel Gating, Markov Chains, Molecular Sequence Data, Potassium Channels, Voltage-Gated, Sequence Analysis, Protein, Sodium Channels, Software

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          Abstract

          This study describes methods for predicting and classifying voltage-gated ion channels. Firstly, a standard support vector machine (SVM) method was developed for predicting ion channels by using amino acid composition and dipeptide composition, with an accuracy of 82.89% and 85.56%, respectively. The accuracy of this SVM method was improved from 85.56% to 89.11% when combined with PSI-BLAST similarity search. Then we developed an SVM method for classifying ion channels (potassium, sodium, calcium, and chloride) by using dipeptide composition and achieved an overall accuracy of 96.89%. We further achieved a classification accuracy of 97.78% by using a hybrid method that combines dipeptide-based SVM and hidden Markov model methods. A web server VGIchan has been developed for predicting and classifying voltage-gated ion channels using the above approaches.

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          Most cited references10

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          Profile hidden Markov models.

          S. Eddy (1998)
          The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
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            Calcium-antagonist drugs.

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              The TASK-1 two-pore domain K+ channel is a molecular substrate for neuronal effects of inhalation anesthetics.

              Despite widespread use of volatile general anesthetics for well over a century, the mechanisms by which they alter specific CNS functions remain unclear. Here, we present evidence implicating the two-pore domain, pH-sensitive TASK-1 channel as a target for specific, clinically important anesthetic effects in mammalian neurons. In rat somatic motoneurons and locus coeruleus cells, two populations of neurons that express TASK-1 mRNA, inhalation anesthetics activated a neuronal K(+) conductance, causing membrane hyperpolarization and suppressing action potential discharge. These membrane effects occurred at clinically relevant anesthetic levels, with precisely the steep concentration dependence expected for anesthetic effects of these compounds. The native neuronal K(+) current displayed voltage- and time-dependent properties that were identical to those mediated by the open-rectifier TASK-1 channel. Moreover, the neuronal K(+) channel and heterologously expressed TASK-1 were similarly modulated by extracellular pH. The decreased cellular excitability associated with TASK-1 activation in these cell groups probably accounts for specific CNS effects of anesthetics: in motoneurons, it likely contributes to anesthetic-induced immobilization, whereas in the locus coeruleus, it may support analgesic and hypnotic actions attributed to inhibition of those neurons.
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