title: VGIchan: prediction and classification of voltage-gated ion channels.
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.
authors.affiliation: Institute of Microbial Technology, Chandigarh 160036, India.
authors.forename: Sudipto
authors.initials: S
authors.lastname: Saha
authors.forename: Jyoti
authors.initials: J
authors.lastname: Zack
authors.forename: Balvinder
authors.initials: B
authors.lastname: Singh
authors.forename: G P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D000595:Amino Acid Sequence; D015220:Calcium Channels / Q000145:classification*; D018118:Chloride Channels / Q000145:classification*; D004151:Dipeptides / Q000737:chemistry; D015640:Ion Channel Gating; D008390:Markov Chains; D008969:Molecular Sequence Data; D024642:Potassium Channels, Voltage-Gated / Q000145:classification*; D020539:Sequence Analysis, Protein; D015222:Sodium Channels / Q000145:classification*; D012984:Software
chemical_list: D015220:Calcium Channels; D018118:Chloride Channels; D004151:Dipeptides; D024642:Potassium Channels, Voltage-Gated; D015222:Sodium Channels