VGIchan: prediction and classification of voltage-gated ion channels.
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.
Saha
Sudipto
S
Institute of Microbial Technology, Chandigarh 160036, India.
Zack
Jyoti
J
Singh
Balvinder
B
Raghava
G P S
GP
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
D015220:Calcium Channels; D018118:Chloride Channels; D004151:Dipeptides; D024642:Potassium Channels, Voltage-Gated; D015222:Sodium Channels