Local sequence information-based support vector machine to classify voltage-gated potassium channels.
In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP), reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels.
Liu
Li-Xia
LX
College of Chemistry, Sichuan University, Chengdu 610064, China, liml@scu.edu.cn.
Li
Meng-Long
ML
Tan
Fu-Yuan
FY
Lu
Min-Chun
MC
Wang
Ke-Long
KL
Guo
Yan-Zhi
YZ
Wen
Zhi-Ning
ZN
Jiang
Lin
L
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D010455:Peptides / Q000737:chemistry; D024642:Potassium Channels, Voltage-Gated / Q000145:classification / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods
D010455:Peptides; D024642:Potassium Channels, Voltage-Gated