Prediction of neurotoxins by support vector machine based on multiple feature vectors.
Neurotoxin is a toxin which acts on nerve cells by interacting with membrane proteins. Different neurotoxins have different functions and sources. With much more knowledge of neurotoxins it would be greatly helpful for the development of drug design. The support vector machine (SVM) was used to predict the neurotoxin based on multiple feature vector descriptors, including the amino acid composition, length of the protein sequence, weight of the protein and the evolution information described by position specific scoring matrix (PSSM). After a five-fold cross-validation procedure, the method achieved an accuracy of 100% in discriminating neurotoxins from non-toxins. As for classifying neurotoxins based on their sources and functions, the accuracy was 99.50% and 99.38% respectively. At last, the method yielded a good performance in sub-classification of ion channels inhibitors with the total accuracy of 87.27%. These results indicate that this method outperforms previously described NTXpred method.
Guang
Xuan-Min
XM
College of Chemistry, Sichuan University, Chengdu, 610064, China.
Guo
Yan-Zhi
YZ
Wang
Xia
X
Li
Meng-Long
ML
D000595:Amino Acid Sequence; D000596:Amino Acids; D008970:Molecular Weight; D009498:Neurotoxins / Q000737:chemistry* / Q000145:classification; D015203:Reproducibility of Results; D060388:Support Vector Machine
D000596:Amino Acids; D009498:Neurotoxins