College of Chemistry, Sichuan University, Chengdu, 610064, China.
Interdiscip Sci. 2010 Sep;2(3):241-6. doi: 10.1007/s12539-010-0044-7. Epub 2010 Jul 25.
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.
神经毒素是通过与膜蛋白相互作用而作用于神经细胞的毒素。不同的神经毒素具有不同的功能和来源。随着对神经毒素认识的不断深入，它将极大地有助于药物设计的发展。支持向量机（SVM）用于基于多种特征向量描述符预测神经毒素，包括氨基酸组成、蛋白质序列长度、蛋白质重量和位置特异性评分矩阵（PSSM）描述的进化信息。经过五重交叉验证程序，该方法在区分神经毒素和非毒素方面的准确率达到 100%。至于根据来源和功能对神经毒素进行分类，准确率分别为 99.50%和 99.38%。最后，该方法在离子通道抑制剂的细分中表现出良好的性能，总准确率为 87.27%。这些结果表明，该方法优于先前描述的 NTXpred 方法。
