[Prediction of lipases types by different scale pseudo-amino acid composition].
[Prediction of lipases types by different scale pseudo-amino acid composition].
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发表时间：
2008-11
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通讯作者：
Guangya Zhang;Hongchun Li;Jiaqiang Gao;B. Fang
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作者：
Guangya Zhang;Hongchun Li;Jiaqiang Gao;B. Fang
脂肪酶是生物技术中广泛使用的酶。虽然它们催化相同的反应，但它们的序列不同。因此，迫切需要开发一种快速可靠的方法来根据脂肪酶的序列鉴定脂肪酶的类型，甚至只是确认它们是否是脂肪酶。通过提出基于两个尺度的伪氨基酸组成方法来提取序列的特征，引入了一种基于k-近邻的预测器来解决这个问题。通过10倍交叉验证试验获得的总体成功率如下：对于脂肪酶和非脂肪酶，成功率分别为92.8%、91.4%和91.3%。对于脂肪酶类型，成功率分别为92.3%、90.3%和89.7%。其中，以Z标度为基础的伪氨基酸组成最好，T标度次之。它们的性能显着优于其他6种常用的序列特征提取方法。对于这样一个严格的数据集的高成功率表明预测脂肪酶的类型是可行的，并且不同尺度的伪氨基酸组成可能是提取蛋白质序列特征的有用工具，或者至少可以对许多其他现有方法起到补充作用。
Lipases are widely used enzymes in biotechnology. Although they catalyze the same reaction, their sequences vary. Therefore, it is highly desired to develop a fast and reliable method to identify the types of lipases according to their sequences, or even just to confirm whether they are lipases or not. By proposing two scales based pseudo amino acid composition approaches to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to address the problems. The overall success rates thus obtained by the 10-fold cross-validation test were shown as below: for predicting lipases and nonlipase, the success rates were 92.8%, 91.4% and 91.3%, respectively. For lipase types, the success rates were 92.3%, 90.3% and 89.7%, respectively. Among them, the Z scales based pseudo amino acid composition was the best, T scales was the second. They outperformed significantly than 6 other frequently used sequence feature extraction methods. The high success rates yielded for such a stringent dataset indicate predicting the types of lipases is feasible and the different scales pseudo amino acid composition might be a useful tool for extracting the features of protein sequences, or at lease can play a complementary role to many of the other existing approaches.