Combining Support Vector Machine with Dual g-gap Dipeptides to Discriminate between Acidic and Alkaline Enzymes

Author(s): Xianfang Wang*, Hongfei Li, Peng Gao, Yifeng Liu, Wenjing Zeng.

Journal Name: Letters in Organic Chemistry

Volume 16 , Issue 4 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


The catalytic activity of the enzyme is different from that of the inorganic catalyst. In a high-temperature, over-acid or over-alkaline environment, the structure of the enzyme is destroyed and then loses its activity. Although the biochemistry experiments can measure the optimal PH environment of the enzyme, these methods are inefficient and costly. In order to solve these problems, computational model could be established to determine the optimal acidic or alkaline environment of the enzyme. Firstly, in this paper, we introduced a new feature called dual g-gap dipeptide composition to formulate enzyme samples. Subsequently, the best feature was selected by using the F value calculated from analysis of variance. Finally, support vector machine was utilized to build prediction model for distinguishing acidic from alkaline enzyme. The overall accuracy of 95.9% was achieved with Jackknife cross-validation, which indicates that our method is professional and efficient in terms of acid and alkaline enzyme predictions. The feature proposed in this paper could also be applied in other fields of bioinformatics.

Keywords: Acidic enzyme, alkaline enzyme, support vector machine, dipeptide composition, feature selection, crossvalidation.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Page: [325 - 331]
Pages: 7
DOI: 10.2174/1570178615666180925125912
Price: $58

Article Metrics

PDF: 2