EHPred: an SVM-based method for epoxide hydrolases recognition and classification.
- Author:
Jia JIA
1
;
Liang YANG
;
Zi-Zhang ZHANG
Author Information
1. James. D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310008, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Amino Acid Sequence;
Artificial Intelligence;
Computing Methodologies;
Epoxide Hydrolases;
chemistry;
classification;
Molecular Sequence Data;
Pattern Recognition, Automated;
methods;
Sequence Alignment;
methods;
Sequence Analysis, Protein;
methods;
Sequence Homology, Amino Acid
- From:
Journal of Zhejiang University. Science. B
2006;7(1):1-6
- CountryChina
- Language:English
-
Abstract:
A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classifiers were built using three different feature vectors extracted from the primary sequence of EHs: the amino acid composition (AAC), the dipeptide composition (DPC), and the pseudo-amino acid composition (PAAC). Validated by 5-fold cross tests, the first layer SVM classifier can differentiate EHs and non-EHs with an accuracy of 94.2% and has a Matthew's correlation coefficient (MCC) of 0.84. Using 2-fold cross validation, PAAC-based second layer SVM can further classify EH subfamilies with an overall accuracy of 90.7% and MCC of 0.87 as compared to AAC (80.0%) and DPC (84.9%). A program called EHPred has also been developed to assist readers to recognize EHs and to classify their subfamilies using primary protein sequences with greater accuracy.
