Mechanisms classification for glycoside hydrolases by sequence and structure features using computational methods Online publication date: Tue, 21-Oct-2014
by Fan Yang; Lin Wang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 9, No. 4, 2014
Abstract: Glycoside Hydrolases (GHs) have played key roles in the development of biofuels as well as many other industries. Research aimed at accurate classification of catalytic mechanisms to increase the catalytic activity of GHs is receiving extensive attention. The traditional theories or methods used in the study of catalytic mechanisms of GHs are limited by reaction conditions. They are not suitable for the study of various GHs because different enzymes would show devious physicochemical properties. In this paper, a new method is proposed to classify and predict the catalytic mechanism of a certain glycoside hydrolase according to their sequence and structure features using k-Nearest Neighbour (kNN) classifier, Support Vector Machine (SVM), Naive Bayes (NB) Classifier and the Multilayer Perception (MLP) Classifier. The classification performance of the four computational methods used were evaluated and compared. Experimental results show that each classifier has its own advantages, but the kNN classifier is more accurate at the overall level. This research also helps us to gain a better understanding of the catalytic mechanisms in different GHs.
Online publication date: Tue, 21-Oct-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org