title: Mechanisms classification for glycoside hydrolases by sequence and structure features using computational methods.
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 Neighbor (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.
authors.forename: Fan
authors.initials: F
authors.lastname: Yang
authors.forename: Lin
authors.initials: L
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002384:Catalysis; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D006026:Glycoside Hydrolases / Q000235:genetics*; D008958:Models, Molecular; D008968:Molecular Conformation; D012984:Software; D060388:Support Vector Machine; D014242:Trichoderma / Q000201:enzymology
chemical_list: D006026:Glycoside Hydrolases