A model for the evaluation of domain based classification of GPCR

Abstract

G-Protein Coupled Receptors (GPCR) are the largest family of membrane bound receptor and plays a vital role in various biological processes with their amenability to drug intervention. They are the spotlight for the pharmaceutical industry. Experimental methods are both time consuming and expensive so there is need to develop a computational approach for classification to expedite the drug discovery process. In the present study domain based classification model has been developed by employing and evaluating various machine learning approaches like Bagging, J48, Bayes net, and Naive Bayes. Various softwares are available for predicting domains. The result and accuracy of output for the same input varies for these software's. Thus, there is dilemma in choosing any one of it. To address this problem, a simulation model has been developed using well known five softwares for domain prediction to explore the best predicted result with maximum accuracy. The classifier is developed for classification up to 3 levels for class A. An accuracy of 98.59% by Naïve Bayes for level I, 92.07% by J48 for level II and 82.14% by Bagging for level III has been achieved.

Keywords: Bayes model; GPCR; membrane proteins; model.

Figure 1

(a) Representation of GPCR; (b) GPCR Family Hierrarchy; (c) ROC curve for metabotropic glutamate; (d) ROC curve for rhodopsin; (e) ROC curve for adhesion; (f) ROC curve for frizzled; (g) ROC curve for secretin