Predicting MHC Class II Epitopes Using Separated Constructive Clustering Ensemble

Abstract

Successful Prediction for MHC Class II epitopes is an essential step in designing Genetic Vaccines[1]. MHC Class II epitopes are short peptides with length between 9 and 25 amino acids which are bound by MHC. These epitopes are recognized by T-Cell Receptors and leads to activation of cellular and humoral immune system and, ultimately, to effective destruction of pathogenic organism. Successful prediction of MHC class II epitopes is more difficult than MHC class I epitopes due to open binding groove at both ends in class II molecules, this structure leads to variable length for MHC II epitopes and complicating the task for detecting the core binding 9-mer. Large efforts have been exerted in developing algorithms to predict which peptides will bind to a given MHC class II molecules. In this paper we presented a novel classification algorithm for predicting MHC Class II epitopes using Multiple Instance Learning technique. Separated Constructive Clustering Ensemble (SCCE) is our new version for Constructive Clustering Ensemble (CCE)[27]. This method was used for converting multiple instance learning problem into normal Single Instance Problem. Most of the processing in this method lies mainly in vector preparation step before using classifier; Support Vector Machine (SVM) has been used as a method with proven performance in a wide range of practical applications[38]. SCCE integrated many algorithms like Genetic Algorithm, K medoid clustering, Ensemble learning and Support vector machine in an orchestration to predict the MHC II epitopes. SCCE was tested over three benchmark data sets and proved to be very competitive with the state of art regression methods. SCCE achieved these results using only binder and non binder flags; without need for regression data. An implementation of MHCII-SCCE as an online web server for predicting MHC-II Epitopes is freely accessible at http://www.fci.cu.edu.eg:8080/MHCII_Server/MHCII SCCE Server 1.0.htm.