Designing of interferon-gamma inducing MHC class-II binders.

ABSTRACT: BACKGROUND: The generation of interferon-gamma (IFN-?) by MHC class II activated CD4+ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author's knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-? inducing peptides. The main dataset used in this study contains 3705 IFN-? inducing and 6728 non-IFN-? inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-? inducing epitopes and 2160 epitopes that induce other cytokine except IFN-?. In addition we have alternate dataset that contains IFN-? inducing and equal number of random peptides. RESULTS: It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-? inducing capabilities of these peptides. We identified the motifs in IFN-? inducing binders/peptides using MERCI software. Our analysis indicates that IFN-? inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-? inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC. CONCLUSION: Based on this study, we have developed a webserver for predicting i) IFN-? inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-? inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/).

SUBMITTER: Dhanda SK 

PROVIDER: S-EPMC4235049 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature