title: Designing of interferon-gamma inducing MHC class-II binders.
abstract: BACKGROUND
  The generation of interferon-gamma (IFN-γ) 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-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ 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-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ 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.
  
  
  CONCLUSIONS
  Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/).
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh 160036, India. raghava@imtech.res.in.
authors.forename: Sandeep Kumar
authors.initials: SK
authors.lastname: Dhanda
authors.forename: Pooja
authors.initials: P
authors.lastname: Vir
authors.forename: Gajendra P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry*; D000949:Histocompatibility Antigens Class II / Q000737:chemistry*; D006801:Humans; D007371:Interferon-gamma / Q000737:chemistry; D008956:Models, Chemical; D022223:Vaccines, Subunit / Q000737:chemistry*
chemical_list: D018984:Epitopes, T-Lymphocyte; D000949:Histocompatibility Antigens Class II; D022223:Vaccines, Subunit; D007371:Interferon-gamma