Applications to IFNepitope
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IFNepitope is a webserver that allows users to identify IFN-gamma inducing MHC class II binding peptides in a peptide/antigen. This server permits the user to predict and design IFN-gamma inducing regions in their protein of interest. This prediction method has been trained on 10433 experimentally validated IFN-gamma inducing and non-inducing MHC class II binders/peptides.
The prediction server has three major tools and the algorithm of the prediction is based on three models (motif based, SVM based and hybrid approach). User can select the method of prediction for all the three modules. An automated job ID will be generated (or user can define job name) and results will be generated in a tabular form (or alternatively mailed to the user provided email ID). The description of the three modules is: 1. Predict: This tool predicts the IFN-gamma inducing MHC II binder peptides in the desired protein sequence based on model selected by user. User can paste the protein sequence or alternatively upload the file with multiple peptide sequence. For details, please visit link 2. Design: This module is developed to assist the users in designing IFN-gamma inducing peptides. This tool allows the user to submit a single peptide sequence and the server generates all the possible (20) mutants for each residue site in the peptide and simultaneously displays the IFN-gamma inducing potential for each mutant. For details, please visit link 3. Scan: This tool enables the users to identify antigenic regions in their proteins that can stimulate specific immune response using IFN-gamma inducing antigenic regions. This tool allows the user to submit single protein sequence and generates overlapping peptides of desired length based on the method of prediction selected. The IFN-gamma inducing potential of generated peptides will be displayed in a tabular form. For details, please visit link This server aids the experimental researchers in better screening of their antigenic regions in terms of eliciting Th1 response for the targeted pathogen. The presentation of the peptide on the antigen-presenting cell by MHC II molecules is a prerequisite for the activation and differentiation of the naive T helper cells into T helper 1 population. This activated population generates IFN-gamma production to combat the pathogen. To assist in the identification of better subunit vaccine candidates, this prediction server allows the researcher to predict IFN-gamma inducer peptides from a list of multiple peptides. Simultaneously, this server enables the user to design and select better versions of their antigen of choice by generating mutants or overlapping peptides. Thus, based on SVM scores or probability assessment, the best antigenic regions with maximum IFN-gamma inducing potential can be chosen for experimental validation. This also help in reducing both the time and cost of validating a large set of peptides in wet experimentation. The server also helps to improve the efficacy of already known antigenic peptide, as it identifies mutations in a peptide to improve IFN-gamma inducing potential and suggests minimum modulations required to make important protein more immunogenic. In simple words, this server enables the users to narrow down the list of all possible MHC II binding epitopes from the bacterial proteome to select better IFN-gamma inducing peptide candidates. |