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Designing of interferon-gamma inducing MHC class-II binders
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4235049/ https://www.ncbi.nlm.nih.gov/pubmed/24304645 http://dx.doi.org/10.1186/1745-6150-8-30 |
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author | Dhanda, Sandeep Kumar Vir, Pooja Raghava, Gajendra PS |
author_facet | Dhanda, Sandeep Kumar Vir, Pooja Raghava, Gajendra PS |
author_sort | Dhanda, Sandeep Kumar |
collection | PubMed |
description | 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. CONCLUSION: 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/). REVIEWERS: This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai. |
format | Online Article Text |
id | pubmed-4235049 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42350492014-11-19 Designing of interferon-gamma inducing MHC class-II binders Dhanda, Sandeep Kumar Vir, Pooja Raghava, Gajendra PS Biol Direct Research 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. CONCLUSION: 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/). REVIEWERS: This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai. BioMed Central 2013-12-05 /pmc/articles/PMC4235049/ /pubmed/24304645 http://dx.doi.org/10.1186/1745-6150-8-30 Text en Copyright © 2013 Dhanda et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Dhanda, Sandeep Kumar Vir, Pooja Raghava, Gajendra PS Designing of interferon-gamma inducing MHC class-II binders |
title | Designing of interferon-gamma inducing MHC class-II binders |
title_full | Designing of interferon-gamma inducing MHC class-II binders |
title_fullStr | Designing of interferon-gamma inducing MHC class-II binders |
title_full_unstemmed | Designing of interferon-gamma inducing MHC class-II binders |
title_short | Designing of interferon-gamma inducing MHC class-II binders |
title_sort | designing of interferon-gamma inducing mhc class-ii binders |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4235049/ https://www.ncbi.nlm.nih.gov/pubmed/24304645 http://dx.doi.org/10.1186/1745-6150-8-30 |
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