Prediction of IL4 Inducing Peptides

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development...

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Main Authors: Sandeep Kumar Dhanda, Sudheer Gupta, Pooja Vir, G. P. S. Raghava
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Clinical and Developmental Immunology
Online Access:http://dx.doi.org/10.1155/2013/263952
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spelling doaj-99a6f951e50945a0aae2b3a4819df2792020-11-24T23:08:41ZengHindawi LimitedClinical and Developmental Immunology1740-25221740-25302013-01-01201310.1155/2013/263952263952Prediction of IL4 Inducing PeptidesSandeep Kumar Dhanda0Sudheer Gupta1Pooja Vir2G. P. S. Raghava3Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, IndiaBioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, IndiaBioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, IndiaBioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, IndiaThe secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.http://dx.doi.org/10.1155/2013/263952
collection DOAJ
language English
format Article
sources DOAJ
author Sandeep Kumar Dhanda
Sudheer Gupta
Pooja Vir
G. P. S. Raghava
spellingShingle Sandeep Kumar Dhanda
Sudheer Gupta
Pooja Vir
G. P. S. Raghava
Prediction of IL4 Inducing Peptides
Clinical and Developmental Immunology
author_facet Sandeep Kumar Dhanda
Sudheer Gupta
Pooja Vir
G. P. S. Raghava
author_sort Sandeep Kumar Dhanda
title Prediction of IL4 Inducing Peptides
title_short Prediction of IL4 Inducing Peptides
title_full Prediction of IL4 Inducing Peptides
title_fullStr Prediction of IL4 Inducing Peptides
title_full_unstemmed Prediction of IL4 Inducing Peptides
title_sort prediction of il4 inducing peptides
publisher Hindawi Limited
series Clinical and Developmental Immunology
issn 1740-2522
1740-2530
publishDate 2013-01-01
description The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.
url http://dx.doi.org/10.1155/2013/263952
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