Prediction of IL4 Inducing Peptides

• Published in: Clinical & developmental immunology Vol. 2013; no. 2013; pp. 1 - 9
• Main Authors: Raghava, Gajendra P. S., Vir, Pooja, Gupta, Sudheer, Dhanda, Sandeep Kumar
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013; Hindawi Limited; Wiley
• Subjects: Alleles; Amino Acid Motifs; Amino Acid Sequence; Binding Sites; Epitope Mapping; Epitopes - chemistry; Epitopes - immunology; Epitopes - metabolism; Helminthes; Histocompatibility Antigens Class II - chemistry; Histocompatibility Antigens Class II - genetics; Histocompatibility Antigens Class II - immunology; Histocompatibility Antigens Class II - metabolism; Immunology; Interleukin-4 - chemistry; Interleukin-4 - immunology; Interleukin-4 - metabolism; Models, Immunological; Peptide Fragments - chemistry; Peptide Fragments - immunology; Peptide Fragments - metabolism; Position-Specific Scoring Matrices; Protein Binding; Reproducibility of Results; Support Vector Machine; T-Lymphocytes - immunology; T-Lymphocytes - metabolism
• ISSN: 2314-8861; 1740-2522; 2314-7156; 1740-2530
• DOI: 10.1155/2013/263952

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.