Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers
Journal Title: International Journal of Genetics - Year 2009, Vol 1, Issue 1
Abstract
The machine learning techniques are playing a major role in the field of immunoinformatics for DNA-binding domain analysis. Functional analysis of the binding ability of DNA-binding domain protein antigen peptides to major histocompatibility complex (MHC) class molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Such predictions can be used to select epitopes for use in rational vaccine design and to increase the understanding of roles of the immune system in infectious diseases. Antigenic epitopes of DNA-binding domain protein form Human papilloma virus-31 are important determinant for protection of many host form viral infection. This study shows active part in host immune reactions and involvement of MHC class-I and MHC II in response to almost all antigens. We used PSSM and SVM algorithms for antigen design, which represented predicted binders as MHCII-IAb, MHCII-IAd, MHCII-IAg7, and MHCII- RT1.B nonamers from viral DNA-binding domain crystal structure. These peptide nonamers are from a set of aligned peptides known to bind to a given MHC molecule as the predictor of MHC-peptide binding. Analysis shows potential drug targets to identify active sites against diseases.
Authors and Affiliations
V. S. Gomase, Yash Parekh, Subin Koshy, Siddhesh Lakhan
Keywords
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- EP ID EP170142
- DOI 10.9735/0975-2862.1.1.1-5
- Views 111
- Downloads 0
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