Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers

Gomase V.S.1*, Yash Parekh2, Subin Koshy3, Siddhesh Lakhan4
1Department of Bioinformatics, Padmashree Dr. D.Y. Patil University, Navi Mumbai, 400614, India
2Department of Bioinformatics, Padmashree Dr. D.Y. Patil University, Navi Mumbai, 400614, India
3Department of Bioinformatics, Padmashree Dr. D.Y. Patil University, Navi Mumbai, 400614, India
4Department of Bioinformatics, Padmashree Dr. D.Y. Patil University, Navi Mumbai, 400614, India
* Corresponding Author : virusgene1@yahoo.co.in

Received : -     Accepted : -     Published : 15-06-2009
Volume : 1     Issue : 1       Pages : 1 - 5
Genetics 1.1 (2009):1-5
DOI : http://dx.doi.org/10.9735/0975-2862.1.1.1-5

Keywords : DNA-binding domain crystal structure, PSSM, SVM, MHC, epitope, peptide vaccine Abbreviations: Goldman, Engelberg and Steitz, (GES); major histocompatibility complex, (MHC); Position Specific Scoring Matrices, (PSSMs); Support Vector Machine, (SVM)
Conflict of Interest : None declared

Cite - MLA : Gomase V.S., et al "Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers." International Journal of Genetics 1.1 (2009):1-5. http://dx.doi.org/10.9735/0975-2862.1.1.1-5

Cite - APA : Gomase V.S., Yash Parekh, Subin Koshy, Siddhesh Lakhan (2009). Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers. International Journal of Genetics, 1 (1), 1-5. http://dx.doi.org/10.9735/0975-2862.1.1.1-5

Cite - Chicago : Gomase V.S., Yash Parekh, Subin Koshy, and Siddhesh Lakhan "Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers." International Journal of Genetics 1, no. 1 (2009):1-5. http://dx.doi.org/10.9735/0975-2862.1.1.1-5

Copyright : © 2009, Gomase V.S., et al, Published by Bioinfo Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

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.

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