MACHINE INTELLIGENCE APPROACH FOR PREDICTION OF MHC BINDER FROM Human papilloma virus- 16

Gomase V.S.¹*, Tripathi A.K.^2
1Department of Bioinformatics, Padmashree Dr. D. Y. Patil University, Plot No-50, Sector-15, CBD Belapur, Navi Mumbai, 400614, MS, India
²Department of Bioinformatics, Padmashree Dr. D. Y. Patil University, Plot No-50, Sector-15, CBD Belapur, Navi Mumbai, 400614, MS, India
* Corresponding Author : Mailvirusgene1@yahoo.co.in

Received : -     Accepted : -     Published : 15-06-2009
Volume : 1     Issue : 1       Pages : 4 - 6
Int J Mach Intell 1.1 (2009):4-6
DOI : http://dx.doi.org/10.9735/0975-2927.1.1.4-6

Keywords : Antigen, Epitope, MHC, Peptide vaccine
Conflict of Interest : None declared

Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class molecules is important in vaccine development from Human papilloma virus-16. 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 capsid protein L1 form Human papilloma virus-16 are important determinant for protection of many disorders 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 also found the SVM based MHCII-IAb, MHCII-IAd, MHCII-IAg7 and MHCII- RT1.B peptide regions, which represented predicted binders from viral capsid protein (Table-1). 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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