Abstract—Deciphering the understanding of T cell epitopes is critical for vaccine development. As recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules, cytotoxic T cells are activated. This is the major step to initiate of immune system response. Knowledge of the MHC specificity will enlighten the way of diagnosis, treatment of pathogens as well as peptide vaccine development. So far, a number of methods have been developed to predict MHC/peptide binding. In this paper, several encoding schemes were performed to predict MHC/peptide complexes. The tests have been carried out on comparatively large HLA-A and HLA-B allele peptide three binding datasets extracted from the Immune Epitope Database and Analysis resource (IEDB). Experimental results show OETMAP encoding technique leads to better classification performance than other amino acid encoding schemes on a standalone classifier.

Index Terms—Epitope prediction, major histocompatibility complex class I, feature encoding, peptide classification.

Murat Gök is with the Computer Engineering, Yalova University, 77100, Yalova, Turkey (e-mail: muratgok@ gmail.com, murat.gok@yalova.edu.tr).

 

Cite:Murat Gök, "A Comparison of Several Feature Encoding Techniques for MHC Class I Binding Prediction," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 3, no. 2, pp. 85-87, 2013.