• Davorka Jandrlić Faculty of mechanical engineering
Keywords: MHC - peptide binding, The rule based classification, K – mean clustering,


Binding of proteolyzed fragments of proteins to MHC molecules is essential and the most selective step that determines T-cell epitopes. Therefore, the prediction of MHC-peptide binding is principal for anticipating potential T cell epitopes and is of immense relevance in vaccine design. Despite numerous methods for predicting MHC binding ligands, there still exist limitations that affect the reliability of a prevailing number of methods. Certain important methods based on physicochemical properties have very low reported accuracy. The aim of this paper is to present a new approach of extracting the most important physicochemical properties that influence the classification of MHC-binding ligands. In this study, we have developed rule based classification models which take into account the physicochemical properties of amino acids and their frequencies. The models use k-means clustering technique for extracting the relevant physicochemical properties. The results of the study indicate that the physicochemical properties of amino acids contribute significantly to the peptide-binding and that the different alleles are characterized by a different set of the physicochemical properties.

Author Biography

Davorka Jandrlić, Faculty of mechanical engineering
Department of mathematics


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