In the literature, epitope prediction has been focused primarily on sequence-dependent methods based on various amino acid properties, such as hydrophilicity, solvent accessibility, secondary structure and others [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] . Several methods based on machine learning approaches have been applied, too [17] . They comprise hidden Markov models (HMM), artificial neural networks (ANN) and support vector machines (SVM) [18] [19] [20] [21] [22] . Machine learning methods have been frequently coupled with the so-called scale-based approach; this approach exploits one or more scales of amino acid properties to weight each residues of the sequence of interest. In particular it has been shown that the combination of different scales with several machine learning algorithms have better performances than single scale methods [23] .