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A new peptide design strategy, the amino acid-based peptide prediction (AABPP) approach, is applied for predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201. The AABPP approach consists of two sets of predictive coefficients. The former is the coefficients for the physicochemical properties of amino acids and the latter is the weight factors for the residue positions in a peptide sequence. An iterative double least square technique is introduced to determine the two sets of coefficients alternately through a benchmark dataset. The coefficients converged through such an iterative process are further used to predict the bioactivities of query peptides. In the AABPP algorithm, the following eight physicochemical properties are used as the descriptors of amino acids: (i) lipophilic indices, (ii) hydrophilic indices, (iii) lipophilic surface area, (iv) hydrophilic surface area, (v) alpha-potency indices, (vi) beta-potency indices, (vii) coil-potency indices and (viii) volume of amino acid side chains. In comparison with the existing methods in this area, a remakable advantage of the current approach is that there is no need to know the exact conformation of a query peptide and its alignment with a template. The two steps are indispensable but cannot always be successfully realized otherwise. It is anticipated that the AABPP approach will become a powerful tool for peptide drug design, or at least play a complemetary role to the existing methods.

Predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201: an application of amino acid-based peptide prediction