Abstract: Methodology for the automated selection and/or optimization of T-cell epitopes is disclosed. The invention provides a data processing system which utilizes sequence-based statistical pattern recognition to compute an epitope selection matrix based on the informational content of epitopes known to bind to a particular major histocompatibility class I allele. The resulting Bayes-corrected scoring matrix is used to predict the relative binding affinities of candidate T-cell epitopes derived from immunologically relevant antigens of self or foreign origin. One aspect of the invention describes an analytical method for identification of modifications in known or predicted T-cell epitopes that confer upon the epitopes the ability to elicit stronger cellular immune response due to more efficient processing and/or presentation to T-cells.

Abstract: Methodology for the automated selection and/or optimization of T-cell epitopes is disclosed. The invention provides a data processing system which utilizes sequence-based statistical pattern recognition to compute an epitope selection matrix based on the informational content of epitopes known to bind to a particular major histocompatibility class I allele. The resulting Bayes-corrected scoring matrix is used to predict the relative binding affinities of candidate T-cell epitopes derived from immunologically relevant antigens of self or foreign origin. One aspect of the invention describes an analytical method for identification of modifications in known or predicted T-cell epitopes that confer upon the epitopes the ability to elicit stronger cellular immune response due to more efficient processing and/or presentation to T-cells.