A method to predict immune response to a peptide in a target protein comprising: establishing a reference database of T cell exposed motifs by assembling a database of reference proteins selected from immunoglobulin variable regions and immunoglobulin constant regions, wherein said T cell exposed motifs are the sub sets of amino acids in the peptide bound in a MHC molecule which are directed outwards and exposed to a T-cell binding to the complex of the peptide bound to an MHC molecule (pMHC), wherein said database is comprised of at least 40,000 immunoglobulin heavy chain variable regions (IGHV), extracting all sequential 9-mer and 15-mer peptide sequences from said reference proteins, identifying T-cell exposed motifs in said peptide sequences by a computer implemented algorithm, wherein the configuration of amino acids in the T-cell exposed motifs comprise the 2,3,5,7,8 positions of a 15 amino acid peptide binding in a MHC-II groove which is numbered from -3 to +3, the -1,3,5,7,8 positions of a 15 amino acid peptide binding in a MHC-II groove which is numbered from -3 to +3, and the 4,5,6,7,8 positions of a 9 amino acid peptide binding in a MHC-I groove; and categorizing said T-cell exposed motifs based on the frequency of occurrence of said T-cell exposed motifs in said reference database of reference proteins, and determining the relative frequency of each T-cell exposed motif and assigning a frequency category to each T-cell exposed motif using a logarithmic frequency categorization scheme; extracting peptide sequences from said target protein, identifying T-cell exposed motifs in said peptide sequences from said target protein by a computer implemented algorithm; and comparing T cell exposed motifs from said target protein to the frequency of occurrence of the same T cell motifs in said reference database and assigning a frequency category for each T cell exposed motif in the target protein. establishing a reference database of T cell exposed motifs by assembling a database of reference proteins selected from immunoglobulin variable regions and immunoglobulin constant regions, wherein said T cell exposed motifs are the sub sets of amino acids in the peptide bound in a MHC molecule which are directed outwards and exposed to a T-cell binding to the complex of the peptide bound to an MHC molecule (pMHC), wherein said database is comprised of at least 40,000 immunoglobulin heavy chain variable regions (IGHV), extracting all sequential 9-mer and 15-mer peptide sequences from said reference proteins, identifying T-cell exposed motifs in said peptide sequences by a computer implemented algorithm, wherein the configuration of amino acids in the T-cell exposed motifs comprise the 2,3,5,7,8 positions of a 15 amino acid peptide binding in a MHC-II groove which is numbered from -3 to +3, the -1,3,5,7,8 positions of a 15 amino acid peptide binding in a MHC-II groove which is numbered from -3 to +3, and the 4,5,6,7,8 positions of a 9 amino acid peptide binding in a MHC-I groove; and categorizing said T-cell exposed motifs based on the frequency of occurrence of said T-cell exposed motifs in said reference database of reference proteins, and determining the relative frequency of each T-cell exposed motif and assigning a frequency category to each T-cell exposed motif using a logarithmic frequency categorization scheme; extracting peptide sequences from said target protein, identifying T-cell exposed motifs in said peptide sequences from said target protein by a computer implemented algorithm; and comparing T cell exposed motifs from said target protein to the frequency of occurrence of the same T cell motifs in said reference database and assigning a frequency category for each T cell exposed motif in the target protein. <EOS>