Disclosed is a method for identifying peptides, which are bound and / or presented by
MHC class I and / or
MHC class II molecules in an individual, where the method utilises a
transformer encoder-decoder model comprising an
artificial neural network (ANN) architecture, wherein the
transformer encoder receives as input MHC molecule
amino acid sequence of
a peptide:MHC data pair and outputs a high-dimensional representation of the MHC
amino acid sequence, wherein the
transformer decoder receives the high-dimensional representation of the MHC molecule
amino acid sequence and the
peptide amino acid sequence of
a peptide:MHC data pair with residue embedding and positional encoding as input and outputs a high-dimensional representation of the relationship between the MHC molecule amino acid sequence and the
peptide sequence, and wherein the output from the transformer decoder is used to calculate a
quantitative assessment of the ability of the MHC molecule to bind and / or present the
peptide. Also disclosed is a method of training the model, a computer or computer
system adapted to carry out the method, and a method of identifying MHC binding amino acid sequence derived from proteins.