A computer implemented process of identifying peptide ligands for a MHC-1 or MHC-II binding partner comprising: a) deriving a set of principal components for every amino acid that, as a set, provide a proxy descriptor of that amino acid, said principal components being derived by: i) assembling measurements of a plurality of physical properties of each amino acid, ii) creating a matrix of said measurements of physical properties and conducting linear transformations to derive multiple principal components, ranked and weighted according to how much of the variance of the amino acid each principal component represents, iii) selecting the ranked and weighted principal components that collectively represent a desired level of variance of the amino acids, and iv) providing that set of principal components as the descriptor of each amino acid; b) combining in sequence the principal component sets describing each of the multiple amino acids in a peptide to provide a vector descriptive of that peptide; c) producing a series of equations by neural network modeling that allow prediction of binding affinities to MHC-I and MHC-II binding partners, wherein the input layer of said neural network is a set of said vectors that are vectors that are descriptors of peptides of desired length, the output layer is LN(K d) for the binding of a peptide to the MHC-I or MHC-II binding partners, and wherein said neural network is trained using peptides with known binding affinities to said binding partners, thereby generating a series of equations within the neural net that predict binding affinities to MHC-I and MHC-II; d) in-putting a target amino acid sequence from a target source into a computer comprising said neural network; e) encoding subsets of amino acid sequences in said target amino acid sequence as a set of vectors, each of which comprises a series of principal components of the physical properties of the amino acids within the peptide; f) applying the equations derived by said trained neural network to predict the binding of said amino acid subsets from the target source to MHC-I and MHC-II binding partners; and g) outputting the predicted binding affinity of said amino acid subsets for said MHC-I and MHC-II binding partners. a) deriving a set of principal components for every amino acid that, as a set, provide a proxy descriptor of that amino acid, said principal components being derived by: i) assembling measurements of a plurality of physical properties of each amino acid, ii) creating a matrix of said measurements of physical properties and conducting linear transformations to derive multiple principal components, ranked and weighted according to how much of the variance of the amino acid each principal component represents, iii) selecting the ranked and weighted principal components that collectively represent a desired level of variance of the amino acids, and iv) providing that set of principal components as the descriptor of each amino acid; i) assembling measurements of a plurality of physical properties of each amino acid, ii) creating a matrix of said measurements of physical properties and conducting linear transformations to derive multiple principal components, ranked and weighted according to how much of the variance of the amino acid each principal component represents, iii) selecting the ranked and weighted principal components that collectively represent a desired level of variance of the amino acids, and iv) providing that set of principal components as the descriptor of each amino acid; b) combining in sequence the principal component sets describing each of the multiple amino acids in a peptide to provide a vector descriptive of that peptide; c) producing a series of equations by neural network modeling that allow prediction of binding affinities to MHC-I and MHC-II binding partners, wherein the input layer of said neural network is a set of said vectors that are vectors that are descriptors of peptides of desired length, the output layer is LN(K d) for the binding of a peptide to the MHC-I or MHC-II binding partners, and wherein said neural network is trained using peptides with known binding affinities to said binding partners, thereby generating a series of equations within the neural net that predict binding affinities to MHC-I and MHC-II; wherein the input layer of said neural network is a set of said vectors that are vectors that are descriptors of peptides of desired length, the output layer is LN(K d) for the binding of a peptide to the MHC-I or MHC-II binding partners, and wherein said neural network is trained using peptides with known binding affinities to said binding partners, thereby generating a series of equations within the neural net that predict binding affinities to MHC-I and MHC-II; d) in-putting a target amino acid sequence from a target source into a computer comprising said neural network; e) encoding subsets of amino acid sequences in said target amino acid sequence as a set of vectors, each of which comprises a series of principal components of the physical properties of the amino acids within the peptide; f) applying the equations derived by said trained neural network to predict the binding of said amino acid subsets from the target source to MHC-I and MHC-II binding partners; and g) outputting the predicted binding affinity of said amino acid subsets for said MHC-I and MHC-II binding partners.