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Machine learning algorithms for identifying peptides containing features that are positively associated with native or exogenous cell processing, transport and major histocompatibility complex (MHC) presentation
Machine learning algorithms for identifying peptides containing features that are positively associated with native or exogenous cell processing, transport and major histocompatibility complex (MHC) presentation
The present invention provides a method for identifying peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation. In particular, the invention/method controls for the influence of protein abundance, stability and HLA/MHC binding on processing and presentation, enabling a machine-learning algorithm or statistical inference model trained using the method to be applied to any test peptide regardless of its HLA/MHC restriction i.e. the algorithm operates in a HLA/MHC-agnostic manner. This is attained through the building of positive and negative data sets of peptide sequences (peptides identified or inferred from surface bound or secreted MHC/peptide complexes in the literature, and those which are not). Specifically, the positive and negative data sets comprise a multiplicity of pairings between individual entries, in which both sequences of a pair are of equal or similar length, and are derived from the same source protein, and/or have similar binding affinities, with respect to the HLA/MHC molecule from which the peptide of the positive peptide is restricted.
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