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1. (WO2017186959) MACHINE LEARNING ALGORITHM FOR IDENTIFYING PEPTIDES THAT CONTAIN FEATURES POSITIVELY ASSOCIATED WITH NATURAL ENDOGENOUS OR EXOGENOUS CELLULAR PROCESSING, TRANSPORTATION AND MAJOR HISTOCOMPATIBILITY COMPLEX (MHC) PRESENTATION

Pub. No.:    WO/2017/186959    International Application No.:    PCT/EP2017/060299
Publication Date: Fri Nov 03 00:59:59 CET 2017 International Filing Date: Sat Apr 29 01:59:59 CEST 2017
IPC: G06F 19/24
G06F 19/18
Applicants: ONCOIMMUNITY AS
Inventors: STRATFORD, Richard
CLANCY, Trevor
Title: MACHINE LEARNING ALGORITHM FOR IDENTIFYING PEPTIDES THAT CONTAIN FEATURES POSITIVELY ASSOCIATED WITH NATURAL ENDOGENOUS OR EXOGENOUS CELLULAR PROCESSING, TRANSPORTATION AND MAJOR HISTOCOMPATIBILITY COMPLEX (MHC) PRESENTATION
Abstract:
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.