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The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.

Original publication

DOI

10.1074/mcp.tir119.001658

Type

Journal article

Journal

Molecular & cellular proteomics : MCP

Publication Date

12/2019

Volume

18

Pages

2459 - 2477

Addresses

Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina.

Keywords

Cell Line, Animals, Cattle, Humans, Peptides, Histocompatibility Antigens Class I, Histocompatibility Antigens Class II, Epitopes, T-Lymphocyte, Ligands, Computational Biology, Amino Acid Motifs, Protein Binding, Algorithms, Databases, Protein, Benchmarking, Mass Spectrometry, Datasets as Topic, Machine Learning