NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions

Journal article

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.

Authors: Alvarez, B; Reynisson, B; Barra, C; Buus, S; Ternette, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Society for Biochemistry and Molecular Biology
• Journal: Molecular and Cellular Proteomics
• Volume: 18
• Issue: 12
• Pages: 2459-2477
• Publication date: 2019-10-02
• Acceptance date: 2019-10-02
• DOI: 10.1074/mcp.TIR119.001658
• EISSN: 1535-9484
• ISSN: 1535-9476
• Pmid: 31578220
• Language: English
• Keywords: antigen presentation; immunology; machine learning; bioinformatics software; immunopeptidomics; mass spectrometry; bioinformatics; algorithms; FFR; immunoinformatics