MAIN REFERENCE
NetMHCpanExp-1.0. The role of antigen expression in shaping the repertoire of HLA presented ligands.
Heli M. Garcia Alvarez 1, Zeynep Koşaloğlu-Yalçın 2, Bjoern Peters2,3, and Morten Nielsen1,4
1
Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, BA 16503, Argentina.
2
Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, California, USA
3
Department of Medicine, University of California, San Diego, La Jolla, California, USA
4
Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark.
In preparation (2022)
OTHER RELEVANT REFERENCES
NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions.
Bruno Alvarez 1, Birkir Reynisson 2, Carolina Barra 1, Søren Buus 3, Nicola Ternette 4, Tim Connelley 5, Massimo Andreatta 1, Morten Nielsen 1,2
1
Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, BA 16503, Argentina.
2
Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark.
3
Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, Denmark.
4
The Jenner Institute, Nuffield Department of Medicine, Oxford, United Kingdom.
5
Roslin Institute, Edinburgh, Midlothian, United Kingdom.
Mol Cell Proteomics (2019); 8(12):2459-2477. DOI: 10.1074/mcp.TIR119.001658. PMID: 31578220
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.