Artículo
Netmhcpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data
Fecha de publicación:
11/2017
Editorial:
American Association of Immunologists
Revista:
Journal of Immunology
ISSN:
0022-1767
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.
Palabras clave:
Mhc
,
Ligands
,
Epitopes
,
Machine Learning
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Articulos(IIB-INTECH)
Articulos de INST.DE INVEST.BIOTECNOLOGICAS - INSTITUTO TECNOLOGICO CHASCOMUS
Articulos de INST.DE INVEST.BIOTECNOLOGICAS - INSTITUTO TECNOLOGICO CHASCOMUS
Citación
Jurtz, Vanessa; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; et al.; Netmhcpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data; American Association of Immunologists; Journal of Immunology; 199; 9; 11-2017; 3360-3368
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