NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions
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 antige...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/153404 |
| Acceso en línea: | http://hdl.handle.net/11336/153404 |
| Access Level: | acceso abierto |
| Palabra clave: | MHC Immunoinformatics Mass Spectrometry https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3 |
| Sumario: | 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 i) clustering peptides into individual specificities; ii) automatic annotation of each cluster to an MHC molecule; and iii) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse datasets, 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. |
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