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NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions

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Standard

NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions. / Alvarez, Bruno; Reynisson, Birkir; Barra, Carolina; Buus, Søren; Ternette, Nicola; Connelley, Tim; Andreatta, Massimo; Nielsen, Morten.

I: Molecular and Cellular Proteomics, Bind 18, Nr. 12, 2019, s. 2459-2477.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Alvarez, B, Reynisson, B, Barra, C, Buus, S, Ternette, N, Connelley, T, Andreatta, M & Nielsen, M 2019, 'NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions', Molecular and Cellular Proteomics, bind 18, nr. 12, s. 2459-2477. https://doi.org/10.1074/mcp.TIR119.001658

APA

Alvarez, B., Reynisson, B., Barra, C., Buus, S., Ternette, N., Connelley, T., ... Nielsen, M. (2019). NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions. Molecular and Cellular Proteomics, 18(12), 2459-2477. https://doi.org/10.1074/mcp.TIR119.001658

Vancouver

Alvarez B, Reynisson B, Barra C, Buus S, Ternette N, Connelley T o.a. NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions. Molecular and Cellular Proteomics. 2019;18(12):2459-2477. https://doi.org/10.1074/mcp.TIR119.001658

Author

Alvarez, Bruno ; Reynisson, Birkir ; Barra, Carolina ; Buus, Søren ; Ternette, Nicola ; Connelley, Tim ; Andreatta, Massimo ; Nielsen, Morten. / NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions. I: Molecular and Cellular Proteomics. 2019 ; Bind 18, Nr. 12. s. 2459-2477.

Bibtex

@article{17a6e54b3a5744e893a590d0bd6cbf47,
title = "NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions",
abstract = "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-ofthe- 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-cellbased therapeutics.",
author = "Bruno Alvarez and Birkir Reynisson and Carolina Barra and S{\o}ren Buus and Nicola Ternette and Tim Connelley and Massimo Andreatta and Morten Nielsen",
year = "2019",
doi = "10.1074/mcp.TIR119.001658",
language = "English",
volume = "18",
pages = "2459--2477",
journal = "Molecular and Cellular Proteomics",
issn = "1535-9476",
publisher = "American Society for Biochemistry and Molecular Biology",
number = "12",

}

RIS

TY - JOUR

T1 - NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions

AU - Alvarez, Bruno

AU - Reynisson, Birkir

AU - Barra, Carolina

AU - Buus, Søren

AU - Ternette, Nicola

AU - Connelley, Tim

AU - Andreatta, Massimo

AU - Nielsen, Morten

PY - 2019

Y1 - 2019

N2 - 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-ofthe- 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-cellbased therapeutics.

AB - 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-ofthe- 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-cellbased therapeutics.

U2 - 10.1074/mcp.TIR119.001658

DO - 10.1074/mcp.TIR119.001658

M3 - Journal article

C2 - 31578220

AN - SCOPUS:85076061599

VL - 18

SP - 2459

EP - 2477

JO - Molecular and Cellular Proteomics

JF - Molecular and Cellular Proteomics

SN - 1535-9476

IS - 12

ER -

ID: 237106018