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 a...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
American Society for Biochemistry and Molecular Biology
2019
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| _version_ | 1797101736628846592 |
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| author | Alvarez, B Reynisson, B Barra, C Buus, S Ternette, N Connelley, T Andreatta, M Nielsen, M |
| author_facet | Alvarez, B Reynisson, B Barra, C Buus, S Ternette, N Connelley, T Andreatta, M Nielsen, M |
| author_sort | Alvarez, B |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T05:56:05Z |
| format | Journal article |
| id | oxford-uuid:ea8b71ce-217c-4008-b2a3-e194723c972c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:56:05Z |
| publishDate | 2019 |
| publisher | American Society for Biochemistry and Molecular Biology |
| record_format | dspace |
| spelling | oxford-uuid:ea8b71ce-217c-4008-b2a3-e194723c972c2022-03-27T11:03:08ZNNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictionsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ea8b71ce-217c-4008-b2a3-e194723c972cEnglishSymplectic Elements at OxfordAmerican Society for Biochemistry and Molecular Biology2019Alvarez, BReynisson, BBarra, CBuus, STernette, NConnelley, TAndreatta, MNielsen, MThe 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. |
| spellingShingle | Alvarez, B Reynisson, B Barra, C Buus, S Ternette, N Connelley, T Andreatta, M Nielsen, M NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions |
| title | NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions |
| title_full | NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions |
| title_fullStr | NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions |
| title_full_unstemmed | NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions |
| title_short | NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions |
| title_sort | nnalign ma mhc peptidome deconvolution for accurate mhc binding motif characterization and improved t cell epitope predictions |
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