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MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates
Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their id...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Biochemistry and Molecular Biology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411678/ https://www.ncbi.nlm.nih.gov/pubmed/35803561 http://dx.doi.org/10.1016/j.mcpro.2022.100266 |
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author | Declercq, Arthur Bouwmeester, Robbin Hirschler, Aurélie Carapito, Christine Degroeve, Sven Martens, Lennart Gabriels, Ralf |
author_facet | Declercq, Arthur Bouwmeester, Robbin Hirschler, Aurélie Carapito, Christine Degroeve, Sven Martens, Lennart Gabriels, Ralf |
author_sort | Declercq, Arthur |
collection | PubMed |
description | Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS(2)PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS(2)PIP was tailored toward tryptic peptides, we have here retrained MS(2)PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS(2)PIP models, DeepLC, and Percolator in one software package, MS(2)Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS(2)Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS(2)Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows. |
format | Online Article Text |
id | pubmed-9411678 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Society for Biochemistry and Molecular Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-94116782022-08-31 MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates Declercq, Arthur Bouwmeester, Robbin Hirschler, Aurélie Carapito, Christine Degroeve, Sven Martens, Lennart Gabriels, Ralf Mol Cell Proteomics Research Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS(2)PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS(2)PIP was tailored toward tryptic peptides, we have here retrained MS(2)PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS(2)PIP models, DeepLC, and Percolator in one software package, MS(2)Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS(2)Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS(2)Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows. American Society for Biochemistry and Molecular Biology 2022-07-06 /pmc/articles/PMC9411678/ /pubmed/35803561 http://dx.doi.org/10.1016/j.mcpro.2022.100266 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Declercq, Arthur Bouwmeester, Robbin Hirschler, Aurélie Carapito, Christine Degroeve, Sven Martens, Lennart Gabriels, Ralf MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates |
title | MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates |
title_full | MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates |
title_fullStr | MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates |
title_full_unstemmed | MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates |
title_short | MS(2)Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates |
title_sort | ms(2)rescore: data-driven rescoring dramatically boosts immunopeptide identification rates |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411678/ https://www.ncbi.nlm.nih.gov/pubmed/35803561 http://dx.doi.org/10.1016/j.mcpro.2022.100266 |
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