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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...

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Autores principales: Declercq, Arthur, Bouwmeester, Robbin, Hirschler, Aurélie, Carapito, Christine, Degroeve, Sven, Martens, Lennart, Gabriels, Ralf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Biochemistry and Molecular Biology 2022
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.
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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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