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MS2Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates
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DOI：10.1016/j.mcpro.2022.100266.png)
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Abstract
En 中文
Immunopeptidomics aims to identify major histocompati-bility 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 immuno-peptides, complicating their identification. Previously, peak intensity predictions by (MSPIP)-P-2 and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as (MSPIP)-P-2 was tailored toward tryptic peptides, we have here retrained (MSPIP)-P-2 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 (MSPIP)-P-2 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, MS2Rescore thus allows sub-stantially improved identification of novel epitopes from existing immunopeptidomics workflows.
Keywords:
MASS-SPECTROMETRY
PREDICTION
(MSPIP)-P-2
PLATFORM
TANDEM
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