Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Kacen, Assaf; Javitt, Aaron; Kramer, Matthias P.; Morgenstern, David; Tsaban, Tomer; Shmueli, Merav D.; Teo, Guo Ci; Leprevost, Felipe da Veiga; Barnea, Eilon; Yu, Fengchao; Admon, Arie; Eisenbach, Lea; Samuels, Yardena; Schueler‐Furman, Ora; Levin, Yishai; Nesvizhskii, Alexey I.; Merbl, Yifat

doi:10.1038/s41587-022-01464-2

Cited by 46 publications

(37 citation statements)

References 91 publications

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“…The workflows used for each dataset are as follows: HLA immunopeptidome [56] ( nonspecific-HLA-C57 workflow), melanoma DIA data [63] with MSFragger-DIA ( DIA_SpecLib_Quant) and with DIA-Umpire ( DIA_DIA-Umpire_SpecLib_Quant ), HeLa timsTOF [2] ( Default ), single cell proteomics with nanoPOTS [64] ( Default ), single cell proteomics with DISCO [46] ( Default ), and secretome [66] ( Default ). The HLA workflow was revised to add “--mods M:15.9949” to the Philosopher filter to perform group-specific FDR estimation [77] using the following three categories: unmodified peptides, peptides with oxidized M only, and peptides with any other modification. The nanoPOTS data were analyzed in separate experiments based on the number of cells (1, 3, 10, or 50).…”

Section: Methodsmentioning

confidence: 99%

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

Yang

Teo

et al. 2022

Preprint

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Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

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Section: Methodsmentioning

confidence: 99%

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

Yang

Teo

et al. 2022

Preprint

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“…However, even highly distinctive peptides can be recognized by the same TCR with different conformations[64], indicating the limited efficacy of similarity searching in the discovery of cross-reactive peptides. In addition, a variety of post-translational modifications can reshape the peptides presented by MHC molecules and influence the T cell immune response[66], which further complicates TCR-pMHC recognition. To better understand and utilize T cell immune responses, the molecular basis underlying TCR-pMHC recognition remains to be comprehensively elucidated in the future, and further studies are necessary to unveil the association between cellular characteristics and TCR-pMHC interactions.…”

Section: Discussionmentioning

confidence: 99%

Section: Sars-cov-2 Epitope Nsp31790-1798 By Tcr-204mentioning

confidence: 99%

Discovering SARS-CoV-2 neoepitopes and the associated TCR-pMHC recognition mechanisms by combining single-cell sequencing, deep learning, and molecular dynamics simulation techniques

Song¹,

Xu²,

Shi³

et al. 2023

Preprint

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The molecular mechanisms underlying the recognition of epitopes by T cell receptors (TCRs) are critical for activating T cell immune responses and rationally designing TCR-based therapeutics. Single-cell sequencing techniques vastly boost the accumulation of TCR sequences, while the limitation of available TCR-pMHC structures hampers further investigations. In this study, we proposed a comprehensive strategy that incorporates structural information and single-cell sequencing data to investigate the epitope-recognition mechanisms of TCRs. By antigen specificity clustering, we mapped the epitope sequences between epitope-known and epitope unknown TCRs from COVID-19 patients. One reported SARS-CoV-2 epitope, NQKLIANQF (S919-927), was identified for a TCR expressed by 614 T cells (TCR-614). Epitope screening also identified a potential cross-reactive epitope, KLKTLVATA (NSP31790-1798), for a TCR expressed by 204 T cells (TCR-204). According to the molecular dynamics (MD) simulations, we revealed the detailed epitope-recognition mechanisms for both TCRs. The structural motifs responsible for epitope recognition revealed by the MD simulations are consistent with the sequential features recognized by the sequence-based clustering method. This strategy will facilitate the discovery and optimization of TCR-based therapeutics. In addition, the comprehensive strategy can also promote the development of cancer vaccines in virtue of the ability to discover neoepitopes and epitope-recognition mechanisms.

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“…Post-translational modifications increase the diversity of the immunopeptidome and may provide new targets for the immune system to recognize tumor cells or respond to pathogens. With PTM-driven antigenicity being continuously highlighted 9,31,43,44 , glycosylation is a key PTM that, despite its long history of research, remains understudied in the context of MHC presentation due to computational related challenges. In this work, we have developed a workflow for glyco-immunopeptidomics that combines the speed and sensitivity of MSFragger-Glyco, with the inclusion of glycopeptide-specific FDR control in Philosopher, which is critical for filtering out low-confidence identifications.…”

Section: Discussionmentioning

confidence: 99%

HLA-Glyco: A large-scale interrogation of the glycosylated immunopeptidome

Bedran

Polasky

Hsiao

et al. 2022

Preprint

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MHC-associated peptides (MAPs) bearing post-translational modifications (PTMs) have raised intriguing questions regarding their attractiveness for targeted therapies. Here, we developed a novel computational glyco-immunopeptidomics workflow that integrates the ultrafast glycopeptide search of MSFragger with a glycopeptide-focused false discovery rate (FDR) control. We performed a harmonized analysis of 8 large-scale publicly available studies and found that glycosylated MAPs are predominantly presented by the MHC class II. We created HLA-Glyco, a resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. Our comprehensive resource reveals high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between classical HLA class II allele groups. To support the nascent field of glyco-immunopeptidomics, we include the optimized workflow in the FragPipe suite and provide HLA-Glyco as a free web resource.

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Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Cited by 46 publications

References 91 publications

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

Discovering SARS-CoV-2 neoepitopes and the associated TCR-pMHC recognition mechanisms by combining single-cell sequencing, deep learning, and molecular dynamics simulation techniques

HLA-Glyco: A large-scale interrogation of the glycosylated immunopeptidome

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