Mechanistic diversity in MHC class I antigen recognition

Barbosa, Camila Raquel Rodrigues; Barton, Justin; Shepherd, Adrian J.; Mishto, Michele

doi:10.1042/bcj20200910

Cited by 13 publications

(10 citation statements)

References 161 publications

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“…CD8 + T cells patrol cells by scanning the sequence of peptides bound to Human Leucocyte Antigen class I (HLA‐I) complexes, which are present in thousands of different variants in the human population. The combination of binding affinity of peptides and HLA‐I variants as well as the avidity of T cell receptors αβ (TCRαβ) for peptide sequence is a highly efficient system to scan peptide‐HLA‐I complexes (Barbosa et al., 2021 ). Various techniques have been developed to identify peptides bound to HLA‐I complexes, that is, HLA‐I immunopeptidomes.…”

Section: Introductionmentioning

confidence: 99%

Database search engines and target database features impinge upon the identification of post‐translationally cis‐spliced peptides in HLA class I immunopeptidomes

Mishto

Horokhovskyi²,

Cormican³

et al. 2022

Proteomics

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Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post‐translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines – that is, Mascot, Mascot+Percolator, and PEAKS DB – as final identification step, as well as the features of target database on the ability to correctly identify non‐spliced and cis‐spliced peptides. We used ground truth datasets measured by MS to benchmark methods’ performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis‐spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.

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Section: Introductionmentioning

confidence: 99%

Database search engines and target database features impinge upon the identification of post‐translationally cis‐spliced peptides in HLA class I immunopeptidomes

Mishto

Horokhovskyi²,

Cormican³

et al. 2022

Proteomics

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“…These attempts assumed a similarity between the features of canonical and noncanonical peptides. In contrast, differences in the mechanism of production, features of the original proteins (among others) between canonical and noncanonical peptides have been hypothesized [3][4][5][15][16][17], and therefore this strategy could be biased. To tackle this issue, we could synthesize all noncanonical peptides identified in a proteomic sample, measure them via MS and compare the MS2 spectra of the peptides and synthetic peptides, which has only been undertaken rarely [18].…”

Section: Introductionmentioning

confidence: 99%

“…A large portion of noncanonical peptides may derive from putative non‐coding regions‐, for example, 5′‐UTR, 3′‐UTR, introns ‐ or alternative transcription and translation processes, such as alternative open reading frame (ORF) usage, alternative RNA splicing, and ribosomal frameshift. These peptides are defined as ‘cryptic’ [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

iBench: A ground truth approach for advanced validation of mass spectrometry identification method

Cormican¹,

Soh²,

Mishto

et al. 2022

Proteomics

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The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof‐of‐concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator).

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“…Infectious diseases in particular are thought to be a major source of selective pressure on the Major Histocompatibility Complex (MHC) region which encodes HLA alleles and is one of the most diverse regions of the human genome (2)(3)(4)(5)(6)(7)(8). There is large diversity in the antigenic peptide sequences which individual HLA alleles can recognize and ultimately present to the adaptive immune system (9), with a positive correlation between increased sequence diversity recognition and fitness (10).…”

Section: Introductionmentioning

confidence: 99%

Discordant results among MHC binding affinity prediction tools

Nguyen

Nellore

Thompson

2022

Preprint

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A large number of machine learning-based Major Histocompatibility Complex (MHC) binding affinity (BA) prediction tools have been developed and are widely used for both investigational and therapeutic applications, so it is important to explore differences in tool outputs. We examined predictions of four popular tools (netMHCpan, HLAthena, MHCflurry, and MHCnuggets) across a range of possible peptide sources (human, viral, and randomly generated) and MHC class I alleles. We uncovered inconsistencies in predictions of BA, allele promiscuity and the relationship between physical properties of peptides by source and BA predictions, as well as quality of training data. Our work raises fundamental questions about the fidelity of peptide-MHC binding prediction tools and their real-world implications.

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Mechanistic diversity in MHC class I antigen recognition

Cited by 13 publications

References 161 publications

Database search engines and target database features impinge upon the identification of post‐translationally cis‐spliced peptides in HLA class I immunopeptidomes

Database search engines and target database features impinge upon the identification of post‐translationally cis‐spliced peptides in HLA class I immunopeptidomes

iBench: A ground truth approach for advanced validation of mass spectrometry identification method

Discordant results among MHC binding affinity prediction tools

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