Pitfalls in HLA Ligandomics—How to Catch a Li(e)gand

Fritsche, Jens; Kowalewski, Daniel J.; Backert, Linus; Gwinner, Frederik; Dorner, Sonja; Priemer, Martin; Tsou, Chih-Chiang; Hoffgaard, Franziska; Roemer, Michael; Schoor, Oliver; Weinschenk, Toni

doi:10.1016/j.mcpro.2021.100110

Cited by 30 publications

(36 citation statements)

References 37 publications

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“…In the second QC study, Fritsche et al. ( 38 ) presented (i) statistics to enable discrimination of true HLA ligands from coisolated HLA-independent proteolytic fragments, (ii) the necessary steps to ensure system suitability of the chromatographic system, (iii) an algorithm for detection of source fragmentation events that are introduced by electrospray ionization during MS, and (iv) an experimental pipeline that enables high-throughput sequence verification through similarity of fragmentation patterns and coelution of synthetic isotope-labeled internal standards. Akin to MS-based proteomics, such QC approaches are useful to show the overall quality of immunopeptidomic datasets in additional to point out limitations and pitfalls that are critical for individual peptides.…”

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confidence: 99%

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MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Datasets

Kovalchik

Wessling

et al. 2022

Molecular & Cellular Proteomics

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MS-based immunopeptidomics is maturing into an automatized and high-throughput technology, producing small- to large-scale datasets of clinically relevant major histocompatibility complex (MHC) class I-associated and class II-associated peptides. Consequently, the development of quality control (QC) and quality assurance systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semiautomated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition, and MHC specificity to greatly accelerate the “pass–fail” QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan, and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community.

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confidence: 99%

“…To date, only two studies have focused on QC measures to validate the quality of immunopeptidomic data generated by MS ( 37 , 38 ). In the first study, Ghosh et al.…”

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confidence: 99%

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Datasets

Kovalchik

Wessling

et al. 2022

Molecular & Cellular Proteomics

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“…As mentioned by Admon in his perspective ( 23 ), a solution could be the comparison of all identified peptides of HLA-I immunopeptidomes with heavy stable isotope-label synthetic peptides. Although ideal, this solution is financially impractical, and, indeed, it has only been applied on target unconventional peptides (or small pools of unconventional peptides) in HLA-I immunopeptidomes ( 31 ). An alternative strategy could be studying PCPS in a simpler system, such as in vitro digestions of synthetic polypeptide substrates by purified proteasomes, measured by mass spectrometry.…”

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Commentary: Are There Indeed Spliced Peptides in the Immunopeptidome?

Mishto

2021

Molecular & Cellular Proteomics

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Hence, downstream analyses of HLA peptidomics often involve HLA binding affinity predictions by artificial neural network models to screen for peptides with strong bindings. Furthermore, like most mass spectrometry analyses, results from HLA peptidomics can include contaminants such as carry-over peptides and non-HLA-specific proteolytic peptides or artifacts from in-source fragmentations 19,20 . A recent study has proposed additional analysis steps that would help reduce the number of contaminant identifications originating from these sources 20 .…”

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confidence: 99%

Unsupervised mining of HLA-I peptidomes reveals new binding motifs and substantial false positives in community database

Sricharoensuk

Boonchalermvichien

Muanwien

et al. 2021

Preprint

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Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.

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Pitfalls in HLA Ligandomics—How to Catch a Li(e)gand

Cited by 30 publications

References 37 publications

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Datasets

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Datasets

Commentary: Are There Indeed Spliced Peptides in the Immunopeptidome?

Unsupervised mining of HLA-I peptidomes reveals new binding motifs and substantial false positives in community database

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