Evidence map›Paper›PMID 35154160›Full record

ArticleFrontiers in immunology2022

Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome.

Saghar Kaabinejadian, Carolina Barra, Bruno Alvarez, Hooman Yari, William H Hildebrand, Morten Nielsen

Abstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

75 citing papers in PubMed.

  1. Article
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  4. Skewed MHC Class I Peptide Presentation in Psoriatic Arthritis.International journal of molecular sciences · 2026
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  13. Comprehensive mapping of human CD4Cell reports. Medicine · 2026
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15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Saghar KaabinejadianPure MHC, LLC., Oklahoma City, OK, United States.
Carolina BarraDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Bruno AlvarezInstituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina.
Hooman YariDepartment of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
William H HildebrandDepartment of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
Morten NielsenDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology, next-generation vaccine development and protein-drug immunogenicity assessment. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that deal with these challenges effectively and guide the analysis and interpretation of this complex type of data is currently a major bottleneck for the large-scale application of this technique. To resolve this, we here present the MHCMotifDecon that benefits from state-of-the-art HLA class-I and class-II predictions to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely HLA molecule, allowing to identify and characterize HLA binding motifs while discarding co-purified contaminants. We have benchmarked the tool against other state-of-the-art methods and illustrated its application on experimental datasets for HLA-DR demonstrating a previously underappreciated role for HLA-DRB3/4/5 molecules in defining HLA class II immune repertoires. With its ease of use, MHCMotifDecon can efficiently guide interpretation of immunopeptidome datasets, serving the discovery of novel T cell targets. MHCMotifDecon is available at https://services.healthtech.dtu.dk/service.php?MHCMotifDecon-1.0.

Indexed as

Cell LineDatabases, ProteinEpitopes, T-LymphocyteHistocompatibility Antigens Class IHistocompatibility Antigens Class IIHLA-DR AntigensHumansLigandsMass SpectrometryPeptidesProtein BindingEpitopes, T-LymphocyteHistocompatibility Antigens Class IHistocompatibility Antigens Class IIHLA-DR AntigensLigandsPeptidesDRB3/4/5immunopeptidomemass spectrometryMHCMotifDeconMHC motif deconvolution

Identifiers

PMID35154160
PMCPMC8826445

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.