Evidence map›Paper›PMID 26783342›Full record

ArticleJournal of immunology (Baltimore, Md. : 1950)2016

The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference.

Thomas Trolle, Curtis P McMurtrey, John Sidney, Wilfried Bardet, Sean C Osborn, Thomas Kaever, Alessandro Sette, William H Hildebrand, Morten Nielsen, Bjoern Peters

Abstract read
In one paragraph

Article in Journal of immunology (Baltimore, Md. : 1950), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
144citing papers in PubMed, 1 pooled it
–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

144 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Machine learning-based prediction of cross-immunity.Briefings in bioinformatics · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Benchmarking Software for DDA-PASEF Immunopeptidomics.Molecular & cellular proteomics : MCP · 2026
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  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

84 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

10 authors.

Thomas TrolleDepartment of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark;ORCID http://orcid.org/0000-0003-0762-2198
Curtis P McMurtreyDepartment of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104;
John SidneyDivision of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037; and.ORCID http://orcid.org/0000-0002-0987-405X
Wilfried BardetDepartment of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104;
Sean C OsbornDepartment of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104;
Thomas KaeverDivision of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037; and.ORCID http://orcid.org/0000-0003-4709-6356
Alessandro SetteDivision of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037; and.
William H HildebrandDepartment of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104;
Morten NielsenDepartment of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark; Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, B 1650 HMP Buenos Aires, Argentina.
Bjoern PetersDivision of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037; and bpeters@lji.org.

Funding

NIAID NIH HHS HHSN272201200010CPHS HHS HHSN272201200010C
6 · The paper itself

Abstract

HLA class I-binding predictions are widely used to identify candidate peptide targets of human CD8(+) T cell responses. Many such approaches focus exclusively on a limited range of peptide lengths, typically 9 aa and sometimes 9-10 aa, despite multiple examples of dominant epitopes of other lengths. In this study, we examined whether epitope predictions can be improved by incorporating the natural length distribution of HLA class I ligands. We found that, although different HLA alleles have diverse length-binding preferences, the length profiles of ligands that are naturally presented by these alleles are much more homogeneous. We hypothesized that this is due to a defined length profile of peptides available for HLA binding in the endoplasmic reticulum. Based on this, we created a model of HLA allele-specific ligand length profiles and demonstrate how this model, in combination with HLA-binding predictions, greatly improves comprehensive identification of CD8(+) T cell epitopes.

Indexed as

Genes, MHC Class IAllelesAnimalsCD8-Positive T-LymphocytesEpitope MappingEpitopes, T-LymphocyteHeLa CellsHLA-A AntigensHLA-B AntigensHumansImmunodominant EpitopesLigandsPeptidesProtein BindingEpitopes, T-LymphocyteHLA-A AntigensHLA-B AntigensImmunodominant EpitopesLigandsPeptides

Identifiers

PMID26783342
PMCPMC4744552

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Registered trials

None linked

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.