Evidence map›Paper›PMID 30399156›Full record

ArticlePloS one2018

Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition.

Dina Schneidman-Duhovny, Natalia Khuri, Guang Qiang Dong, Michael B Winter, Eric Shifrut, Nir Friedman, Charles S Craik, Kathleen P Pratt, Pedro Paz, Fred Aswad and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
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

21 citing papers in PubMed, 37 citations in OpenAlex.

  1. Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026
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  11. Revisiting the Principles of Designing a Vaccine.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
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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

11 authors at 5 institutions in 2 countries.

Dina Schneidman-DuhovnyDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.ORCID 0000-0003-0480-0438
Natalia KhuriDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.
Guang Qiang DongDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.
Michael B WinterDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America.
Eric ShifrutDepartment of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Nir FriedmanDepartment of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Charles S CraikDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America.
Kathleen P PrattUniformed Services University of the Health Sciences, Bethesda, MD, United States of America.
Pedro PazBayer HealthCare, San Francisco, CA, United States of America.
Fred AswadBayer HealthCare, San Francisco, CA, United States of America.ORCID 0000-0001-9062-3735
Andrej SaliDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.
University of California, San Francisco · USBayer (United States) · USWeizmann Institute of Science · ILQB3 · USUniformed Services University of the Health Sciences · US

Funding

Mechanisms of Race-Based Differences in Factor VIII Immunogenicity in HemophiliaRC2HL101851 · NHLBI · SEPULVEDA RESEARCH CORPORATION · PI HOWARD, TOM EUGENE, PRATT, KATHLEEN PALMER · 2009 to 2010
$6.5M
Bio-Organic Biomedical Mass Spectromy ResourceP41GM103481 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BURLINGAME, ALMA L · 2012 to 2015
$6.4M
IMP: Software for Hybrid Determination of Macromolecular Assembly StructuresR01GM083960 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SALI, ANDREJ · 2008 to 2024
$5.2M
Design of Less Immunogenic Factor VIII ProteinsR01HL130448 · NHLBI · HENRY M. JACKSON FDN FOR THE ADV MIL/MED · PI PRATT, KATHLEEN PALMER · 2016 to 2019
$1.5M
Extracellular Proteolysis as a Molecular Stratification Tool for CancerR21CA186077 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CRAIK, CHARLES SCOTT · 2014 to 2015
$370k
Profiling the Role of Protease Activity in Cancer ProgressionF32CA168150 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WINTER, MICHAEL B. · 2012 to 2014
$136k
NCI NIH HHS F32 CA168150NCI NIH HHS R21 CA186077NHLBI NIH HHS R01 HL130448NHLBI NIH HHS RC2 HL101851NIGMS NIH HHS P41 GM103481NIGMS NIH HHS R01 GM083960
6 · The paper itself

Abstract

Accurate predictions of T-cell epitopes would be useful for designing vaccines, immunotherapies for cancer and autoimmune diseases, and improved protein therapies. The humoral immune response involves uptake of antigens by antigen presenting cells (APCs), APC processing and presentation of peptides on MHC class II (pMHCII), and T-cell receptor (TCR) recognition of pMHCII complexes. Most in silico methods predict only peptide-MHCII binding, resulting in significant over-prediction of CD4 T-cell epitopes. We present a method, ITCell, for prediction of T-cell epitopes within an input protein antigen sequence for given MHCII and TCR sequences. The method integrates information about three stages of the immune response pathway: antigen cleavage, MHCII presentation, and TCR recognition. First, antigen cleavage sites are predicted based on the cleavage profiles of cathepsins S, B, and H. Second, for each 12-mer peptide in the antigen sequence we predict whether it will bind to a given MHCII, based on the scores of modeled peptide-MHCII complexes. Third, we predict whether or not any of the top scoring peptide-MHCII complexes can bind to a given TCR, based on the scores of modeled ternary peptide-MHCII-TCR complexes and the distribution of predicted cleavage sites. Our benchmarks consist of epitope predictions generated by this algorithm, checked against 20 peptide-MHCII-TCR crystal structures, as well as epitope predictions for four peptide-MHCII-TCR complexes with known epitopes and TCR sequences but without crystal structures. ITCell successfully identified the correct epitopes as one of the 20 top scoring peptides for 22 of 24 benchmark cases. To validate the method using a clinically relevant application, we utilized five factor VIII-specific TCR sequences from hemophilia A subjects who developed an immune response to factor VIII replacement therapy. The known HLA-DR1-restricted factor VIII epitope was among the six top-scoring factor VIII peptides predicted by ITCall to bind HLA-DR1 and all five TCRs. Our integrative approach is more accurate than current single-stage epitope prediction algorithms applied to the same benchmarks. It is freely available as a web server (http://salilab.org/itcell).

Indexed as

Antigen PresentationModels, ImmunologicalAlgorithmsAntigensCathepsinsCD4-Positive T-LymphocytesComputer SimulationEpitopes, T-LymphocyteFactor VIIIHemophilia AHistocompatibility Antigens Class IIHumansProtein Structure, TertiaryReceptors, Antigen, T-CellAntigensCathepsinsEpitopes, T-LymphocyteFactor VIIIHistocompatibility Antigens Class IIReceptors, Antigen, T-Cell

Identifiers

PMID30399156
PMCPMC6219782
OpenAlexW2950352272

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.