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Immunogenetics. 2018 Mar;70(3):159-168. doi: 10.1007/s00251-017-1023-5. Epub 2017 Aug 4.

On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition.

De Neuter N1,2,3, Bittremieux W1,2, Beirnaert C1,2, Cuypers B1,2,4, Mrzic A1,2, Moris P1,2, Suls A3,5,6, Van Tendeloo V3,7, Ogunjimi B3,7,8,9,10, Laukens K11,12,13, Meysman P1,2,3.

Author information

1
Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.
2
Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium.
3
AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium.
4
Molecular Parasitology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
5
GENOMED, Center for Medical Genetics, University of Antwerp, Edegem, Belgium.
6
Center for Medical Genetics, Antwerp University Hospital, Edegem, Belgium.
7
LEH, Laboratory of Experimental Hematology, Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.
8
Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.
9
Department of Paediatric Nephrology and Rheumatology, Ghent University Hospital, Ghent, Belgium.
10
Department of Paediatrics, Antwerp University Hospital, Edegem, Belgium.
11
Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium. kris.laukens@uantwerpen.be.
12
Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium. kris.laukens@uantwerpen.be.
13
AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium. kris.laukens@uantwerpen.be.

Abstract

Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.

KEYWORDS:

Bioinformatics; Immunoinformatics; Random forest classifier; T cell epitope prediction; T cell receptor

PMID:
28779185
DOI:
10.1007/s00251-017-1023-5
[Indexed for MEDLINE]

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