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

  • Nicolas De Neuter
  • Wout Bittremieux
  • Charlie Beirnaert
  • Bart Cuypers
  • Aida Mrzic
  • Pieter Moris
  • Arvid Suls
  • Viggo Van Tendeloo
  • Benson Ogunjimi
  • Kris Laukens
  • Pieter Meysman
Original Article

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

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

Supplementary material

251_2017_1023_MOESM1_ESM.csv (10 kb)
Online resource 1List of TCRβs with MHC-epitope association used during training. TCRβs listed are either HLA-B*08-EIYKRWII or HLA-B*08-FLKEKGGL restricted. The list contains 4 columns describing respectively: V-family/gene region number, CDR3 amino acid sequence, J-family/gene region number and the target epitope. (CSV 10 kb)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Nicolas De Neuter
    • 1
    • 2
    • 3
  • Wout Bittremieux
    • 1
    • 2
  • Charlie Beirnaert
    • 1
    • 2
  • Bart Cuypers
    • 1
    • 2
    • 4
  • Aida Mrzic
    • 1
    • 2
  • Pieter Moris
    • 1
    • 2
  • Arvid Suls
    • 3
    • 5
    • 6
  • Viggo Van Tendeloo
    • 3
    • 7
  • Benson Ogunjimi
    • 3
    • 7
    • 8
    • 9
    • 10
  • Kris Laukens
    • 1
    • 2
    • 3
  • Pieter Meysman
    • 1
    • 2
    • 3
  1. 1.Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer ScienceUniversity of AntwerpAntwerpBelgium
  2. 2.Biomedical Informatics Research Network Antwerp (biomina)University of AntwerpAntwerpBelgium
  3. 3.AUDACIS, Antwerp Unit for Data Analysis and Computation in Immunology and SequencingUniversity of AntwerpAntwerpBelgium
  4. 4.Molecular Parasitology Unit, Department of Biomedical SciencesInstitute of Tropical MedicineAntwerpBelgium
  5. 5.GENOMED, Center for Medical GeneticsUniversity of AntwerpEdegemBelgium
  6. 6.Center for Medical GeneticsAntwerp University HospitalEdegemBelgium
  7. 7.LEH, Laboratory of Experimental Hematology, Vaccine & Infectious Disease Institute (VAXINFECTIO)University of AntwerpAntwerpBelgium
  8. 8.Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO)University of AntwerpAntwerpBelgium
  9. 9.Department of Paediatric Nephrology and RheumatologyGhent University HospitalGhentBelgium
  10. 10.Department of PaediatricsAntwerp University HospitalEdegemBelgium

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