T-cell receptor insights: Determinants of Major Histocompatibility Complex class I versus class II recognition

  • Marcus De Almeida Mendes
  • , Leila Chihab
  • , Jonas Birkelund Nilsson
  • , Lonneke Scheffer
  • , Morten Nielsen
  • , Bjoern Peters*
  • *Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

In this study, we analyzed large-scale T-cell receptor (TCR) sequence data to determine whether TCRs preferentially bind to major histocompatibility complex (MHC) class I (CD8+) or class II (CD4+) epitopes. Using the International ImMunoGeneTics information system numbering scheme, we identified specific positions with distinct amino acid enrichment for each MHC class and developed machine learning models for classification. While our frequency-based approach effectively differentiated MHC-I from MHC-II TCRs in cross-validation, performance declined when only beta chain data were used from real-world peripheral blood mononuclear cell samples. However, incorporating the TCR alpha chain significantly improved accuracy, emphasizing its importance for MHC recognition. Overall, we found that V-region loops can signal MHC class bias, aiding in immunotherapy design and TCR repertoire analysis, while highlighting the need for larger, more diverse datasets for reliable predictions.

Original languageEnglish
Article numbere70262
JournalProtein Science
Volume34
Issue number9
Number of pages13
ISSN0961-8368
DOIs
Publication statusPublished - 2025

Keywords

  • Computational biology
  • Immunoinformatics
  • Immunology
  • Machine learning
  • T-cell receptor

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