Abstract
Introduction: The HLA region is the hallmark of balancing selection, argued to be driven by the pressure to present a wide variety of viral epitopes. As such selection on the peptide-binding positions has been proposed to drive HLA population genetics. MHC molecules also directly binds to the T-Cell Receptor and killer cell immunoglobulin-like receptors (KIR). Methods: We here combine the HLA allele frequencies in over six-million Hematopoietic Stem Cells (HSC) donors with a novel machine-learning-based method to predict allele frequency. Results: We show for the first time that allele frequency can be predicted from their sequences. This prediction yields a natural measure for selection. The strongest selection is affecting KIR binding regions, followed by the peptide-binding cleft. The selection from the direct interaction with the KIR and TCR is centered on positively charged residues (mainly Arginine), and some positions in the peptide-binding cleft are not associated with the allele frequency, especially Tyrosine residues. Discussion: These results suggest that the balancing selection for peptide presentation is combined with a positive selection for KIR and TCR binding.
Original language | English |
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Article number | 1236080 |
Journal | Frontiers in Immunology |
Volume | 14 |
DOIs | |
State | Published - 2023 |
Bibliographical note
Publisher Copyright:Copyright © 2023 Levi, Levi and Louzoun.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work of RL and YL was funded by ISF grant 870/20, a Vatat DSI grant and an Israel MOH grant.
Funders | Funder number |
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Israel MOH | |
Vatat DSI | |
Israel Science Foundation | 870/20 |
Keywords
- Bw4
- HLA
- T cell receptor
- allele
- balancing
- machine learning
- selection