Dermot H Mallon1, J Andrew Bradley, Peter J Winn, Craig J Taylor, Vasilis Kosmoliaptsis. 1. 1 Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom. 2 School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom. 3 Tissue Typing Laboratory, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom.
Abstract
BACKGROUND: We have previously shown that qualitative assessment of surface electrostatic potential of HLA class I molecules helps explain serological patterns of alloantibody binding. We have now used a novel computational approach to quantitate differences in surface electrostatic potential of HLA B-cell epitopes and applied this to explain HLA Bw4 and Bw6 antigenicity. METHODS: Protein structure models of HLA class I alleles expressing either the Bw4 or Bw6 epitope (defined by sequence motifs at positions 77 to 83) were generated using comparative structure prediction. The electrostatic potential in 3-dimensional space encompassing the Bw4/Bw6 epitope was computed by solving the Poisson-Boltzmann equation and quantitatively compared in a pairwise, all-versus-all fashion to produce distance matrices that cluster epitopes with similar electrostatics properties. RESULTS: Quantitative comparison of surface electrostatic potential at the carboxyl terminal of the α1-helix of HLA class I alleles, corresponding to amino acid sequence motif 77 to 83, produced clustering of HLA molecules in 3 principal groups according to Bw4 or Bw6 epitope expression. Remarkably, quantitative differences in electrostatic potential reflected known patterns of serological reactivity better than Bw4/Bw6 amino acid sequence motifs. Quantitative assessment of epitope electrostatic potential allowed the impact of known amino acid substitutions (HLA-B*07:02 R79G, R82L, G83R) that are critical for antibody binding to be predicted. CONCLUSIONS: We describe a novel approach for quantitating differences in HLA B-cell epitope electrostatic potential. Proof of principle is provided that this approach enables better assessment of HLA epitope antigenicity than amino acid sequence data alone, and it may allow prediction of HLA immunogenicity.
BACKGROUND: We have previously shown that qualitative assessment of surface electrostatic potential of HLA class I molecules helps explain serological patterns of alloantibody binding. We have now used a novel computational approach to quantitate differences in surface electrostatic potential of HLA B-cell epitopes and applied this to explain HLA Bw4 and Bw6 antigenicity. METHODS: Protein structure models of HLA class I alleles expressing either the Bw4 or Bw6 epitope (defined by sequence motifs at positions 77 to 83) were generated using comparative structure prediction. The electrostatic potential in 3-dimensional space encompassing the Bw4/Bw6 epitope was computed by solving the Poisson-Boltzmann equation and quantitatively compared in a pairwise, all-versus-all fashion to produce distance matrices that cluster epitopes with similar electrostatics properties. RESULTS: Quantitative comparison of surface electrostatic potential at the carboxyl terminal of the α1-helix of HLA class I alleles, corresponding to amino acid sequence motif 77 to 83, produced clustering of HLA molecules in 3 principal groups according to Bw4 or Bw6 epitope expression. Remarkably, quantitative differences in electrostatic potential reflected known patterns of serological reactivity better than Bw4/Bw6 amino acid sequence motifs. Quantitative assessment of epitope electrostatic potential allowed the impact of known amino acid substitutions (HLA-B*07:02 R79G, R82L, G83R) that are critical for antibody binding to be predicted. CONCLUSIONS: We describe a novel approach for quantitating differences in HLA B-cell epitope electrostatic potential. Proof of principle is provided that this approach enables better assessment of HLA epitope antigenicity than amino acid sequence data alone, and it may allow prediction of HLA immunogenicity.
Authors: Cynthia S M Kramer; Johan Koster; Geert W Haasnoot; Dave L Roelen; Frans H J Claas; Sebastiaan Heidt Journal: HLA Date: 2020-04-13 Impact factor: 4.513