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Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility

Histocompatibility in solid-organ transplantation has a strong impact on long-term graft survival. Although recent advances in matching of both B-cell epitopes and T-cell epitopes have improved understanding of allorecognition, the immunogenic determinants are still not fully understood. We hypothes...

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Autores principales: Niemann, Matthias, Matern, Benedict M., Spierings, Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372366/
https://www.ncbi.nlm.nih.gov/pubmed/35967374
http://dx.doi.org/10.3389/fimmu.2022.937587
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author Niemann, Matthias
Matern, Benedict M.
Spierings, Eric
author_facet Niemann, Matthias
Matern, Benedict M.
Spierings, Eric
author_sort Niemann, Matthias
collection PubMed
description Histocompatibility in solid-organ transplantation has a strong impact on long-term graft survival. Although recent advances in matching of both B-cell epitopes and T-cell epitopes have improved understanding of allorecognition, the immunogenic determinants are still not fully understood. We hypothesized that HLA solvent accessibility is allele-specific, thus supporting refinement of HLA B-cell epitope prediction. We developed a computational pipeline named Snowflake to calculate solvent accessibility of HLA Class I proteins for deposited HLA crystal structures, supplemented by constructed HLA structures through the AlphaFold protein folding predictor and peptide binding predictions of the APE-Gen docking framework. This dataset trained a four-layer long short-term memory bidirectional recurrent neural network, which in turn inferred solvent accessibility of all known HLA Class I proteins. We extracted 676 HLA Class-I experimental structures from the Protein Data Bank and supplemented it by 37 Class-I alleles for which structures were predicted. For each of the predicted structures, 10 known binding peptides as reported by the Immune Epitope DataBase were rendered into the binding groove. Although HLA Class I proteins predominantly are folded similarly, we found higher variation in root mean square difference of solvent accessibility between experimental structures of different HLAs compared to structures with identical amino acid sequence, suggesting HLA’s solvent accessible surface is protein specific. Hence, residues may be surface-accessible on e.g. HLA-A*02:01, but not on HLA-A*01:01. Mapping these data to antibody-verified epitopes as defined by the HLA Epitope Registry reveals patterns of (1) consistently accessible residues, (2) only subsets of an epitope’s residues being consistently accessible and (3) varying surface accessibility of residues of epitopes. Our data suggest B-cell epitope definitions can be refined by considering allele-specific solvent-accessibility, rather than aggregating HLA protein surface maps by HLA class or locus. To support studies on epitope analyses in organ transplantation, the calculation of donor-allele-specific solvent-accessible amino acid mismatches was implemented as a cloud-based web service.
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spelling pubmed-93723662022-08-13 Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility Niemann, Matthias Matern, Benedict M. Spierings, Eric Front Immunol Immunology Histocompatibility in solid-organ transplantation has a strong impact on long-term graft survival. Although recent advances in matching of both B-cell epitopes and T-cell epitopes have improved understanding of allorecognition, the immunogenic determinants are still not fully understood. We hypothesized that HLA solvent accessibility is allele-specific, thus supporting refinement of HLA B-cell epitope prediction. We developed a computational pipeline named Snowflake to calculate solvent accessibility of HLA Class I proteins for deposited HLA crystal structures, supplemented by constructed HLA structures through the AlphaFold protein folding predictor and peptide binding predictions of the APE-Gen docking framework. This dataset trained a four-layer long short-term memory bidirectional recurrent neural network, which in turn inferred solvent accessibility of all known HLA Class I proteins. We extracted 676 HLA Class-I experimental structures from the Protein Data Bank and supplemented it by 37 Class-I alleles for which structures were predicted. For each of the predicted structures, 10 known binding peptides as reported by the Immune Epitope DataBase were rendered into the binding groove. Although HLA Class I proteins predominantly are folded similarly, we found higher variation in root mean square difference of solvent accessibility between experimental structures of different HLAs compared to structures with identical amino acid sequence, suggesting HLA’s solvent accessible surface is protein specific. Hence, residues may be surface-accessible on e.g. HLA-A*02:01, but not on HLA-A*01:01. Mapping these data to antibody-verified epitopes as defined by the HLA Epitope Registry reveals patterns of (1) consistently accessible residues, (2) only subsets of an epitope’s residues being consistently accessible and (3) varying surface accessibility of residues of epitopes. Our data suggest B-cell epitope definitions can be refined by considering allele-specific solvent-accessibility, rather than aggregating HLA protein surface maps by HLA class or locus. To support studies on epitope analyses in organ transplantation, the calculation of donor-allele-specific solvent-accessible amino acid mismatches was implemented as a cloud-based web service. Frontiers Media S.A. 2022-07-29 /pmc/articles/PMC9372366/ /pubmed/35967374 http://dx.doi.org/10.3389/fimmu.2022.937587 Text en Copyright © 2022 Niemann, Matern and Spierings https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Niemann, Matthias
Matern, Benedict M.
Spierings, Eric
Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility
title Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility
title_full Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility
title_fullStr Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility
title_full_unstemmed Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility
title_short Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility
title_sort snowflake: a deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372366/
https://www.ncbi.nlm.nih.gov/pubmed/35967374
http://dx.doi.org/10.3389/fimmu.2022.937587
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