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. |
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| Authors: | Niemann, Matthias, Matern, Benedict M., Spierings, Eric |
| Source: | Frontiers in Immunology; 7/29/2022, Vol. 13, p1-13, 13p |
| Subject Terms: | HLA histocompatibility antigens, AMINO acid sequence, RECURRENT neural networks, SNOWFLAKES, PEPTIDES |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Niemann%2C+Matthias%22">Niemann, Matthias</searchLink><br /><searchLink fieldCode="AR" term="%22Matern%2C+Benedict+M%2E%22">Matern, Benedict M.</searchLink><br /><searchLink fieldCode="AR" term="%22Spierings%2C+Eric%22">Spierings, Eric</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Immunology; 7/29/2022, Vol. 13, p1-13, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22HLA+histocompatibility+antigens%22">HLA histocompatibility antigens</searchLink><br /><searchLink fieldCode="DE" term="%22AMINO+acid+sequence%22">AMINO acid sequence</searchLink><br /><searchLink fieldCode="DE" term="%22RECURRENT+neural+networks%22">RECURRENT neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22SNOWFLAKES%22">SNOWFLAKES</searchLink><br /><searchLink fieldCode="DE" term="%22PEPTIDES%22">PEPTIDES</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Immunology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fimmu.2022.937587 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: HLA histocompatibility antigens Type: general – SubjectFull: AMINO acid sequence Type: general – SubjectFull: RECURRENT neural networks Type: general – SubjectFull: SNOWFLAKES Type: general – SubjectFull: PEPTIDES Type: general Titles: – TitleFull: Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Niemann, Matthias – PersonEntity: Name: NameFull: Matern, Benedict M. – PersonEntity: Name: NameFull: Spierings, Eric IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 07 Text: 7/29/2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 16643224 Numbering: – Type: volume Value: 13 Titles: – TitleFull: Frontiers in Immunology Type: main |
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