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雪花:一种基于深度学习的考虑等位基因特异性表面可及性的人类白细胞抗原匹配算法。

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

机构信息

Research and Development, PIRCHE AG, Berlin, Germany.

Center for Translational Immunology, University Medical Center, Utrecht, Netherlands.

出版信息

Front Immunol. 2022 Jul 29;13:937587. doi: 10.3389/fimmu.2022.937587. eCollection 2022.

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-A02:01, but not on HLA-A01:01. 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.

摘要

实体器官移植中的组织相容性对长期移植物存活率有重大影响。尽管 B 细胞表位和 T 细胞表位匹配的最新进展提高了对同种异体识别的理解,但免疫原性决定因素仍未完全了解。我们假设 HLA 溶剂可及性具有等位基因特异性,从而支持 HLA B 细胞表位预测的细化。我们开发了一个名为 Snowflake 的计算管道,用于计算已存入 HLA 晶体结构的 HLA 类 I 蛋白的溶剂可及性,并通过 AlphaFold 蛋白质折叠预测器补充构建的 HLA 结构,以及 APE-Gen 对接框架的肽结合预测。该数据集训练了一个四层长短期记忆双向递归神经网络,该网络反过来推断了所有已知 HLA 类 I 蛋白的溶剂可及性。我们从蛋白质数据库中提取了 676 个 HLA 类 I 实验结构,并补充了 37 个预测结构的 HLA 等位基因。对于每个预测结构,我们将免疫表位数据库报告的 10 个已知结合肽渲染到结合槽中。尽管 HLA 类 I 蛋白主要折叠方式相似,但我们发现不同 HLA 的实验结构之间溶剂可及性的均方根差差异较大,而具有相同氨基酸序列的结构差异较小,这表明 HLA 的溶剂可及表面是特定于蛋白质的。因此,例如,残基可能在 HLA-A02:01 上是表面可及的,但不在 HLA-A01:01 上。将这些数据映射到抗体验证的表位上,如 HLA 表位注册中心所定义的,揭示了(1)一致可及残基、(2)表位的仅部分残基一致可及和(3)表位残基的可变表面可及性的模式。我们的数据表明,可以通过考虑等位基因特异性溶剂可及性来细化 B 细胞表位定义,而不是通过 HLA 类别或基因座来聚合 HLA 蛋白表面图谱。为了支持器官移植中表位分析的研究,实现了作为基于云的 Web 服务的供体等位基因特异性溶剂可及性氨基酸错配的计算。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a293/9372366/26b43200f5c6/fimmu-13-937587-g001.jpg

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