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抗体库的结构映射

Structurally Mapping Antibody Repertoires.

作者信息

Krawczyk Konrad, Kelm Sebastian, Kovaltsuk Aleksandr, Galson Jacob D, Kelly Dominic, Trück Johannes, Regep Cristian, Leem Jinwoo, Wong Wing K, Nowak Jaroslaw, Snowden James, Wright Michael, Starkie Laura, Scott-Tucker Anthony, Shi Jiye, Deane Charlotte M

机构信息

Department of Statistics, Oxford University, Oxford, United Kingdom.

UCB Pharma, Slough, United Kingdom.

出版信息

Front Immunol. 2018 Jul 23;9:1698. doi: 10.3389/fimmu.2018.01698. eCollection 2018.

DOI:10.3389/fimmu.2018.01698
PMID:30083160
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6064724/
Abstract

Every human possesses millions of distinct antibodies. It is now possible to analyze this diversity next-generation sequencing of immunoglobulin genes (Ig-seq). This technique produces large volume sequence snapshots of B-cell receptors that are indicative of the antibody repertoire. In this paper, we enrich these large-scale sequence datasets with structural information. Enriching a sequence with its structural data allows better approximation of many vital features, such as its binding site and specificity. Here, we describe the structural annotation of antibodies pipeline that maps the outputs of large Ig-seq experiments to known antibody structures. We demonstrate the viability of our protocol on five separate Ig-seq datasets covering ca. 35 m unique amino acid sequences from ca. 600 individuals. Despite the great theoretical diversity of antibodies, we find that the majority of sequences coming from such studies can be reliably mapped to an existing structure.

摘要

每个人都拥有数百万种不同的抗体。现在可以通过免疫球蛋白基因的下一代测序(Ig-seq)来分析这种多样性。该技术能生成大量B细胞受体的序列快照,这些快照可指示抗体库。在本文中,我们利用结构信息丰富这些大规模序列数据集。用结构数据丰富序列能更好地近似许多重要特征,比如其结合位点和特异性。在此,我们描述了抗体结构注释流程,该流程将大型Ig-seq实验的输出结果映射到已知的抗体结构上。我们在五个独立的Ig-seq数据集上证明了我们方案的可行性,这些数据集涵盖了约600个人的约3500万个独特氨基酸序列。尽管抗体在理论上具有极大的多样性,但我们发现,来自此类研究的大多数序列都能可靠地映射到现有的结构上。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dc8/6064724/d117b3901323/fimmu-09-01698-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dc8/6064724/33d4b48a00d9/fimmu-09-01698-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dc8/6064724/2c6da9e660c2/fimmu-09-01698-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dc8/6064724/d117b3901323/fimmu-09-01698-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dc8/6064724/33d4b48a00d9/fimmu-09-01698-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dc8/6064724/2c6da9e660c2/fimmu-09-01698-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dc8/6064724/d117b3901323/fimmu-09-01698-g003.jpg

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本文引用的文献

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Front Immunol. 2017 Dec 8;8:1753. doi: 10.3389/fimmu.2017.01753. eCollection 2017.
2
Learning the High-Dimensional Immunogenomic Features That Predict Public and Private Antibody Repertoires.学习预测公共和私有抗体库的高维免疫基因组特征。
J Immunol. 2017 Oct 15;199(8):2985-2997. doi: 10.4049/jimmunol.1700594. Epub 2017 Sep 18.
3
Arpeggio: A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structures.
免疫后抗原特异性 B 细胞受体的计算检测。
Proc Natl Acad Sci U S A. 2024 Aug 27;121(35):e2401058121. doi: 10.1073/pnas.2401058121. Epub 2024 Aug 20.
4
Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery.对 40 亿个人类抗体可变区的大规模数据挖掘揭示了治疗性抗体和天然抗体之间的趋同,这限制了生物药物发现的搜索空间。
MAbs. 2024 Jan-Dec;16(1):2361928. doi: 10.1080/19420862.2024.2361928. Epub 2024 Jun 6.
5
Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications.使用序列、结构和机器学习相似性度量对抗体聚类方法进行基准测试,以用于抗体发现应用。
Front Mol Biosci. 2024 Mar 28;11:1352508. doi: 10.3389/fmolb.2024.1352508. eCollection 2024.
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Structural modeling of antibody variable regions using deep learning-progress and perspectives on drug discovery.利用深度学习进行抗体可变区的结构建模——药物发现的进展与展望
Front Mol Biosci. 2023 Jul 7;10:1214424. doi: 10.3389/fmolb.2023.1214424. eCollection 2023.
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Sci Rep. 2023 Jul 18;13(1):11612. doi: 10.1038/s41598-023-38108-7.
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