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PLAbDab-纳米抗体数据库:一个来自专利和文献的骆驼科动物及鲨鱼纳米抗体数据库。

PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature.

作者信息

Gordon Gemma L, Greenshields-Watson Alexander, Agarwal Parth, Wong Ashley, Boyles Fergus, Hummer Alissa, Lujan Hernandez Ana G, Deane Charlotte M

机构信息

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Twist Bioscience, 681 Gateway Blvd, South San Francisco, CA 94080, USA.

出版信息

Nucleic Acids Res. 2025 Jan 6;53(D1):D535-D542. doi: 10.1093/nar/gkae881.

DOI:10.1093/nar/gkae881
PMID:39385626
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11701533/
Abstract

Nanobodies are essential proteins of the adaptive immune systems of camelid and shark species, complementing conventional antibodies. Properties such as their relatively small size, solubility and high thermostability make VHH (variable heavy domain of the heavy chain) and VNAR (variable new antigen receptor) modalities a promising therapeutic format and a valuable resource for a wide range of biological applications. The volume of academic literature and patents related to nanobodies has risen significantly over the past decade. Here, we present PLAbDab-nano, a nanobody complement to the Patent and Literature Antibody Database (PLAbDab). PLAbDab-nano is a self-updating, searchable repository containing ∼5000 annotated VHH and VNAR sequences. We describe the methods used to curate the entries in PLAbDab-nano, and highlight how PLAbDab-nano could be used to design diverse libraries, as well as find sequences similar to known patented or therapeutic entries. PLAbDab-nano is freely available as a searchable web server (https://opig.stats.ox.ac.uk/webapps/plabdab-nano/).

摘要

纳米抗体是骆驼科动物和鲨鱼物种适应性免疫系统的重要蛋白质,是对传统抗体的补充。其相对较小的尺寸、溶解性和高热稳定性等特性,使得VHH(重链可变重域)和VNAR(可变新抗原受体)形式成为一种有前景的治疗形式,以及广泛生物应用的宝贵资源。在过去十年中,与纳米抗体相关的学术文献和专利数量显著增加。在此,我们展示了PLAbDab-nano,它是专利和文献抗体数据库(PLAbDab)的纳米抗体补充。PLAbDab-nano是一个可自我更新、可搜索的储存库,包含约5000条注释的VHH和VNAR序列。我们描述了用于整理PLAbDab-nano条目的方法,并强调了PLAbDab-nano如何可用于设计多样化的文库,以及找到与已知专利或治疗条目相似的序列。PLAbDab-nano可作为一个可搜索的网络服务器免费获取(https://opig.stats.ox.ac.uk/webapps/plabdab-nano/)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/c89e0cfbd621/gkae881fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/9593343e3b2c/gkae881figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/3894d1abe474/gkae881fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/d0e138d1be34/gkae881fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/0a3892ae662d/gkae881fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/58e54d9d1c1d/gkae881fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/cbf88d3e706c/gkae881fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/c89e0cfbd621/gkae881fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/9593343e3b2c/gkae881figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/3894d1abe474/gkae881fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/d0e138d1be34/gkae881fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/0a3892ae662d/gkae881fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/58e54d9d1c1d/gkae881fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/cbf88d3e706c/gkae881fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0df/11701533/c89e0cfbd621/gkae881fig6.jpg

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本文引用的文献

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Front Immunol. 2024 Jan 23;14:1303353. doi: 10.3389/fimmu.2023.1303353. eCollection 2023.
2
NanoLAS: a comprehensive nanobody database with data integration, consolidation and application.NanoLAS:一个具有数据集成、整合和应用功能的综合性纳米抗体数据库。
Database (Oxford). 2024 Jan 31;2024. doi: 10.1093/database/baae003.
3
The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures.
专利和文献抗体数据库(PLAbDab):一个不断发展的、具有多种功能的参考集,包含经过文献注释的抗体序列和结构。
Nucleic Acids Res. 2024 Jan 5;52(D1):D545-D551. doi: 10.1093/nar/gkad1056.
4
Current status and future expectations of nanobodies in oncology trials.纳米抗体在肿瘤临床试验中的现状与展望。
Expert Opin Investig Drugs. 2023 Jul-Dec;32(8):705-721. doi: 10.1080/13543784.2023.2249814. Epub 2023 Aug 28.
5
A comparison of the binding sites of antibodies and single-domain antibodies.抗体和单域抗体结合位点的比较。
Front Immunol. 2023 Jul 18;14:1231623. doi: 10.3389/fimmu.2023.1231623. eCollection 2023.
6
KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies.KA-Search,一种用于快速、全面搜索已知抗体序列同一性的方法。
Sci Rep. 2023 Jul 18;13(1):11612. doi: 10.1038/s41598-023-38108-7.
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ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.免疫构建体:用于预测免疫蛋白结构的深度学习模型。
Commun Biol. 2023 May 29;6(1):575. doi: 10.1038/s42003-023-04927-7.
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