发现针对复杂多跨膜蛋白的治疗性抗体。

IF 5.4 2区 医学 Q1 IMMUNOLOGY BioDrugs Pub Date : 2024-11-01 Epub Date: 2024-10-25 DOI:10.1007/s40259-024-00682-1
Amberley D Stephens, Trevor Wilkinson
{"title":"发现针对复杂多跨膜蛋白的治疗性抗体。","authors":"Amberley D Stephens, Trevor Wilkinson","doi":"10.1007/s40259-024-00682-1","DOIUrl":null,"url":null,"abstract":"<p><p>Complex integral membrane proteins, which are embedded in the cell surface lipid bilayer by multiple transmembrane spanning polypeptides, encompass families of proteins that are important target classes for drug discovery. These protein families include G protein-coupled receptors, ion channels, transporters, enzymes, and adhesion molecules. The high specificity of monoclonal antibodies and the ability to engineer their properties offers a significant opportunity to selectively bind these target proteins, allowing direct modulation of pharmacology or enabling other mechanisms of action such as cell killing. Isolation of antibodies that bind these types of membrane proteins and exhibit the desired pharmacological function has, however, remained challenging due to technical issues in preparing membrane protein antigens suitable for enabling and driving antibody drug discovery strategies. In this article, we review progress and emerging themes in defining discovery strategies for a generation of antibodies that target these complex membrane protein antigens. We also comment on how this field may develop with the emerging implementation of computational techniques, artificial intelligence, and machine learning.</p>","PeriodicalId":9022,"journal":{"name":"BioDrugs","volume":" ","pages":"769-794"},"PeriodicalIF":5.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530565/pdf/","citationCount":"0","resultStr":"{\"title\":\"Discovery of Therapeutic Antibodies Targeting Complex Multi-Spanning Membrane Proteins.\",\"authors\":\"Amberley D Stephens, Trevor Wilkinson\",\"doi\":\"10.1007/s40259-024-00682-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Complex integral membrane proteins, which are embedded in the cell surface lipid bilayer by multiple transmembrane spanning polypeptides, encompass families of proteins that are important target classes for drug discovery. These protein families include G protein-coupled receptors, ion channels, transporters, enzymes, and adhesion molecules. The high specificity of monoclonal antibodies and the ability to engineer their properties offers a significant opportunity to selectively bind these target proteins, allowing direct modulation of pharmacology or enabling other mechanisms of action such as cell killing. Isolation of antibodies that bind these types of membrane proteins and exhibit the desired pharmacological function has, however, remained challenging due to technical issues in preparing membrane protein antigens suitable for enabling and driving antibody drug discovery strategies. In this article, we review progress and emerging themes in defining discovery strategies for a generation of antibodies that target these complex membrane protein antigens. We also comment on how this field may develop with the emerging implementation of computational techniques, artificial intelligence, and machine learning.</p>\",\"PeriodicalId\":9022,\"journal\":{\"name\":\"BioDrugs\",\"volume\":\" \",\"pages\":\"769-794\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530565/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BioDrugs\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s40259-024-00682-1\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioDrugs","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40259-024-00682-1","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/25 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

复杂的整联膜蛋白由多个跨膜多肽嵌入细胞表面脂质双分子层,其中包含的蛋白质家族是药物发现的重要目标类别。这些蛋白质家族包括 G 蛋白偶联受体、离子通道、转运体、酶和粘附分子。单克隆抗体的高度特异性和设计其特性的能力为选择性结合这些靶蛋白提供了重要机会,从而可以直接调节药理学或实现其他作用机制,如杀死细胞。然而,由于制备膜蛋白抗原的技术问题,分离能结合这些类型的膜蛋白并表现出所需药理功能的抗体仍具有挑战性。在这篇文章中,我们回顾了在确定针对这些复杂膜蛋白抗原的一代抗体的发现策略方面所取得的进展和新出现的主题。我们还评论了这一领域如何随着计算技术、人工智能和机器学习的新兴应用而发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Discovery of Therapeutic Antibodies Targeting Complex Multi-Spanning Membrane Proteins.

Complex integral membrane proteins, which are embedded in the cell surface lipid bilayer by multiple transmembrane spanning polypeptides, encompass families of proteins that are important target classes for drug discovery. These protein families include G protein-coupled receptors, ion channels, transporters, enzymes, and adhesion molecules. The high specificity of monoclonal antibodies and the ability to engineer their properties offers a significant opportunity to selectively bind these target proteins, allowing direct modulation of pharmacology or enabling other mechanisms of action such as cell killing. Isolation of antibodies that bind these types of membrane proteins and exhibit the desired pharmacological function has, however, remained challenging due to technical issues in preparing membrane protein antigens suitable for enabling and driving antibody drug discovery strategies. In this article, we review progress and emerging themes in defining discovery strategies for a generation of antibodies that target these complex membrane protein antigens. We also comment on how this field may develop with the emerging implementation of computational techniques, artificial intelligence, and machine learning.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
BioDrugs
BioDrugs 医学-免疫学
CiteScore
12.60
自引率
2.90%
发文量
50
审稿时长
>12 weeks
期刊介绍: An essential resource for R&D professionals and clinicians with an interest in biologic therapies. BioDrugs covers the development and therapeutic application of biotechnology-based pharmaceuticals and diagnostic products for the treatment of human disease. BioDrugs offers a range of additional enhanced features designed to increase the visibility, readership and educational value of the journal’s content. Each article is accompanied by a Key Points summary, giving a time-efficient overview of the content to a wide readership. Articles may be accompanied by plain language summaries to assist patients, caregivers and others in understanding important medical advances. The journal also provides the option to include various other types of enhanced features including slide sets, videos and animations. All enhanced features are peer reviewed to the same high standard as the article itself. Peer review is conducted using Editorial Manager®, supported by a database of international experts. This database is shared with other Adis journals.
期刊最新文献
Effect of Biological Therapy for Psoriasis on the Development of Psoriatic Arthritis: A Population-Based Cohort Study. Biochemical Amenability in Fabry Patients Under Chaperone Therapy-How and When to Test? Introducing the Biosimilar Paradigm to Neurology: The Totality of Evidence for the First Biosimilar Natalizumab. Patient Satisfaction and Experience with CT-P17 Following Transition from Reference Adalimumab or Another Adalimumab Biosimilar: Results from the Real-World YU-MATTER Study. Pharmacokinetics, Safety, and Immunogenicity of a Biosimilar of Nivolumab (LY01015): A Randomized, Double-Blind, Parallel-Controlled Phase I Clinical Trial in Healthy Chinese Male Subjects.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1