通过计算设计提高抗体在无抗原情况下的热稳定性和亲和力。

IF 5.6 2区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL mAbs Pub Date : 2024-01-01 Epub Date: 2024-06-20 DOI:10.1080/19420862.2024.2362775
Mark Hutchinson, Jeffrey A Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J Gray, Gilad Kaplan
{"title":"通过计算设计提高抗体在无抗原情况下的热稳定性和亲和力。","authors":"Mark Hutchinson, Jeffrey A Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J Gray, Gilad Kaplan","doi":"10.1080/19420862.2024.2362775","DOIUrl":null,"url":null,"abstract":"<p><p>Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. <i>In silico</i> tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and <i>in silico</i> antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (T<sub>onset</sub>, T<sub>m</sub>, T<sub>agg</sub>), affinity (K<sub>D</sub>) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in T<sub>m1</sub>), with most clones retaining the favorable developability profile of the parental antibody. Additional <i>in silico</i> tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of <i>in silico</i> antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"16 1","pages":"2362775"},"PeriodicalIF":5.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195458/pdf/","citationCount":"0","resultStr":"{\"title\":\"Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.\",\"authors\":\"Mark Hutchinson, Jeffrey A Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J Gray, Gilad Kaplan\",\"doi\":\"10.1080/19420862.2024.2362775\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. <i>In silico</i> tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and <i>in silico</i> antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (T<sub>onset</sub>, T<sub>m</sub>, T<sub>agg</sub>), affinity (K<sub>D</sub>) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in T<sub>m1</sub>), with most clones retaining the favorable developability profile of the parental antibody. Additional <i>in silico</i> tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of <i>in silico</i> antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.</p>\",\"PeriodicalId\":18206,\"journal\":{\"name\":\"mAbs\",\"volume\":\"16 1\",\"pages\":\"2362775\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195458/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"mAbs\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/19420862.2024.2362775\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"mAbs","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/19420862.2024.2362775","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

在过去二十年中,治疗性抗体已成为生物制剂领域中一个迅速扩展的领域。能够简化抗体发现和优化过程的硅学工具对于支持数量和复杂性逐年增加的管线至关重要。高质量的结构信息对于抗体优化过程仍然至关重要,但抗体-抗原复合物结构往往不可用,而且硅学抗体对接方法仍然不可靠。在本研究中,DeepAb 是一种直接从序列预测抗体 Fv 结构的深度学习模型,它与单点实验深度突变扫描(DMS)富集数据结合使用,设计出了 200 个抗鸡蛋溶菌酶(HEL)抗体的潜在优化变体。我们试图确定DeepAb设计的变体是否含有DMS中的有益突变组合,是否表现出更强的热稳定性,以及这种优化是否会影响它们的可显影性。我们采用一种稳健的高通量方法制备了 200 个变体,并测试了它们的热稳定性和胶体稳定性(Tonset、Tm、Tagg)、相对于亲代抗体的亲和力(KD)以及可开发性参数(非特异性结合、聚集倾向、自结合)。在设计的克隆中,91% 和 94% 分别显示出更高的热稳定性、胶体稳定性和亲和力。其中,10% 的克隆对 HEL 的亲和力(增加 5 到 21 倍)和热稳定性(Tm1 增加 2.5 摄氏度以上)显著提高,大多数克隆保留了亲代抗体的良好显影特性。其他硅测试表明,即使不首先收集实验性 DMS 测量结果,这些方法也能富集结合亲和力。这些数据开辟了硅学抗体优化的可能性,而无需预测抗体-抗原界面,这在没有晶体结构的情况下是非常困难的。
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
Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

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
mAbs
mAbs 工程技术-仪器仪表
CiteScore
10.70
自引率
11.30%
发文量
77
审稿时长
6-12 weeks
期刊介绍: mAbs is a multi-disciplinary journal dedicated to the art and science of antibody research and development. The journal has a strong scientific and medical focus, but also strives to serve a broader readership. The articles are thus of interest to scientists, clinical researchers, and physicians, as well as the wider mAb community, including our readers involved in technology transfer, legal issues, investment, strategic planning and the regulation of therapeutics.
期刊最新文献
Sequence-based engineering of pH-sensitive antibodies for tumor targeting or endosomal recycling applications. Systematic analysis of Fc mutations designed to reduce binding to Fc-gamma receptors Navigating large-volume subcutaneous injections of biopharmaceuticals: a systematic review of clinical pipelines and approved products Antibody association in solution: cluster distributions and mechanisms Reducing neonatal Fc receptor binding enhances clearance and brain-to-blood ratio of TfR-delivered bispecific amyloid-β antibody
×
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