De Novo Evolution of an Antibody‐Mimicking Multivalent Aptamer via a DNA Framework

适体 指数富集配体系统进化 贪婪 DNA 化学 SELEX适体技术 小分子 抗体 组合化学 上皮细胞粘附分子 计算生物学 生物物理学 分子生物学 生物 生物化学 核糖核酸 细胞 遗传学 基因
作者
Linlin Tang,Mengjiao Huang,Mingjiao Zhang,Yufeng Pei,Yan Liu,Yong Wei,Chaoyong Yang,Teng Xie,Dong Zhang,Ruhong Zhou,Yanling Song,Jie Song
出处
期刊:Small methods [Wiley]
卷期号:7 (6): e2300327-e2300327 被引量:11
标识
DOI:10.1002/smtd.202300327
摘要

Multivalent interactions can often endow ligands with more efficient binding performance toward target molecules. Generally speaking, a multivalent aptamer can be constructed via post-assembly based on chemical structural information of target molecules and pre-identified monovalent aptamers derived from traditional systematic evolution of ligands by exponential enrichment (SELEX) technology. However, many target molecules may not have known matched aptamer partners, thus a de novo evolution will be highly desired as an alternative strategy for directed selection of a high-avidity, multivalent aptamer. Here, inspired by the superiority of multivalent interactions between antibodies and antigens, a direct SELEX strategy with a preorganized DNA framework library for an "Antibody-mimicking multivalent aptamer" (Amap) selection to epithelial cell adhesion molecule (EpCAM), a model target protein is reported. The Amap presents a relatively good binding affinity through both aptamer moieties concurrently binding to EpCAM, which has been confirmed by affinity analysis and molecular modeling. Furthermore, dynamic interactions between Amap and EpCAM are directly visualized by magnetic tweezers at the single-molecule level. A nice binding affinity of Amap to EpCAM-positive cancer cells has also been verified, which hints that their Amap-SELEX strategy has the potential to be a new route for de novo evolution of multivalent aptamers.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
哇哈哈发布了新的文献求助10
1秒前
2秒前
2秒前
犹未雪应助林qiuxiang采纳,获得10
2秒前
星辰大海应助阳光的虔纹采纳,获得10
3秒前
3秒前
4秒前
lll发布了新的文献求助10
5秒前
5秒前
科研通AI6.2应助LH采纳,获得10
6秒前
鲤鱼诗桃发布了新的文献求助10
7秒前
宝儿糯发布了新的文献求助30
8秒前
laine完成签到,获得积分10
8秒前
9秒前
汉堡包应助朴素的小土豆采纳,获得10
9秒前
Lumos完成签到,获得积分10
9秒前
10秒前
干爆瓶颈发布了新的文献求助10
11秒前
13秒前
14秒前
ccc发布了新的文献求助10
15秒前
Aaron完成签到,获得积分10
16秒前
16秒前
叶qing完成签到 ,获得积分10
17秒前
宝儿糯完成签到,获得积分20
18秒前
zxc579发布了新的文献求助10
18秒前
星辰大海应助藏杨同学采纳,获得10
20秒前
molihuakai应助Judy采纳,获得10
21秒前
21秒前
Mansis发布了新的文献求助10
21秒前
高大摇伽发布了新的文献求助10
22秒前
22秒前
孤央完成签到 ,获得积分10
22秒前
22秒前
酷波er应助ccc采纳,获得10
23秒前
Heyouatpome完成签到,获得积分10
23秒前
Hello应助211JZH采纳,获得10
23秒前
蛋黄完成签到,获得积分10
24秒前
南笙关注了科研通微信公众号
24秒前
Lefting发布了新的文献求助10
24秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Navigating Normative Orders. Interdisciplinary Perspectives 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7758651
求助须知:如何正确求助?哪些是违规求助? 9304563
关于积分的说明 20281533
捐赠科研通 7342400
什么是DOI,文献DOI怎么找? 3312257
关于科研通互助平台的介绍 2462832
邀请新用户注册赠送积分活动 2326174