Non-SELEX Selection of Aptamers

指数富集配体系统进化 适体 化学 选择(遗传算法) 毛细管电泳 DNA 计算生物学 计算机科学 核糖核酸 色谱法 分子生物学 人工智能 生物 生物化学 基因
作者
Maxim V. Berezovski,Michael U. Musheev,Andrei P. Drabovich,Sergey N. Krylov
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
卷期号:128 (5): 1410-1411 被引量:227
标识
DOI:10.1021/ja056943j
摘要

Aptamers are typically selected from libraries of random DNA (or RNA) sequences by SELEX, which involves multiple rounds of alternating steps of partitioning and PCR amplification. Here we report, for the first time, non-SELEX selection of aptamersa process that involves repetitive steps of partitioning with no amplification between them. A highly efficient affinity method, non-equilibrium capillary electrophoresis of equilibrium mixtures (NECEEM), was used for partitioning. We found that three steps of NECEEM-based partitioning in the non-SELEX approach were sufficient to improve the affinity of a DNA library to a target protein by more than 4 orders of magnitude. The resulting affinity was higher than that of the enriched library obtained in three rounds of NECEEM-based SELEX. Remarkably, NECEEM-based non-SELEX selection took only 1 h in contrast to several days or several weeks required for a typical SELEX procedure by conventional partitioning methods. In addition, NECEEM-based non-SELEX allowed us to accurately measure the abundance of aptamers in the library. Not only does this work introduce an extremely fast and economical method for aptamer selection, but it also suggests that aptamers may be much more abundant than they are thought to be. Finally, this work opens the opportunity for selection of drug candidates from libraries of small molecules, which cannot be PCR-amplified and thus are not approachable by SELEX.
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
豆子发布了新的文献求助10
刚刚
Jenny应助木野狐采纳,获得10
刚刚
Khr1stINK发布了新的文献求助10
1秒前
牛牛完成签到,获得积分10
2秒前
2秒前
2秒前
li完成签到,获得积分10
2秒前
无花果应助发嗲的忆寒采纳,获得30
2秒前
xiaotudou95应助excellent_shit采纳,获得10
3秒前
btcat完成签到,获得积分10
3秒前
小蘑菇应助搬砖道人采纳,获得10
4秒前
思源应助校长采纳,获得10
4秒前
鸣隐完成签到,获得积分10
4秒前
5秒前
5秒前
5秒前
7秒前
7秒前
科研通AI5应助yx采纳,获得10
7秒前
8秒前
hym完成签到,获得积分10
8秒前
马静雨关注了科研通微信公众号
8秒前
111222完成签到,获得积分20
8秒前
9秒前
9秒前
三卡车安排你完成签到,获得积分10
10秒前
请叫我风吹麦浪应助Seiswan采纳,获得10
10秒前
10秒前
11秒前
11秒前
11秒前
12秒前
曾经以亦完成签到,获得积分10
12秒前
所所应助发疯的游子采纳,获得10
12秒前
13秒前
jcm发布了新的文献求助10
14秒前
辛勤的初晴完成签到,获得积分20
14秒前
Scidog发布了新的文献求助10
14秒前
单于静柏完成签到,获得积分10
15秒前
校长发布了新的文献求助10
15秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527849
求助须知:如何正确求助?哪些是违规求助? 3107938
关于积分的说明 9287239
捐赠科研通 2805706
什么是DOI,文献DOI怎么找? 1540033
邀请新用户注册赠送积分活动 716893
科研通“疑难数据库(出版商)”最低求助积分说明 709794