Protein synergistic action-based development and application of a molecularly imprinted chiral sensor for highly stereoselective recognition of S-fluoxetine

人血清白蛋白 对映体 分子印迹聚合物 立体选择性 化学 分子识别 检出限 手性(物理) 选择性 组合化学 分子 色谱法 立体化学 有机化学 Nambu–Jona Lasinio模型 手征对称破缺 物理 量子力学 夸克 催化作用
作者
Lianming Zhang,Jingxia Gao,Kui Luo,Jianping Li,Ying Zeng
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:223: 115027-115027 被引量:22
标识
DOI:10.1016/j.bios.2022.115027
摘要

In order to improve the recognition performance of MIPs sensors in chiral drug enantiomers, a novel a highly selective molecular recognition method based on protein-assisted immobilization of chiral molecular conformation was developed. S-fluoxetine (S-FLX) as the target chiral molecule, human serum albumin (HSA), which has a high affinity and strong interactions with S-FLX, was screened from 11 proteins to serve as an auxiliary recognition unit for the fixation of chiral conformation. By incorporating HSA into the preparation of molecularly imprinted polymers (MIPs), the natural chirality and high stereoselectivity of the protein were leveraged for the induction and fixation of the stereo conformation of S-FLX, refinement of internal structures of the imprinted cavities. The sensor exhibited excellent chiral recognition ability and high detection sensitivity. The changes of probe signal intensity of the MIPs/HSA sensor were positively correlated with the logarithmic concentration of S-FLX in the range of 1.0 × 10−16–1.0 × 10−11 mol L−1, where a detection limit of 6.43 × 10−17 mol L−1 was achieved (DL = 3δb/K). The selectivity of MIPs/HSA sensor in recognizing S-FLX was increased by 18.5 times and the sensitivity was increased by 2.6 times after the incorporation of HSA. The developed sensor was successfully used for the analysis of S-FLX in fluoxetine hydrochloride capsules.
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
星辰大海应助奇点采纳,获得10
刚刚
Jason发布了新的文献求助20
刚刚
大力道罡完成签到,获得积分10
1秒前
星辰大海应助tong采纳,获得10
1秒前
不做科研废物完成签到,获得积分10
1秒前
TDW发布了新的文献求助10
2秒前
帅气的绿凝完成签到,获得积分10
4秒前
杨主意发布了新的文献求助10
4秒前
O已w时o完成签到 ,获得积分10
4秒前
hechchy发布了新的文献求助10
5秒前
5秒前
Erinnnnjin完成签到,获得积分10
6秒前
科研通AI6.4应助lll采纳,获得10
6秒前
molihuakai应助罗昱昕采纳,获得10
6秒前
深情安青应助飞翔采纳,获得10
6秒前
8秒前
8秒前
慕青应助杨zy采纳,获得10
8秒前
桐桐应助蜉蝣采纳,获得10
9秒前
10秒前
Orange应助Jason采纳,获得10
10秒前
vivivian完成签到,获得积分20
10秒前
nightgaunt完成签到 ,获得积分10
11秒前
嘟嘟嘟发布了新的文献求助10
11秒前
情怀应助啦啦啦啦啦采纳,获得10
12秒前
14秒前
顾年完成签到,获得积分10
14秒前
嘟嘟嘟完成签到,获得积分20
18秒前
elisa828完成签到,获得积分10
19秒前
20秒前
20秒前
南忆发布了新的文献求助10
21秒前
cdercder应助yeweiyutu采纳,获得10
22秒前
可爱的函函应助桃博采纳,获得10
23秒前
科研通AI6.2应助Eating采纳,获得10
24秒前
27秒前
27秒前
Lucas应助整齐的慕卉采纳,获得30
28秒前
30秒前
YIGUAN发布了新的文献求助10
31秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 2000
Advanced Weaponeering Fourth Edition, Volume 2 1000
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
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 7554392
求助须知:如何正确求助?哪些是违规求助? 9136926
关于积分的说明 19528202
捐赠科研通 7145627
什么是DOI,文献DOI怎么找? 3260862
关于科研通互助平台的介绍 2427310
邀请新用户注册赠送积分活动 2249855