Classification of difference between inhibition constants of an inhibitor to facilitate identifying the inhibition type

非竞争性抑制剂 非竞争性抑制 化学 米氏-门汀动力学 立体化学 生物化学 酶分析
作者
Xiaolan Yang,Zhiyin Du,Jun Pu,Hua Zhao,Hong Chen,Yin Liu,Zhirong Li,Zhenli Cheng,Huansi Zhong,Fei Liao
出处
期刊:Journal of Enzyme Inhibition and Medicinal Chemistry [Taylor & Francis]
卷期号:28 (1): 205-213 被引量:45
标识
DOI:10.3109/14756366.2011.645240
摘要

To identify the common inhibition types, the putative decision system is unsatisfactory. In a new decision system, Michaelis-Menten constants and maximal reaction rates were plotted versus inhibitor concentrations for deriving Kik and Kiv as the inhibition constants, respectively; their difference was quantified as the ratio of the larger one to the smaller one. Such ratios below 2.0 suggested uncompetitive inhibitors, over 5.0 suggested noncompetitive or competitive inhibitors, and from 2.0 to 5.0 suggested mixed inhibitors. By the new decision system, (i) the simulation recovery of uncompetitive inhibitors under CVs of 2% or 5% was improved by four times, but that of competitive or noncompetitive inhibitors was improved slightly; (ii) the recovery of L-phenylalanine as an uncompetitive inhibitor of intestinal alkaline phosphatase reached 38%, while the putative decision system lost all; the recovery of xanthine as a competitive inhibitor of uricase was improved slightly. Therefore, the new decision system was better.

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
刚刚
可爱幻桃发布了新的文献求助10
刚刚
调皮的醉山完成签到 ,获得积分10
1秒前
1秒前
乘风破浪发布了新的文献求助10
2秒前
2秒前
帅b发布了新的文献求助10
3秒前
3秒前
Lucas应助123654采纳,获得10
4秒前
5秒前
善良凌波发布了新的文献求助10
5秒前
5秒前
CodeCraft应助懒羊羊采纳,获得10
6秒前
6秒前
酷波er应助何松采纳,获得10
6秒前
ssffzb2008发布了新的文献求助10
7秒前
7秒前
7秒前
中道完成签到,获得积分20
7秒前
8秒前
8秒前
啦啦发布了新的文献求助10
8秒前
万能图书馆应助直率发夹采纳,获得10
9秒前
9秒前
化学发布了新的文献求助10
9秒前
9秒前
小二郎应助雪白可乐采纳,获得10
9秒前
汉堡包应助xiaokun采纳,获得10
10秒前
自然寻绿发布了新的文献求助10
10秒前
上官若男应助cmwcmw采纳,获得10
10秒前
yyyyds完成签到,获得积分10
10秒前
青糯完成签到 ,获得积分0
10秒前
10秒前
雪白亦旋发布了新的文献求助10
10秒前
如柏完成签到 ,获得积分10
11秒前
Tao完成签到,获得积分10
11秒前
土豆兄弟发布了新的文献求助10
12秒前
悦己完成签到,获得积分10
13秒前
铁柱发布了新的文献求助10
13秒前
吴宣京发布了新的文献求助10
13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 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 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 7582505
求助须知:如何正确求助?哪些是违规求助? 9161468
关于积分的说明 19603244
捐赠科研通 7164661
什么是DOI,文献DOI怎么找? 3266154
关于科研通互助平台的介绍 2431016
邀请新用户注册赠送积分活动 2257371