Faculty Opinions recommendation of Adaptive protein evolution grants organismal fitness by improving catalysis and flexibility.

作者
Reinhard Sterner
出处
期刊:
标识
DOI:10.3410/f.1145142.602280
摘要

Protein evolution is crucial for organismal adaptation and fitness. This process takes place by shaping a given 3-dimensional fold for its particular biochemical function within the metabolic requirements and constraints of the environment. The complex interplay between sequence, structure, functionality, and stability that gives rise to a particular phenotype has limited the identification of traits acquired through evolution. This is further complicated by the fact that mutations are pleiotropic, and interactions between mutations are not always understood. Antibiotic resistance mediated by beta-lactamases represents an evolutionary paradigm in which organismal fitness depends on the catalytic efficiency of a single enzyme. Based on this, we have dissected the structural and mechanistic features acquired by an optimized metallo-beta-lactamase (MbetaL) obtained by directed evolution. We show that antibiotic resistance mediated by this enzyme is driven by 2 mutations with sign epistasis. One mutation stabilizes a catalytically relevant intermediate by fine tuning the position of 1 metal ion; whereas the other acts by augmenting the protein flexibility. We found that enzyme evolution (and the associated antibiotic resistance) occurred at the expense of the protein stability, revealing that MbetaLs have not exhausted their stability threshold. Our results demonstrate that flexibility is an essential trait that can be acquired during evolution on stable protein scaffolds. Directed evolution aided by a thorough characterization of the selected proteins can be successfully used to predict future evolutionary events and design inhibitors with an evolutionary perspective. PMID: 19098096 Funding information This work was supported by: Howard Hughes Medical Institute, United States

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
火星天完成签到,获得积分10
2秒前
Dawang完成签到,获得积分10
3秒前
微雨若,,完成签到 ,获得积分10
3秒前
简单酒窝完成签到,获得积分20
3秒前
隐形曼青应助小L采纳,获得10
3秒前
陈陈完成签到,获得积分10
4秒前
杜青发布了新的文献求助10
5秒前
minmin2199完成签到,获得积分10
5秒前
lito完成签到,获得积分10
6秒前
6秒前
valley完成签到,获得积分10
6秒前
huajiao完成签到,获得积分10
7秒前
清脆往事完成签到,获得积分10
7秒前
9秒前
9秒前
10秒前
安静的缘分完成签到,获得积分10
10秒前
SirDream完成签到,获得积分10
11秒前
11秒前
bitman完成签到,获得积分10
12秒前
秋123完成签到,获得积分20
13秒前
13秒前
lxl0823完成签到,获得积分10
13秒前
刘欢发布了新的文献求助10
14秒前
学术大亨发布了新的文献求助10
16秒前
16秒前
17秒前
炒米粉完成签到,获得积分10
18秒前
负责的汉堡完成签到 ,获得积分10
19秒前
零负一发布了新的文献求助10
20秒前
张欢馨应助德鲁梦雨采纳,获得10
21秒前
22秒前
荣荣完成签到,获得积分10
23秒前
23秒前
在水一方应助科研通管家采纳,获得10
24秒前
星辰大海应助科研通管家采纳,获得10
24秒前
Souvenir完成签到,获得积分10
24秒前
慕青应助科研通管家采纳,获得10
24秒前
思谷应助科研通管家采纳,获得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
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Handbuch Trainingswissenschaft – Trainingslehre 500
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 500
Variations: A More Diverse Picture of Contemporary Art 400
A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) Fourth Edition 400
Induction Heating and Heat Treatment (ASM Handbook, Volume 4C) 300
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 7586733
求助须知:如何正确求助?哪些是违规求助? 9165014
关于积分的说明 19614364
捐赠科研通 7167174
什么是DOI,文献DOI怎么找? 3266697
关于科研通互助平台的介绍 2431714
邀请新用户注册赠送积分活动 2258530