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Allosteric impact of non-active-site mutations on enzymatic function

非活性位点突变对酶功能的变构影响

基本信息

批准号：
10692526
负责人：
Gregory R Bowman
金额：
$ 6.82万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-05 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10692526
关键词：
Allosteric impact non active site

项目摘要

Antibiotic-resistant infections kill tens of thousands of Americans and cost our nation billions of dollars every year. β-lactamase enzymes are one of the most common sources of resistance and are capable of quickly evolving the ability to degrade new β-lactam antibiotics as they are introduced. Surprisingly, many of the mutations that confer β-lactamases with new functions are far from the enzyme's active site and have little effect on the structure of the active site, as observed by x-ray crystallography. Such non-active site (NAS) mutations also appear frequently in other contexts, such as the evolution of other forms of drug resistance and directed evolution studies. Understanding how NAS mutations allosterically impact distant sites would provide a basis for predicting new forms of drug resistance and designing allosteric drugs to combat diseases like antibiotic-resistant infections. The objective of this proposal is to understand how NAS mutations confer β- lactamases with activity against new substrates. A predictive understanding of NAS mutations remains elusive because of the ruggedness of proteins' energy landscapes and the great diversity of mechanisms that couple distant residues, including both concerted structural changes and correlations between the dynamics of different residues. These obstacles will be overcome by integrating novel computational methods with in vitro and in vivo experiments to converge on a quantitative understanding of the full spectrum of correlated fluctuations responsible for allosteric coupling. For example, the research team will apply new methods they developed to facilitate comprehensive sampling of proteins' energy landscapes, such as their FAST algorithm for leveraging Markov State Models (MSMs) to efficiently sample conformations with pre-specified features. In Aim 1, these methods will be used to identify what features of β-lactamase’s structure and dynamics give rise to new activities by comparing models for variants with different activities against the antibiotic cefotaxime. In aim 2, new methods for identifying both concerted structural changes and correlations between the dynamics of different residues will be developed. These methods will be used to predict new sites where NAS mutations can alter activities of β-lactamases. To test insights from each aim, mutations will be designed to confer β- lactamases with new activities. Then experiments will be performed to test 1) whether these mutations have the intended impact on the activities of β-lactamases and 2) whether the designed variants are capable of protecting bacteria from the target antibiotic. Completion of this work will result in a general framework for understanding allosteric communication that will serve as a basis for future efforts to predict drug resistance, design new antibiotics that allosterically inhibit their targets, and manipulate allostery in other systems.

抗生素耐药性感染导致数以万计的美国人死亡，并给我们国家造成了数十亿美元的损失每年美元。 β-内酰胺酶是最常见的耐药性来源之一，能够快速进化出降解新型 β-内酰胺抗生素的能力介绍了。令人惊讶的是，许多赋予β-内酰胺酶新功能的突变是远离酶的活性位点，对活性位点的结构影响很小，通过X射线晶体学观察。这种非活性位点（NAS）突变也经常出现在其他背景，例如其他形式的耐药性的进化和定向进化研究。了解 NAS 突变如何变构影响远处位点将提供预测新形式耐药性和设计变构药物来对抗的基础抗生素耐药性感染等疾病。该提案的目的是了解如何 NAS 突变赋予 β-内酰胺酶针对新底物的活性。一个预测性的由于蛋白质能量的坚固性，对 NAS 突变的理解仍然难以捉摸景观和耦合远距离残基的机制的多样性，包括协调一致的结构变化和不同残基动态之间的相关性。这些通过将新颖的计算方法与体外和体内相结合来克服障碍实验集中于对所有相关性的定量理解引起变构耦合的波动。例如，研究团队将应用新的他们开发的方法促进蛋白质能量景观的全面采样，例如他们利用马尔可夫状态模型 (MSM) 进行高效采样的 FAST 算法具有预先指定特征的构象。在目标 1 中，这些方法将用于识别什么通过比较模型，β-内酰胺酶的结构和动力学特征产生了新的活性针对抗生素头孢噻肟具有不同活性的变体。在目标 2 中，新方法识别协调一致的结构变化以及不同动态之间的相关性残留物将被开发。这些方法将用于预测 NAS 突变的新位点可以改变β-内酰胺酶的活性。为了测试每个目标的见解，突变将被设计为赋予β-内酰胺酶新的活性。然后将进行实验来测试 1) 是否这些突变对 β-内酰胺酶的活性有预期的影响，2) 是否设计的变体能够保护细菌免受目标抗生素的侵害。完成此工作将产生一个理解变构通讯的总体框架，该框架将服务于作为未来预测耐药性的基础，设计新的变构抗生素抑制它们的目标，并操纵其他系统中的变构。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Mechanistic Basis for ATP-Dependent Inhibition of Glutamine Synthetase by Tabtoxinine-β-lactam.

DOI：
10.1021/acs.biochem.7b00838
发表时间：
2018-01-09
期刊：
Biochemistry
影响因子：
2.9
作者：
Patrick GJ;Fang L;Schaefer J;Singh S;Bowman GR;Wencewicz TA
通讯作者：
Wencewicz TA

Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes.

DOI：
10.1021/acs.jctc.8b00500
发表时间：
2018-11-13
期刊：
Journal of chemical theory and computation
影响因子：
5.5
作者：
Zimmerman MI;Porter JR;Sun X;Silva RR;Bowman GR
通讯作者：
Bowman GR

SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential.

DOI：
10.1016/j.bpj.2021.03.024
发表时间：
2021-07-20
期刊：
Biophysical journal
影响因子：
3.4
作者：
Vithani N;Ward MD;Zimmerman MI;Novak B;Borowsky JH;Singh S;Bowman GR
通讯作者：
Bowman GR

Opening of a cryptic pocket in β-lactamase increases penicillinase activity.

DOI：
10.1073/pnas.2106473118
发表时间：
2021-11-23
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Knoverek CR;Mallimadugula UL;Singh S;Rennella E;Frederick TE;Yuwen T;Raavicharla S;Kay LE;Bowman GR
通讯作者：
Bowman GR

Enspara: Modeling molecular ensembles with scalable data structures and parallel computing.

Enspara：利用可扩展的数据结构和并行计算对分子整体进行建模。