权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

抗生素耐药性感染杀死了成千上万的美国人，使我们的国家损失了数十亿美元。美元每年。β-内酰胺酶是最常见的耐药性来源之一，能够快速进化降解新β-内酰胺抗生素的能力，介绍令人惊讶的是，许多赋予β-内酰胺酶新功能的突变是远离酶的活性位点，对活性位点的结构几乎没有影响，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下，如其他形式的耐药性和定向进化的演变问题研究了解NAS突变如何变构影响远端位点将提供预测新形式的耐药性和设计变构药物的基础，比如抗药性感染本建议的目的是了解如何 NAS突变赋予β-内酰胺酶对新底物的活性。预测由于蛋白质能量的不稳定性，对NAS突变的理解仍然是难以捉摸的景观和耦合遥远残留物的机制的巨大多样性，包括两者协调一致的结构变化和不同残留物动态之间的相关性。这些通过将新的计算方法与体外和体内相结合，实验收敛于相关的全光谱的定量理解负责变构偶联的波动。例如，研究小组将应用新的他们开发的方法，以促进全面采样蛋白质的能量景观，例如他们的FAST算法，用于利用马尔可夫状态模型（MSM）来有效地采样具有预定特征的构象。在目标1中，这些方法将用于确定 β-内酰胺酶的结构和动力学特征通过比较模型产生新的活性用于对抗生素头孢噻肟具有不同活性的变体。在目标2中，确定协调一致的结构变化和不同国家动态之间的相关性，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洞察力，突变将被设计成赋予β-内酰胺酶新的活性。然后将进行实验以测试1）是否这些突变对β-内酰胺酶的活性具有预期的影响，以及2）设计的变体能够保护细菌免受目标抗生素的侵害。完成本这项工作将产生一个理解变构通讯的总体框架，作为未来努力预测耐药性的基础，设计新的抗生素，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。