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

抗生素耐药性感染导致数万美国人死亡，给我们国家造成数十亿美元的损失 每年一美元。β-内酰胺酶是最常见的耐药性来源之一 能够迅速进化出降解新的β-内酰胺类抗生素的能力 介绍。令人惊讶的是，许多赋予β-内酰胺酶新功能的突变是 远离酶的活性部位，对活性部位的结构影响不大，因为 用X射线结晶学观察。这种非活性位点(NAS)突变也经常出现在 其他背景，如其他形式耐药性的演变和定向进化 学习。了解NAS突变如何以变构方式影响远程站点将提供 预测新的耐药性形式和设计对抗变构药物的基础 像抗生素耐药性感染这样的疾病。这项建议的目标是了解如何 NAS突变赋予β-内酰胺酶对新底物的活性。预测性的 由于蛋白质能量的不稳定性，对NAS突变的理解仍然难以捉摸 景观和连接遥远残基的巨大多样性的机制，包括两者 不同残留物的协同结构变化和动力学之间的相关性。这些 通过将新的计算方法与体外和体内相结合将克服障碍 实验收敛于对关联的全谱的定量理解 引起变构偶联的波动。例如，研究团队将应用新的 他们开发的方法有助于对蛋白质的能量状况进行全面采样， 例如它们利用马尔可夫状态模型(MSM)来有效采样快速算法 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 对于对抗生素头孢噻肟具有不同活性的变种。在目标2中，新的方法 确定协调一致的结构变化和不同动态之间的相关性 将开发残留物。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，突变将被设计为 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-内酰胺酶的活性有预期的影响，以及2)是否 设计的变种能够保护细菌免受目标抗生素的伤害。完成这项工作 这项工作将产生一个理解变构通信的一般框架，这将有助于 作为未来预测抗药性的基础，设计新的变构抗生素 抑制它们的靶标，并在其他系统中操纵变构。

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.