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

• 批准号: 9977221
• 负责人: Gregory R Bowman
• 金额: $ 29.93万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-05 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977221
• 关键词: Active Sites; Algorithms; American; Amino Acid Sequence; Antibiotics; Bacteria; Cefotaxime; Communication; Computing Methodologies; Coupling; Directed Molecular Evolution; Disease; Distant; Drug Binding Site; Drug Design; Drug resistance; Enzymes; Equilibrium; Evolution; Freedom; Freezing; Future; Heterogeneity; In Vitro; Kinetics; Knowledge; Measures; Methods; Modeling; Molecular Conformation; Monobactams; Mutagenesis; Mutate; Mutation; Pharmaceutical Preparations; Play; Population; Proteins; Research; Resistance; Role; Sampling; Site; Source; Specific qualifier value; Structure; System; Testing; Time; Variant; Work; X-Ray Crystallography; antibiotic resistant infections; base; beta-Lactamase; beta-Lactams; biological systems; combat; cost; design; experimental study; in vivo; insight; novel

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，每年给我们国家造成数十亿美元的损失 β-内酰胺酶是最常见的耐药源之一，并且能够迅速产生耐药性。 令人惊讶的是，随着新的β-内酰胺抗生素的出现，许多抗生素都具有降解能力。 赋予β-内酰胺酶新功能的突变远离酶的活性位点，并且几乎没有什么作用。 通过 X 射线晶体学观察到的对活性位点结构的影响。 突变也经常出现在其他情况下，例如其他形式的耐药性的进化和 了解 NAS 突变如何影响远距离位点将提供定向进化研究。 预测新形式的耐药性和设计变构药物来对抗疾病的基础 该提案的目的是了解 NAS 突变如何赋予 β- 抗生素耐药性感染。 对新底物具有活性的内酰胺酶仍然难以预测。 由于蛋白质能量景观的崎岖性以及耦合机制的多样性 遥远的残基，包括一致的结构变化和动力学之间的相关性 这些障碍将通过将新的计算方法与体外相结合来克服。 和体内实验，以集中对所有相关的定量理解 例如，研究小组将应用新方法来解释变构耦合。 旨在促进蛋白质能量景观的全面采样，例如 FAST 算法 利用马尔可夫状态模型 (MSM) 有效地对具有预先指定特征的构象进行采样。 目标 1，这些方法将用于确定 β-内酰胺酶结构和动力学的哪些特征引起 通过比较针对抗生素头孢噻肟具有不同活性的变体的模型来发现新的活性。 目标 2，识别协同结构变化和动态之间相关性的新方法 这些方法将被开发用于预测 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，将设计突变以赋予β-内酰胺酶。 然后将进行实验来测试1）这些突变是否具有新的活性。 对 β-内酰胺酶活性的预期影响以及 2) 设计的变体是否能够 保护细菌免受目标抗生素的侵害。完成这项工作将产生一个总体框架。 了解变构通讯，这将作为未来预测耐药性的基础， 设计新的抗生素，以变构方式抑制其靶标，并操纵其他系统中的变构。