MSMs, adaptive sampling, and data sharing on the cloud

MSM、自适应采样和云端数据共享

• 批准号: 10166370
• 负责人: Gregory R Bowman
• 金额: $ 22.13万
• 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/10166370
• 关键词: Active Sites; Adopted; Adoption; Algorithms; American; Antibiotics; Bacteria; Biomedical Research; Cefotaxime; Cloud Service; Code; Collaborations; Communication; Communities; Complement; Computer software; Computers; Computing Methodologies; Coupling; Data; Data Set; Directed Molecular Evolution; Disease; Distant; Drug Design; Drug resistance; Ebola; Enzymes; Evolution; FAIR principles; Future; Goals; High Performance Computing; Home environment; In Vitro; Individual; Infrastructure; Institutes; Learning; Letters; Memory; Methods; Modeling; Molecular; Molecular Conformation; Monobactams; Mutation; Output; Personal Computers; Pharmaceutical Preparations; Process; Protein Dynamics; Proteins; Research; Research Personnel; Resistance; Resources; Running; Sampling; Science; Scientist; Site; Software Engineering; Source; Specific qualifier value; Structure; Supercomputing; System; Testing; Training; Ursidae Family; Variant; Work; X-Ray Crystallography; antibiotic resistant infections; base; beta-Lactamase; beta-Lactams; citizen science; cloud platform; cluster computing; combat; computing resources; cost; data sharing; data tools; design; discount; experimental study; flexibility; in vivo; insight; interest; molecular dynamics; novel; open source; service providers; simulation; small molecule; software development; success; tool; volunteer

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，每年花费我们国家数十亿美元。 年β-内酰胺酶是最常见的耐药性来源之一，能够快速地 随着新β-内酰胺抗生素的引入，进化降解它们的能力。令人惊讶的是，许多 赋予β-内酰胺酶新功能的突变远离酶的活性位点， 对活性位点结构的影响，如通过X射线晶体学观察到的。非活性位点（NAS） 突变也经常出现在其他情况下，如其他形式的耐药性的演变， 定向进化研究了解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以对抗疾病的基础， 抗药性感染本提案的目的是了解NAS突变如何赋予β- 对新底物具有活性的内酰胺酶。对NAS突变的预测性理解仍然难以捉摸 因为蛋白质能量景观的崎岖性和耦合机制的巨大多样性 遥远的残留物，包括协调一致的结构变化和动力学之间的相关性， 不同的残留物。这些障碍将通过将新的计算方法与体外 和体内实验，以定量了解相关的 负责变构偶联的波动。例如，研究小组将采用新的方法， 开发用于促进蛋白质能量景观的全面采样，例如其FAST算法 用于利用马尔可夫状态模型（MSM）来有效地对具有预先指定的特征的构象进行采样。在 目的1、利用这些方法研究β-内酰胺酶的结构和动力学特征 通过比较对抗生素头孢噻肟具有不同活性的变体的模型，在 目标2，确定协调结构变化和动态之间相关性的新方法 将开发不同的残留物。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- 具有新活性的内酰胺酶。然后将进行实验以测试1）这些突变是否具有 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。这项工作完成后，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，通过变构抑制它们的靶点，并在其他系统中操纵变构。