Ensemble-function Studies of Enzyme Mechanism

酶是所有生物学的催化剂，它使反应在不到一秒的时间内发生，否则需要几十年，几千年或更长的时间，并为生物学所需的正确反应提供催化剂。鉴于酶的非凡能力及其在生物学中的中心地位，酶近世纪来一直是研究的热门课题。从这项研究中产生了许多具有实用价值的药物和酶。然而，了解酶如何实现其非凡的能力仍然是难以捉摸的。这种理解应该是定量的，因为定量模型更完整，更容易测试，并且在实际应用中更有效。这个建议将物理学中一个公认的概念整合到酶的研究中，构象系综。特别是，在获得和预测蛋白质的”结构“方面已经取得了巨大的进展。然而，蛋白质并不是以单一的结构存在，而是一种复杂的构象状态“集合”。这些多重状态通常与功能有关，例如使我们的心脏和其他肌肉工作的马达蛋白，即使没有直接参与，系综性质也是将分子与自由能联系起来的缺失环节，自由能决定分子事件的概率，如结合和催化。这个提议将酶的构象特性与酶催化（或加速）反应的能力联系起来，并且在这些分析的过程中，将确定负责催化的分子特征。最广泛地说，这些信息将产生基本的理解，并提供提示和指导，可能有助于设计新的酶用于实际用途。该项目将培训研究生和本科生。特别努力将花在指导代表性不足的少数民族学生。这项建议的首要目标是开发基于基础物理和化学的酶催化定量模型。集成功能分析提供了实现这一目标所需的范例。关键是，需要系综，而不是静态结构，以获得自由能，从而反应概率，根据统计力学定律。还需要系综来评估静态结构中观察到的构象状态和相互作用是否代表分子群体。这项研究将利用现有的结构数据和在生理温度下获得晶体学数据的最新进展，使用伪系综和多温度（MT）X射线晶体学获得系综信息。EnsemblePDB将被开发用于从现有蛋白质数据库结构的巨大宝库中快速构建伪集合，并评估催化的两个基本方面：（1）在反应状态中发生了什么变化？- 从而为酶促反应路径提供了一个最小模型;以及（2）这些变化的能量后果是什么？- 从而为负责催化的酶特征提供定量模型。考虑到酶的催化贡献的功能，酶反应相比，相应的溶液反应，使用从量子力学计算和能量的酶和溶液的相互作用从能量函数从晶体学数据和分子动力学获得的溶液反应路径。这项建议将首先将这种方法应用于丝氨酸蛋白酶，几乎通用的教科书酶催化的例子。这一建议的可行性包括：（1）丝氨酸蛋白酶催化作用的原子级定量模型;（2）确定进化相关酶和进化不同酶之间以及在不同化学约束下进行反应的酶之间共有的催化特征;（3）测试酶冷适应的进化机制;以及（4）酶构象集合中温度依赖性变化的深入测定，这些信息可能是研究变构和设计新酶的基础。最广泛地说，这一建议的科学成就有可能催化一种范式转变从目前占主导地位的结构-功能方法到将结构与功能基础的能量学联系起来的整体功能研究。它们也有可能彻底改变酶机制的教学，并统一跨学科的教学，将教科书描述从一般和描述性变为基于化学课上教授的基本化学和物理学原理的特定模型。该项目由分子和细胞生物科学部的分子生物物理学小组资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Enzymes are the catalysts underlying all of biology, allowing reactions—that otherwise would take decades, millennia or longer—to occur in less than a second and providing catalysis for just the right reactions needed for biology. Given their remarkable capabilities and their centrality to biology, enzymes have been an intense subject of research for nearly a century. Resulting from this research are many drugs and enzymes engineered to have practical value. Nevertheless, understanding how enzymes achieve their extraordinary abilities has remained elusive. Such an understanding should be quantitative, as quantitative models are more complete, more readily tested, and more powerfully used in practical applications. This proposal integrates a well-recognized concept in physics into the study of enzymes, conformational ensembles. In particular, there has been enormous progress at obtaining and now predicting “the” structure of proteins. Yet, proteins do not exist as a single structure but rather a complex “ensemble” of conformational states. These multiple states are often involved in function, such as motor proteins that allow our heart and other muscles to work, and, even when not directly involved, ensemble properties are the missing linking in relating molecules to free energies, the energy values that determine the probabilities of molecular events such as binding and catalysis. This proposal links enzyme conformational properties to the ability of the enzyme to catalyze (or speed) reactions, and, in the course of these analysis, the molecular features that are responsible for catalysis will be identified. Most broadly, this information will yield fundamental understanding and provide hints and guidelines that may aid in the design of new enzymes for practical use. This project will train graduate and undergraduate students. Special efforts will be spent to mentor underrepresented minority students.The overarching goal of this proposal is to develop quantitative models for enzyme catalysis that are rooted in fundamental physics and chemistry. Ensemble–function analysis provides the paradigm needed to achieve this goal. Critically, ensembles, rather than static structures, are needed to obtain free energies and thus reaction probabilities, based on the laws of statistical mechanics. Ensembles are also needed to evaluate whether conformational states and interactions observed in static structures are representative of the population of molecules. This research will leverage existing structural data and recent advances in obtaining crystallographic data at physiological temperatures to obtain ensemble information using pseudo- ensembles and multi-temperature (MT) X-ray crystallography. EnsemblePDB will be developed to rapidly build pseudo-ensembles from the vast trove of existing Protein Data Bank structures and to assess two fundamental aspects of catalysis: (1) What changes occur across the reaction states? –thereby providing a minimal model for the enzymatic reaction path; and (2) What are the energetic consequences of these changes? –thereby providing quantitative models for the enzyme features responsible for catalysis. To account for catalytic contributions from enzyme features, the enzyme reaction is compared to the corresponding solution reaction, using solution reaction paths obtained from quantum mechanical calculations and the energetics of enzyme and solution interactions obtained from energy functions derived from crystallographic data and from molecular dynamics. This proposal will first apply this approach to serine proteases, the near-universal textbook example for enzyme catalysis. Deliverables of the proposal include: (1) A quantitative, atomic-level model of serine protease catalysis; (2) Determination of catalytic features shared among evolutionarily-related and evolutionarily-distinct enzymes and across enzymes carrying out reactions with different chemical constraints; (3) Tests of evolutionary mechanisms of enzyme cold-adaptation; and (4) In-depth determination of temperature-dependent changes in enzyme conformational ensembles, information that may be foundational for studying allostery and designing new enzymes. Most broadly, the scientific achievements from this proposal have the potential to catalyze a paradigm shift—from the currently dominant structure–function approaches to ensemble–function studies that link structure to the energetics that underlie function. They also have the potential to revolutionize the teaching of enzyme mechanisms and unify teaching across scientific disciplines, changing textbook descriptions from general and descriptive to specific models based on fundamental chemistry and physics principles that are taught in chemistry classes. This project was funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.