Exploiting Enzyme Plasticity in Drug Discovery: application to glutamate racemase

描述（由申请人提供）：我们建议根据过渡态分析的基本原理开发一类新的抗菌药物。过渡态分析是基于反应的药物设计，而不是基于底物模拟的“基于结构”的药物设计，源于对相关催化化学的严格化学评价，以揭示酶机制和稳定过渡态的结构变化。过渡态分析将产生紧密模拟过渡态结构的小分子过渡态类似物。对于拟议的研究，我们选择了谷氨酸消旋酶（GR），一个越来越重要的抗菌药物靶点。GR已被广泛证实是许多致病物种中有吸引力的药物靶标。GR催化的外消旋化主要通过广泛的灵活性来实现，这是GR晶体结构中基本上不存在的信息。我们研究小组最近的研究强烈表明，通过在虚拟筛选活动中考虑其过渡态结构，可以发现针对GR的化学多样性抑制剂空间。这些研究为发现具有高配体效率的多种抑制性支架提供了一种潜在的强有力的方法，这为满足抗微生物药物空间相对狭窄的要求提供了解决方案。然而，虚拟筛选方法的巨大潜力被相对快速地对蛋白质-配体亲和力进行质量预测的明显失败所显著阻碍。该提议直接填补了阻碍发现GR的真正过渡态抑制剂的两个重要且相关的知识空白：1）确定GR的实验验证的过渡态结构，以及导致沿着反应轨迹的超紧密结合的过渡态药效团，以及2）如何从针对高度灵活的蛋白质受体的虚拟筛选中准确地排序命中。拟议研究的成功完成将使发现和设计用于柔性酶药物靶标的新型高效抑制性支架成为可能，从而在药物发现领域产生变革性结果。该提案的具体目标如下：目标1：一个集成的计算方法来解决一个灵活的酶药物靶标的排序问题：灵活的酶受体方法（FERM）转向MD（SMD）-对接。目标二：FERM挑战：使用构象限制的谷氨酸类似物（螺[3.3]庚烷家族）文库的GR构象目录目的3：GR的过渡态药效团的阐明（B.枯草芽孢杆菌RacE和B. anthracis RacE 1和RacE 2）。目的4：将其结合在一起：FERM-SMD对接对GR过渡态药效团和基态系综的应用。 公共卫生相关性：通过完全捕获酶受体中发生的动态变化，有巨大的机会发现针对酶靶点的新型高效药物，这些动态变化与酶受体与反应中间体的内在超紧密结合有关。关键挑战与准确捕获小规模和大规模蛋白质运动（无法从结构数据中轻易获得）以及蛋白质溶剂界面静电行为的准确表示有关。目前的提案描述了一种使用实验和计算方法快速捕获和整合缺失信息的方法，这将消除导致不准确预测的许多假阳性，并促进发现新的和有效的小分子疗法。

DESCRIPTION (provided by applicant): We propose to develop a new class of antimicrobial drugs based on the fundamental principles of transition state analysis. Rather than "structure-based" drug design, which is based largely on substrate mimicry, transition-state analysis is "reaction-based" drug design, stemming from a rigorous chemical evaluation of the relevant catalytic chemistry to reveal the enzyme mechanism and the structural changes that stabilize a transition state. Transition state analysis will yield small molecule transition state analogs that closely mimic the transition state structure. For the proposed studies we selected Glutamate Racemase (GR), an increasingly important antimicrobial drug target. GR has been widely validated to be an attractive drug target in numerous pathogenic species. GR-catalyzed racemization is primarily achieved through extensive flexibility, which is information that is largely absent from GR crystal structures. Recent studies by our research group have strongly suggested that a more chemically diverse inhibitor space can be discovered against GR by considering its transition state structure in virtual screening campaigns. These studies have produced a potentially powerful approach for the discovery of diverse inhibitory scaffolds with high ligand efficiency, which provides a solution to the problems of meeting the relatively narrow requirements of antimicrobial drug space. However, the enormous potential of virtual screening methods are significantly hindered by the pronounced failures to relatively rapidly make quality predictions about protein-ligand affinities. This proposal directly fills two significant and related knowledge gaps that hinder discovery of true transition state inhibitors for GR: 1) determination of an experimentally validated transition state structure for GR, and the transition state pharmacophore that leads to the ultra tight binding along the reaction trajectory and 2) how to accurately rank-order hits from virtual screening against a highly flexible protein receptor. The successful completion of the proposed studies will enable the discovery and design of novel high efficiency inhibitory scaffolds for flexible enzyme drug targets, and thus yield transformative results in the field of drug discovery. The specific aims for this proposal are as follows: Aim 1: An integrated computational approach to solve the rank-order problem for a flexible enzyme drug target: the Flexible Enzyme Receptor Method (FERM) for Steered MD (SMD)-Docking. Aim 2: A FERM Challenge: a conformational inventory of GR using a library of conformationally restricted glutamate analogs (the spiro[3.3]heptane family) Aim 3: Elucidation of the Transition State Pharmacophore for GR (B. subtilis RacE and B. anthracis RacE1 and RacE2) via Kinetic Isotope Effects. Aim 4: Bringing it all together: the application of FERM-SMD Docking against the GR transition state pharmacophore and ground state ensembles. PUBLIC HEALTH RELEVANCE: There are enormous opportunities to discover novel high potency drugs against enzyme targets by fully capturing the dynamic changes that occur in the enzyme receptor, which are related to their intrinsic ultra-tight binding to reaction intermediates. The key challenges are related to accurately capturing both small and large scale protein motions, which are not readily available from structural data, and also accurate representation of the behavior of electrostatics at the protein solvent interface. The current proposal describes a method that rapidly captures and integrates the missing information, using both experimental and computational approaches, which will eliminate the many false positives that lead to inaccurate predictions, and facilitate the discovery of novel and potent small molecule therapeutics.