Exploiting Enzyme Plasticity in Drug Discovery: application to glutamate racemase

描述（由申请人提供）：我们建议基于过渡态分析的基本原理开发一类新的抗菌药物。与“基于结构”的药物设计不同，“基于结构”的药物设计主要基于底物模仿，过渡状态分析是“基于反应”的药物设计，源于对相关催化化学的严格化学评估，以揭示酶的机制和稳定过渡状态的结构变化。过渡态分析将产生与过渡态结构非常相似的小分子过渡态类似物。在我们的研究中，我们选择了谷氨酸消旋酶（GR），这是一个越来越重要的抗菌药物靶点。GR已被广泛证实是一种有吸引力的药物靶点，用于许多致病物种。GR催化的外消旋化主要是通过广泛的柔韧性来实现的，这是GR晶体结构中基本缺乏的信息。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GR的过渡态结构，可以发现一种化学上更多样化的抑制剂空间。这些研究为发现具有高配体效率的多种抑制支架提供了潜在的有力途径，解决了满足相对狭窄的抗菌药物空间要求的问题。然而，虚拟筛选方法的巨大潜力受到相对快速地对蛋白质配体亲和力进行高质量预测的明显失败的显著阻碍。该建议直接填补了阻碍发现GR真正过渡态抑制剂的两个重要且相关的知识空白：1)确定实验验证的GR过渡态结构，以及导致反应轨迹超紧密结合的过渡态药效团；2)如何从针对高度灵活的蛋白质受体的虚拟筛选中准确排序命中。这些研究的成功完成将有助于发现和设计针对柔性酶药物靶点的新型高效抑制支架，从而在药物发现领域产生变革性的结果。本课题的具体目标如下：目标1：解决柔性酶药物靶点的秩序问题的综合计算方法：柔性酶受体方法（FERM）用于定向MD （SMD）对接。目的2:FERM挑战：利用构象限制性谷氨酸类似物库（螺旋[3.3]庚烷家族）对GR进行构象清查。目的3：通过动力学同位素效应阐明GR（枯草芽孢杆菌RacE和炭疽芽孢杆菌RacE1和RacE2）的过渡态药效团。目标4：将其整合在一起：FERM-SMD对接对GR过渡态药效团和基态综合体的应用。

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.