Exploiting Enzyme Plasticity in Drug Discovery: application to glutamate racemase

在药物发现中利用酶可塑性：在谷氨酸消旋酶中的应用

基本信息

批准号：
8238516
负责人：
Michael Ashley Spies
金额：
$ 28万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-01 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8238516
关键词：
Adopted Affinity Bacillus anthracis Back Behavior Binding Biological Assay Charge Chemicals Chemistry Complex Computer Assisted Computer Simulation Data Development Diagnosis Dissociation Diversity Library Docking Drug Delivery Systems Drug Design Electrostatics Enzymes Equilibrium Equipment and supply inventories Evaluation Failure Family Future Glutamate racemase Glutamates Goals Heptanes Hybrids Isotopes Kinetics Knowledge Lead Libraries Ligands Mammalian Cell Methods Mining Modeling Molecular Conformation Motion Pharmaceutical Preparations Phase Physics Property Protein Isoforms Proteins Publishing Race Reaction Research Resources Screening Result Screening procedure Shapes Solutions Solvents Structure Testing Therapeutic Toxic effect analog antimicrobial antimicrobial drug base carbanion computational chemistry design drug development drug discovery enzyme mechanism falls feeding flexibility in vitro Assay inhibitor/antagonist meetings mimicry novel pharmacophore programs racemization receptor scaffold small molecule stem virtual

项目摘要

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.

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型抗菌药物。与主要基于底物模拟的“基于结构”的药物设计不同，过渡态分析是“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，以揭示酶机制和结构稳定过渡状态的变化。过渡态分析将产生密切模仿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），这是一个日益重要的抗菌药物靶点。 GR 已被广泛证实是众多致病物种中有吸引力的药物靶点。 GR 催化的外消旋化主要是通过广泛的灵活性来实现的，而 GR 晶体结构中基本上不存在这种信息。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，可以发现化学上更加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一种潜在的强大方法，为满足抗菌药物空间相对狭窄的要求的问题提供了解决方案。然而，虚拟筛选方法的巨大潜力因明显无法相对快速地对蛋白质-配体亲和力进行质量预测而受到严重阻碍。该提案直接填补了阻碍发现真正的 GR 过渡态抑制剂的两个重要且相关的知识空白：1）确定经过实验验证的 GR 过渡态结构，以及导致沿反应轨迹超紧密结合的过渡态药效团2) 如何对高度灵活的蛋白质受体的虚拟筛选中的命中结果进行准确排序。该研究的成功完成将有助于发现和设计用于灵活酶药物靶标的新型高效抑制支架，从而在药物发现领域产生变革性成果。该提案的具体目标如下：目标 1：一种解决灵活酶药物靶标排序问题的集成计算方法：用于转向 MD (SMD) 对接的灵活酶受体方法 (FERM)。目标 2：FERM 挑战：使用构象限制性谷氨酸类似物（螺[3.3]庚烷家族）文库对 GR 进行构象库存目标 3：阐明 GR 的过渡态药效团（枯草芽孢杆菌 RacE 和炭疽芽孢杆菌 RacE1）和 RacE2）通过动力学同位素效应。目标 4：将所有内容结合在一起：FERM-SMD 对接针对 GR 过渡态药效团和基态集合的应用。公共健康相关性：通过充分捕捉酶受体中发生的动态变化，有巨大的机会发现针对酶靶标的新型高效药物，这些变化与其与反应中间体固有的超紧密结合有关。关键的挑战涉及准确捕获小规模和大规模的蛋白质运动（这些运动很难从结构数据中获得），以及准确表示蛋白质溶剂界面的静电行为。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误报，并促进新型有效的小分子疗法的发现。