CHEMICAL SYNTHESIS CORE

化学合成核心

• 批准号: 7384102
• 负责人: DOUGLAS F COVEY
• 金额: $ 15.18万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7384102
• 关键词: Acetylcholine; Affinity; Binding; Binding Proteins; Binding Sites; Biological; Biological Testing; Cholinergic Receptors; Complex; Computer Assisted; Computer Simulation; Data; Databases; Development; Docking; Electrostatics; Elements; Enzymes; Estrogen Receptors; Evaluation; Excretory function; Extracellular Domain; Family; Free Energy; GABA-A Receptor; Goals; Homology Modeling; Human; Hydrogen Bonding; Image; Individual; Journals; Laboratories; Ligand Binding; Ligands; Lymnaea AChBP protein; Membrane; Metabolism; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Nicotinic Receptors; Numbers; Paper; Pharmaceutical Preparations; Positioning Attribute; Principal Investigator; Property; Protein Binding; Protein Isoforms; Proteins; Publishing; Quantitative Structure-Activity Relationship; Radiology Specialty; Range; Rattus; Research; Research Activity; Research Personnel; Scientific Evaluation; Sequence Alignment; Sequence Homology; Series; Statistical Methods; Steroids; Structural Models; Structure; Techniques; Torpedo; Training; Work; absorption; base; chemical synthesis; comparative; computational chemistry; design; falls; in vivo; lipophilicity; member; molecular modeling; novel; novel strategies; programs; receptor; receptor binding; receptor expression; response; steroid analog; three dimensional structure; tool

by methods 1 and 3 of the molecular modeling core. All projects have specific aims that can be aided by methods 2 and 3 of the core. QSAR and chemoinformatic studies of the screened compounds, can provide information on molecular features such as lipophilicity, orientation of hydrogen bonding groups, and molecular volumes. Such studies will allow the elucidation of common features for active and inactive compounds, the prediction of biological activities including properties such as membrane accumulation. There will be 3 types of computational chemistry in the molecular modeling effort: Modeling Methods (1) Comparative (Homology) Models of rat a1p2Y2 pentameric GABA-A receptor. PHS 398/2590 (Rev.09/04, Reissued 4/2006) Page o PT 1 Continuation Format Page Principal Investigator/Program Director (Last, First, Middle): Steinbach, Joseph Henry (2) QSAR studies of previously utilized compounds (3) Docking studies of compounds to receptor models These molecular modeling studies will be carried out in the laboratory of Dr. David Reichert in our Department of Radiology. The Reichert lab's research is primarily focused on the computer aided molecular design of in vivo imaging agents. As part of this work they have developed expertise in the modeling of steroids and steroid analogs for imaging estrogen receptor expression. In order to validate methodologies that they believed could be successfully applied to imaging agents, they first developed computer models capable of predicting the binding affinities of known ligands for both known isoforms of the estrogen receptor (a and P). This work has been published as two papers in the Journal of Computer-Aided Molecular Design, and the Journal of Molecular Graphics and Modelling [43,44]. Comparative modeling - The goals proposed for this core require structural models of the GABA-A receptor. Although the three dimensional structure of many enzymes and receptors are still unknown, the number of experimentally determined structures is rapidly growing. In 1995, the October release of the Protein Data Bank (PDB) [7] had 3,821 structures [34]. As of September 2006, this number had grown to 38,620. Unfortunately, the GABA-A receptor is not one of the known structures. Two related structures are known and have been used by several groups to build comparative models of the GABA-A receptor, these are the acetylcholine-binding protein (AChBP) from Lymnaea stagnalis (PDB ID 119B)and the nicotinic acetylcholine receptor (nAChR) from Torpedo marmorata (PDB ID 2BG9). Recent examples of the development of comparative models of the GABA-A receptor are from Trudell and Bertaccini [40], Ernst et at [15], and Campagna-Slater and Weaver [9]. Of particular utility is the work of Ernst et a\, who developed a model of the rat a<\$2\2 pentameric receptor [15]. The development of comparative models for the GABA-A receptor is a non-trivial task, primarily due to the low sequence identity between members of the "cys-loop" family [14]. In general, with a sequence identity >60% pairwise sequence alignment is quite accurate, this falls off quickly with a sequence identity < 40%. The sequence identity between AChBP and "cys-loop" extracellular domains ranges from 15-30%. The situation for the membrane spanning regions is even worse, the sequence identity for the rat cti subunit to the nAChr subunits ranges from 19- 21%. Despite this fact, reasonable alignments can be produced using conserved positions; QSAR and CoMFA - Quantitative structure-activity relationships (QSAR) have become a common tool in the field of molecular modeling since their introduction [20]. Indeed they have found application in both the prediction of biological activity and more recently in the prediction of the Absorption, Distribution, Metabolism, Excretion and Toxicological (ADME/tox) properties of organic drug-like compounds [4,17-19,25]. A related technique CoMFA (Comparative Molecular Field Analysis) has been utilized extensively to study the relationship between three-dimensional molecular information such as steric and electrostatic fields and biological activity [12,21,42]. CoMFA is based on the premise that the pharmacophoric elements which are responsible for the biological activity of a compound will be represented in the calculated steric and electrostatic fields of the compound. By studying a series of compounds, called the training set, consisting of compounds with good, medium and poor bioactivity for a specific protein target it is possible to extrapolate a three-dimensional pharmacophoric model that explains the observed bioactivity. Indeed this model suggests how the steric and electrostatic fields might be manipulated to produce a novel compound with enhanced bioactivity. One requirement of CoMFA is that the compounds in the training set be aligned against each other so that the overlap of the pharmacophoric elements responsible for producing a biological response is maximized. In cases where the ligands are very diverse in structure or have several possible modes of binding, developing the alignment can be problematic. In cases where the crystal structure of the target protein complexed to a ligand has been resolved, the structure of the docked ligand can be used as a template. However, even in this advantageous case it is difficult to deal with compounds in the training set which might have multiple protein binding conformations while maintaining a high pharmacophoric overlap with the template compound. A new approach to this problem is to use a docking program capable of predicting the most favorable conformation of the bound ligand without introducing any human bias. Molecular Docking - The objective of molecular docking is to obtain the lowest free energy structure forthe ligand - receptor complex. As stated by Kuntz in 1994, the docking problem can be divided into three components [26]. The first is the representation of the binding

通过分子方法1和3 建模核心。所有项目都有特定的目标，可以通过核心方法 2 和 3 来辅助。 QSAR 和 对筛选的化合物进行化学信息学研究，可以提供分子特征的信息，例如 亲脂性、氢键基团的方向和分子体积。此类研究将使 阐明活性和非活性化合物的共同特征，预测生物活性 包括膜积累等特性。计算化学将分为 3 种类型 分子建模工作： 建模方法 (1)大鼠a1p2Y2五聚体GABA-A受体的比较(同源)模型。 PHS 398/2590（Rev.09/04，重新发布 4/2006） 页 o PT 1 延续格式页 首席研究员/项目总监（最后、第一、中间）：Steinbach、Joseph Henry (2) 先前使用的化合物的 QSAR 研究 (3) 化合物与受体模型的对接研究 这些分子建模研究将在我们的 David Reichert 博士的实验室中进行 放射科。 Reichert 实验室的研究主要集中在计算机辅助分子 体内成像剂的设计。作为这项工作的一部分，他们开发了类固醇建模方面的专业知识 和用于雌激素受体表达成像的类固醇类似物。为了验证他们的方法 相信可以成功应用于显像剂，他们首先开发了能够 预测已知配体与雌激素受体的两种已知亚型（a 和 P）的结合亲和力。 该工作已在《计算机辅助分子设计杂志》和《计算机辅助分子设计杂志》上发表两篇论文 分子图形与建模杂志[43,44]。 比较建模 - 为此核心提出的目标需要 GABA-A 的结构模型 受体。尽管许多酶和受体的三维结构仍然未知， 实验确定的结构数量正在迅速增长。 1995 年 10 月发布的 Protein 数据库 (PDB) [7] 有 3,821 个结构 [34]。截至 2006 年 9 月，这一数字已增至 38,620 人。 不幸的是，GABA-A 受体不是已知的结构之一。两个相关的结构是已知的并且 已被多个小组用来建立 GABA-A 受体的比较模型，这些是 来自 Lymnaea stagnalis 的乙酰胆碱结合蛋白 (AChBP) (PDB ID 119B) 和烟碱乙酰胆碱 来自鱼雷鱼雷 (PDB ID 2BG9) 的受体 (nAChR)。最近的发展实例 GABA-A 受体的比较模型来自 Trudell 和 Bertaccini [40]、Ernst 等人 [15]，以及 坎帕尼亚-斯莱特和韦弗 [9]。 Ernst 等人的工作特别有用，他们开发了一个模型 大鼠 a<\$2\2 五聚体受体 [15]。 GABA-A 受体比较模型的开发是一项艰巨的任务，主要是由于 “cys-loop”家族成员之间的序列同一性较低[14]。一般来说，具有序列同一性 >60% 的成对序列比对相当准确，但随着序列同一性 < 40%，这种情况很快就会下降。这 AChBP 和“cys 环”胞外结构域之间的序列同一性范围为 15-30%。情况为 跨膜区域更糟糕，大鼠 cti 亚基与 nAChr 的序列同一性 亚基范围为 19-21%。尽管如此，仍然可以使用保守的序列来产生合理的比对。 职位； QSAR 和 CoMFA - 定量构效关系 (QSAR) 已成为一种常用工具 自其引入以来一直是分子建模领域的一个领域[20]。事实上，它们已在以下两个方面找到了应用： 生物活性的预测，以及最近吸收、分布、代谢的预测， 有机类药物化合物的排泄和毒理学 (ADME/tox) 特性 [4,17-19,25]。相关的 CoMFA（比较分子场分析）技术已被广泛用于研究 空间场和静电场等三维分子信息之间的关系 生物活性[12,21,42]。 CoMFA 的前提是负责生物活性的药效元件 化合物的活性将用计算出的化合物的空间场和静电场来表示。经过 研究一系列化合物，称为训练集，由良好、中等和较差的化合物组成 特定蛋白质靶标的生物活性可以推断出三维药效团模型 这解释了观察到的生物活性。事实上，这个模型表明空间场和静电场可能如何 被操纵以产生具有增强生物活性的新型化合物。 CoMFA 的一项要求是 训练集中的化合物相互对齐，以便药效团的重叠 负责产生生物反应的元素被最大化。如果配体非常 由于结构不同或具有多种可能的结合模式，因此进行比对可能会出现问题。 如果与配体复合的靶蛋白的晶体结构已被解析，则 对接配体的结构可以用作模板。然而，即使在这种有利的情况下， 难以处理训练集中可能具有多种蛋白质结合构象的化合物 同时保持与模板化合物的高度药效重叠。解决这个问题的新方法 是使用能够预测结合配体的最有利构象的对接程序，而无需 引入任何人类偏见。 分子对接-分子对接的目的是获得分子的最低自由能结构 配体-受体复合物。正如 Kuntz 在 1994 年指出的，对接问题可以分为三个 组件[26]。第一个是绑定的表示