Advanced Studies of Molecular Recognition Processes

分子识别过程的高级研究

• 批准号: 7583341
• 负责人: Eric V. Anslyn
• 金额: $ 24.55万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7583341
• 关键词: Affinity; Anions; Area; Binding; Biological Neural Networks; Cations; Chemicals; Complex; Computer Simulation; Cyclodextrins; Data; Data Set; Electrostatics; Entropy; Event; Financial compensation; Free Energy; Goals; Hydrogen Bonding; Ions; Lead; Light; Measures; Metals; Methods; Modeling; Molecular; Multivariate Analysis; Organic Chemistry; Pattern Recognition; Process; Protocols documentation; Published Comment; Research; Research Proposals; Series; Solvents; Structure; Surface; Testing; Thermodynamics; Training; Water; Work; base; chelation; design; enthalpy; functional group; improved; insight; molecular recognition; predictive modeling; programs; public health relevance; receptor; research study; theories; Affinity; Anions; Area; Binding; Biological Neural Networks; Cations; Chemicals; Complex; Computer Simulation; Cyclodextrins; Data; Data Set; Electrostatics; Entropy; Event; Financial compensation; Free Energy; Goals; Hydrogen Bonding; Ions; Lead; Light; Measures; Metals; Methods; Modeling; Molecular; Multivariate Analysis; Organic Chemistry; Pattern Recognition; Process; Protocols documentation; Published Comment; Research; Research Proposals; Series; Solvents; Structure; Surface; Testing; Thermodynamics; Training; Water; Work; base; chelation; design; enthalpy; functional group; improved; insight; molecular recognition; predictive modeling; programs; public health relevance; receptor; research study; theories

DESCRIPTION (provided by applicant): This application describes two hypothesis driven projects. In brief, the first hypothesis is that the enthalpy and entropy of molecular recognition events can be correlated to solvation parameters, buried surface area, and electrostatic interactions between the hosts and guests. The correlations may be as simple as linear relationships, but they are expected to be more complex, possibly requiring artificial neural networks. The second hypothesis is that cooperative binding will follow a compensating enthalpy/entropy relationship that is influenced by differences in solvation effects, buried surface area, and the molecular recognition contacts involved. In Project 1 we will use three classes of synthetic receptors: Anslyn group receptors, Schneider group receptors, and cyclodextrins. We will measure the enthalpy and entropy of binding of a series of guests, measure solvation parameters of the guests, computationally model buried surface area upon binding, and determine the electrostatic contacts created upon binding. The data will be used as training sets for pattern recognition routines to predict the enthalpy and entropy of binding to guests not used in the training sets. In Project 2 cooperativity between three different binding forces in water will be studied: ion-pairing, metal-chelation, and the hydrophobic effect. We define three guests, A, B and AB, where A and B are subunits of AB. Our studies will involve ITC analyses of the binding of A, B, and AB to a host. Using mathematical relationships we derive herein, the results from the ITC studies will give the enthalpy or entropy origin of the various binding events. Because the guests are synthetic and structurally simple, several can be quickly generated to reveal varying degrees of negative and positive cooperativity. After examining the enthalpy and entropy of binding A, B, and AB for the series of guests, a compensation relationship for the enthalpy/entropy of connection will be generated (Eq. 7 herein). Lastly, the compensating enthalpy and entropy of connection will then be correlated with solvation parameters, buried surface area, and electrostatic contacts, tying together projects 1 and 2, and revealing the chemical basis for cooperative binding. PUBLIC HEALTH RELEVANCE: The work described herein focuses upon two aspects of the physical organic chemistry of molecular recognition. The first deals with creating protocols to predict binding affinities, while the second involves exploring methods to achieve positive cooperativity.

描述（由申请人提供）：本申请描述了两个假设驱动的项目。简而言之，第一个假设是分子识别事件的焓和熵可以与宿主与来宾之间的溶剂化参数，埋入表面积和静电相互作用相关。相关性可能与线性关系一样简单，但期望它们更复杂，可能需要人工神经网络。第二个假设是，合作结合将遵循补偿焓/熵关系，该关系受溶剂效应，埋入表面积和所涉及的分子识别接触的影响。在项目1中，我们将使用三类合成受体：Anslyn组受体，施耐德组受体和环糊精。我们将测量一系列客人结合的焓和熵，测量来宾的溶剂化参数，结合后的计算模型掩埋的表面积，并确定结合后产生的静电接触。数据将用作模式识别程序的训练集，以预测与未在训练集中使用的客人结合的焓和熵。在项目2中，将研究水中三种不同结合力之间的合作性：离子配对，金属旋转和疏水作用。我们定义了三位客人A，B和AB，其中A和B是AB的亚基。我们的研究将涉及对A，B和AB与宿主结合的ITC分析。使用我们在此得出的数学关系，ITC研究的结果将给出各种结合事件的焓或熵起源。由于客人是合成的，在结构上很简单，因此可以迅速生成几个来揭示不同程度的负和积极合作。在检查了一系列客人的绑定A，B和AB的焓和熵之后，将生成连接焓/熵的补偿关系（本文等式7）。最后，补偿焓和连接的熵将与溶剂化参数，埋入的表面积和静电触点相关，将项目1和2绑在一起，并揭示了合作结合的化学基础。 公共卫生相关性：本文所述的工作着重于分子识别的物理有机化学的两个方面。第一个涉及创建协议以预测约束亲和力，而第二个则涉及探索实现积极合作的方法。