Development of Statistical Models to Identify Structural Features and Noncovalent Interactions Influencing Asymmetric Catalysis

项目摘要 该提案描述了新的统计方法的发展，以评估弱，非共价相互作用 参与了不对称催化。非共价相互作用对于生物识别以及功能是必不可少的。 生物催化剂--酶。该项目将在两个对映体会聚取代的背景下进行 a-氯代甘氨酸酯的反应。这些反应将为合成有价值的芳基和烯丙基非天然产物提供便利。 α-氨基酸，而目前的合成方法可能导致低的对映选择性或产率。初步结果显示 所提出的反应在被芳基吡咯烷并方酰胺衍生物催化时能够具有高的对映选择性， 但是对最佳催化剂的测试揭示了对映体选择性和低产率的非直观趋势。改进目前 反应优化的能力，新的统计方法将被开发，以允许同时优化产率 和对映选择性。将选择一组性能良好、中等和较差的催化剂，并将反应 使用每种催化剂。将测量对映体选择性以及反应速率以构建数据集。空间 并且催化剂的电子特征将被计算建模以产生用于统计分析的参数。 然后进行多元线性回归以生成对映选择性和产率的预测模型。 在优化这些反应之后，将在一定范围内用每种催化剂进行对映选择性测量。 温度从这些数据中，可以计算每个反应中单个催化剂的H2O和H2O S。预测统计 然后，将从为该项目开发的计算参数库中， 优化模型基于这些模型中出现的参数，与 可以确定催化剂的对映选择性。每个相关结构的熵和熵贡献 也可以定量评估。这一结果将有助于更深入地了解非共价键如何 相互作用在这些反应的对映体决定步骤中起作用。由于这些知识，改进的催化剂和 可以设计衬底。将拟议的方法应用于任何催化转化， 有效反应优化和用于该反应的催化剂设计。最终，这项工作可以带来从头催化剂， 在创建一个全面的计算参数和统计模型库之后进行设计， 代表性反应组的反应速率、反应速率和反应速率。拟议工作的影响不仅将 有助于不对称催化领域，但它也将提供获得非天然α-氨基的改进方法， acids.这些化合物对生命系统的生物学研究至关重要，这种方法可以促进生物学的发展。 保健药品的合成。

Project Summary This proposal describes the development of new statistical methods to evaluate the weak, noncovalent interactions involved in asymmetric catalysis. Noncovalent interactions are essential to biological recognition as well as to the function of biological catalysts—enzymes. The project will be conducted in the context of two enantioconvergent substitution reactions of a-chloroglycine esters. The proposed reactions would provide facile access to valuable aryl and allylic unnatural a-amino acids, whereas current synthetic methods can result in low enantioselectivities or yields. Preliminary results show the proposed reactions to be capable of high enantioselectivities when catalyzed by arylpyrrolidino squaramide derivatives, but testing for the optimal catalyst has revealed nonintuitive trends in enantioselectivities and low yields. To improve current capabilities for reaction optimization, new statistical methods will be developed to allow simultaneous optimization of yield and enantioselectivity. A set of good-, modest-, and poor-performing catalysts will be selected, and reactions will be performed using each catalyst. Enantioselectivities as well as reaction rates will be measured to build the data set. Steric and electronic features of the catalysts will be modeled computationally to generate parameters for the statistical analysis. A multivariate linear regression will then be conducted to generate predictive models for both enantioselectivity and yield. After optimizing these reactions, enantioselectivity measurements will be made with each catalyst over a range of temperatures. From this data, ∆∆H‡ and ∆∆S‡ can be calculated for individual catalysts in each reaction. Predictive statistical models will then be created for ∆∆H‡ and ∆∆S‡, drawing from the computational parameter library developed for the optimization models. Based on the parameters that appear in these models, structural features relevant to the enantioselectivities of the catalysts can be identified. The enthalpic and entropic contributions of each relevant structural feature could also be quantitatively assessed. This outcome would contribute to a deeper understanding of how noncovalent interactions operate in the enantiodetermining step of these reactions. As a result of this knowledge, improved catalysts and substrates could be designed. Application of the proposed methodology to any catalytic transformation would result in more efficient reaction optimization and catalyst design for that reaction. Eventually, this work could bring about de novo catalyst design following the creation of a comprehensive library of computational parameters and statistical models encompassing ∆∆G‡, rate, ∆∆H‡, and ∆∆S‡ for representative groups of reactions. The impact of the proposed work would not only contribute to the field of asymmetric catalysis, but it would also provide improved methods of accessing unnatural a-amino acids. These compounds are essential to biological studies of living systems, and this methodology could advance the synthesis of health-improving pharmaceuticals.