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

项目摘要 这项建议描述了新的统计方法的发展，以评估弱的，非共价相互作用 参与不对称催化的。非共价相互作用对于生物识别和功能都是必不可少的 生物催化剂--酶。该项目将在两个对映体收敛取代的背景下进行 α-氯甘氨酸酯的反应。建议的反应将提供获得有价值的芳基和烯丙基的非天然途径。 A-氨基酸，而目前的合成方法会导致对映体选择性或产率较低。初步结果显示 当芳基吡咯烷基方酰胺类化合物催化时，所提出的反应能够具有高的对映选择性， 但对最佳催化剂的测试显示，在对映体选择性和低产率方面出现了不直观的趋势。改善电流 用于反应优化的能力，将开发新的统计方法以允许同时优化产率 和对映体选择性。将选出一组性能好、性能中等和性能差的催化剂，并将反应 使用每种催化剂进行反应。将测量对映体选择性和反应速率来建立数据集。立体的 并将对催化剂的电子特征进行计算建模，以生成用于统计分析的参数。 然后将进行多变量线性回归，以生成对映选择性和产率的预测模型。 在优化这些反应后，将用每种催化剂在一定范围内进行对映体选择性测量。 温度。根据这些数据，可以计算出每个反应中单个催化剂的∆∆H‡和∆∆S‡。预测统计 然后将从为∆∆H‡和∆∆S‡开发的计算参数库中创建模型 优化模型。根据这些模型中出现的参数，与 可以确定催化剂的对映体选择性。各相关结构的焓和熵的贡献 也可以对特征进行定量评估。这一结果将有助于更深入地理解非共价 相互作用在这些反应的对映体确定步骤中起作用。由于这一知识，改进的催化剂和 可以设计基板。将建议的方法应用于任何催化转化都会产生更多 该反应的高效反应优化和催化剂设计。最终，这项工作可能带来从头开始的催化剂。 在创建全面的计算参数和统计模型库之后进行设计，包括 ∆∆G‡、Rate、∆∆H‡和∆∆S‡代表反应组。拟议工作的影响不仅是 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 酸。这些化合物对生命系统的生物学研究是必不可少的，这种方法可能会推动 保健药物的合成。

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.