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

开发统计模型来识别影响不对称催化的结构特征和非共价相互作用

• 批准号: 10399188
• 负责人: Jacquelyne Aliscia Read
• 金额: $ 1.71万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10399188
• 关键词: Amino Acids; Automobile Driving; Binding; Biological; Biological Process; Catalysis; Cations; Charge; Computer Models; Data; Data Set; Dependence; Development; Entropy; Enzymes; Esters; GTP-Binding Protein alpha Subunits, Gs; Health; Hydrogen Bonding; Individual; Libraries; Life; Linear Regressions; Measurement; Measures; Medicine; Methodology; Methods; Modeling; Nature; Organism; Outcome; Pharmacologic Substance; Process; Proteins; Reaction; Research; Role; Savings; Statistical Data Interpretation; Statistical Methods; Statistical Models; Stretching; Structure; Synthesis Chemistry; System; Temperature; Testing; Time; Training; Validation; Work; base; catalyst; chemical synthesis; design; enthalpy; improved; insight; knowledge of results; predictive modeling; process optimization; reaction rate; screening; trend; tv watching; unnatural amino acids; virtual library

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.

项目概要 该提案描述了评估弱非共价相互作用的新统计方法的开发 参与不对称催化。非共价相互作用对于生物识别和功能至关重要 生物催化剂——酶。该项目将在两种对映异构体取代的背景下进行 α-氯甘氨酸酯的反应。所提出的反应将方便地获得有价值的芳基和烯丙基非天然化合物 α-氨基酸，而目前的合成方法可能导致对映选择性或产率较低。初步结果显示 当由芳基吡咯烷方酰胺衍生物催化时，所提出的反应能够具有高对映选择性， 但对最佳催化剂的测试揭示了对映选择性和低产率的非直观趋势。为改善电流 反应优化能力，将开发新的统计方法以允许同时优化产量 和对映选择性。将选择一组性能良好、中等和较差的催化剂，并且反应将被 使用每种催化剂进行。将测量对映选择性和反应速率以构建数据集。空间位阻 催化剂的电子特征将通过计算建模以生成用于统计分析的参数。 然后将进行多元线性回归以生成对映选择性和产率的预测模型。 优化这些反应后，将对每种催化剂在一定范围内进行对映选择性测量 温度。根据这些数据，可以计算每个反应中各个催化剂的 ΔΔH‡ 和 ΔΔS‡。预测统计 然后，将从为 ΔΔH‡ 和 ΔΔS‡ 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 可以鉴定催化剂的对映选择性。每个相关结构的焓和熵贡献 还可以对特征进行定量评估。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 可以设计基板。将所提出的方法应用于任何催化转化将导致更多 该反应的有效反应优化和催化剂设计。最终，这项工作可能带来从头催化剂 创建综合计​​算参数库和统计模型后进行设计 代表反应组的 ΔΔG‡、速率、ΔΔH‡ 和 ΔΔS‡。拟议工作的影响不仅 对不对称催化领域做出了贡献，但它也将提供获得非天然α-氨基的改进方法 酸。这些化合物对于生命系统的生物学研究至关重要，这种方法可以促进 改善健康药物的合成。