Computational design of new biocatalysts for organic synthesis

酶是已知的最有效的催化剂。它们可以将反应速率提高多达17个数量级，同时显示出精致的区域，化学和立体选择性。由于这些优点，许多酶在许多工业合成中被用作生物催化剂。我们的研究计划旨在利用蛋白质工程和设计开发用于有机合成的新生物催化剂。为此，我们将利用计算蛋白质设计（CPD）算法。这些算法预测与靶结构在能量上相容的氨基酸序列，同时允许将新特性引入靶蛋白支架中。CPD的能力来自于它对比那些可以实验测试的序列空间大得多（> 10 E80序列）的序列空间进行虚拟筛选的能力。我们将开发生物催化剂用于D-氨基酸的直接不对称合成。这些化合物在许多药物、精细化学品、农用化学品和食品添加剂的合成中是重要的手性结构单元。然而，大多数D-氨基酸要么作为商品化学品不可用，要么过于昂贵。这就需要开发更有效和更经济的方法来合成D-氨基酸。我们的研究计划将通过工程改造氨基转移酶来满足这一需求，以便它们可以合成各种具有任何所需侧链的D-氨基酸。将用于实现我们的目标的一般策略是：1）聚焦组合文库的计算设计，2）使用标准分子生物学技术的DNA文库构建，3）使用适合于高通量方法的方法的蛋白质文库筛选，以及4）阳性命中的表征。这最后一步将需要突变体的生产和纯化，以及使用基于HPLC的测定进行详细的稳态动力学表征。该研究计划的成果将是开发用于直接不对称合成D-氨基酸的新生物催化剂。这些结果将有助于开发一种更生态有效的替代现有的化学合成，并在加拿大应用绿色化学。

Enzymes are the most efficient catalysts known. They can increase the rate of a reaction by up to 17 orders of magnitude while displaying exquisite regio-, chemo- and stereoselectivity. Because of these advantages, many enzymes are being exploited as biocatalysts in numerous industrial syntheses. Our research program aims to develop new biocatalysts for organic synthesis using protein engineering and design. To do this, we will exploit computational protein design (CPD) algorithms. These algorithms predict amino acid sequences that are energetically compatible with the target structure while simultaneously allowing a novel property to be introduced into the target protein scaffold. The power of CPD results from its capability to perform virtual screening of sequence spaces astronomically larger (>10E80 sequences) than those that can be experimentally tested. We will develop biocatalysts for the direct asymmetric synthesis of D-amino acids. These compounds are important chiral building blocks in the synthesis of many pharmaceuticals, fine chemicals, agrochemicals, and food additives. However, most D-amino acids are either unavailable as commodity chemicals or are prohibitively expensive. This has created a need for more efficient and economical methods for the synthesis of D-amino acids to be developed. Our research program will address this need by engineering aminotransferases so that they can synthesize a wide range of D-amino acids with any desired side chain. The general strategy that will be employed to achieve our goals is: 1) computational design of focused combinatorial libraries, 2) DNA library construction using standard molecular biology techniques, 3) protein library screening using methods amenable to high-throughput approaches, and 4) characterization of positive hits. This last step will require production and purification of mutants, and detailed steady-state kinetic characterization using an HPLC-based assay. Outcomes of this research program will be the development of new biocatalysts for the direct asymmetric synthesis of D-amino acids. These results will contribute to the development of a more eco-efficient alternative to available chemical syntheses, and the application of green chemistry in Canada.