School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332-0363, USA.
Curr Opin Chem Biol. 2011 Apr;15(2):194-200. doi: 10.1016/j.cbpa.2010.11.011. Epub 2010 Nov 27.
Recent advances in the development of both experimental and computational protein engineering tools have enabled a number of further successes in the development of biocatalysts ready for large-scale applications. Key tools are first, the targeting of libraries, leading to far smaller but more useful libraries than in the past, second, the combination of structural, mechanistic, and sequence-based knowledge often based on prior successful cases, and third, the advent of structurally based algorithms allowing the design of novel functions. Based on these tools, a number of improved biocatalysts for pharmaceutical applications have been presented, such as an (R)-transaminase for the synthesis of active pharmaceutical ingredients (APIs) of sitagliptin (Januvia®) and ketoreductases, glucose dehydrogenases, and haloalkane dehalogenases for the API synthesis toward atorvastatin (Lipitor®) and montelukast (Singulair®).
近年来,实验和计算蛋白质工程工具的发展取得了一些新的进展,为开发可用于大规模应用的生物催化剂提供了更多的成功机会。关键的工具包括:首先,文库的靶向筛选,得到的文库比过去更小,但更有用;其次,结构、机制和基于序列的知识的结合,这些知识通常基于以前的成功案例;第三,基于结构的算法的出现,允许设计新的功能。基于这些工具,已经提出了许多用于药物应用的改进型生物催化剂,例如用于合成西他列汀(Januvia®)活性药物成分(API)的(R)-转氨酶,以及用于阿托伐他汀(Lipitor®)和孟鲁司特(Singulair®)API 合成的酮还原酶、葡萄糖脱氢酶和卤代烷脱卤酶。