Earticle

초록

영어

In the past decade methods of directed evolution has become a widely accepted and broadly applied method for biocatalyst engineering. A directed evolution experiment comprises two main steps: generating diverse mutant libraries and screening for improved protein variants. Although the vast majority of reported directed evolution experiments use a combination of error-prone PCR and DNA shuffling, methods for constructing diverse molecular libraries continue to accumulate. More important than the motivation to circumvent patent laws, these new approaches aim to generate more comprehensive, less biased libraries, because the quality of a mutant library is decisive for the success of a directed evolution experiment. This emerging focus on library quality is also reflected in new computational methods. In parallel with experimental techniques, computational predictive frameworks have been dramatic advances in design and analysis of directed evolution experiments. Recently, chemical DNA synthesis has been adopted to make various controlled mutant libraries with low bias and high fidelity, because of high efficiency, fidelity, cost reduction and process automation. Advanced mathematical and data-mining tools in computer modeling have been applied to analyze sequence-activity or sequence-stability relationships and to assist in the design of proteins with specified properties. It is unclear now, however, whether it is more efficient to mutate an enzyme randomly
or to mutate active sites or key sites specifically designed by computer modeling. We expect that the powerful combination of in silico and in vitro methods based on DNA synthesis and computational analysis for directed evolution will further accelerate the successful development of desired biocatalysts.

저자

• Eui-Joong Kim [ GenoFocus, Inc. ]
• Jae-Gu Pan [ KRIBB ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국생물공학회 학술대회

• 간기

  반년간

• 수록기간

  1985~2013

• 십진분류

  KDC 476 DDC 576