State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Biotechnol Adv. 2024 Jul-Aug;73:108376. doi: 10.1016/j.biotechadv.2024.108376. Epub 2024 May 11.
Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.
酶在各个行业中发挥着关键作用，能够实现高效、环保和可持续的化学工艺。然而，酶的周转率低和底物选择性差限制了它们的大规模应用。借助计算算法的合理计算酶设计提供了一种更有针对性且劳动强度更低的方法。目前已经在采用合理的计算蛋白质工程策略来克服这些问题方面取得了显著进展，但迄今为止尚未进行全面综述。本文综述了合理计算酶设计的最新进展，将其分为基于结构、基于序列和基于数据的机器学习计算设计三种类型。通过案例研究展示了在催化活性、稳定性和底物选择性方面的成功增强。最后，本文对这些方法进行了全面分析，强调了现有挑战和潜在解决方案，并对未来的发展方向提出了见解。
