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.

中文翻译：

---

通过合理的计算设计策略释放酶工程的潜力


酶通过实现高效、环保和可持续的化学过程，在各个行业中发挥着关键作用。然而，酶的低周转率和较差的底物选择性限制了其大规模应用。在计算算法的推动下，合理的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。采用合理的计算蛋白质工程策略来克服这些问题已经取得了显着的进步，但迄今为止尚未得到全面的审查。本文回顾了理性计算酶设计的最新进展，将其分为三种类型：基于结构、基于序列和数据驱动的机器学习计算设计。案例研究证明了催化活性、稳定性和底物选择性的成功增强。最后，本文对这些方法进行了全面分析，强调了现有的挑战和潜在的解决方案，并对未来的发展方向提出了见解。