Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, UK.
Laboratory of Organic Chemistry, ETH Zürich, Zürich, Switzerland.
Nature. 2022 Jun;606(7912):49-58. doi: 10.1038/s41586-022-04456-z. Epub 2022 Jun 1.
The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.
从头设计高效酶将对化学、生物技术和医学产生深远的影响。过去十年中蛋白质工程的快速发展使我们乐观地认为这一目标是可以实现的。含有金属辅因子和非经典有机催化基团的人工酶的开发表明，蛋白质结构可以如何被优化以利用非蛋白质元素的反应性。与此同时，基于过渡态稳定的基本原理，计算方法已被用于设计用于各种反应的蛋白质催化剂。尽管设计催化剂的活性相当低，但已经广泛使用实验室进化来产生高效酶。对这些系统的结构分析揭示了设计具有更高活性的催化剂所需的高精度。为此，新兴的蛋白质设计方法，包括深度学习，为提高模型准确性提供了特别的前景。在这里，我们总结了该领域的关键进展，并强调了新的创新机会，这应该使我们能够超越当前的技术水平，并能够稳健地设计生物催化剂以满足社会需求。
