Building Enzymes through Design and Evolution
Building Enzymes through Design and Evolution
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DOI：
10.1021/acscatal.3c02746
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发表时间：
2023-09
期刊：
影响因子：
12.9
通讯作者：
Euan J. Hossack;Florence J. Hardy;Anthony P Green
中科院分区：
文献类型：
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作者：
Euan J. Hossack;Florence J. Hardy;Anthony P Green
设计高效的酶是现代生物催化领域的一项艰巨挑战。在这里，我们回顾了该领域的最新发展，并说明了计算设计和先进的蛋白质工程之间的相互作用如何产生了具有不同活性的酶。天然蛋白质已经通过计算重新设计，嵌入了设计的催化位置，提供了活性催化剂，可以通过实验室进化来优化，以提高效率和选择性。计算设计工具可以可靠地产生稳定的新蛋白，其形状和骨架几何形状超出了自然界中的那些，这可以作为理想的模板来承载催化位点。遗传密码重编程方法已被用来将额外的功能元件引入蛋白质活性部位，以扩大设计酶可获得的化学成分的范围。最后，最近出现的基于深度学习的强大的蛋白质设计工具有望通过极大地提高设计速度和模型精度，对该领域产生革命性的影响。通过将酶设计的最新计算和实验工具结合在一起，我们乐观地认为，从零开始可靠地构建有用的生物催化剂的雄心是触手可及的。
Designing efficient enzymes is a formidable challenge at the forefront of modern biocatalysis. Here, we review recent developments in the field and illustrate how the interplay between computational design and advanced protein engineering has given rise to enzymes with diverse activities. Natural proteins have been re-engineered computationally to embed designed catalytic sites, affording active catalysts that can be optimized through laboratory evolution to enhance efficiency and selectivity. Computational design tools can reliably generate stablede novoproteins with shapes and backbone geometries beyond those found in nature, which can serve as idealized templates for hosting catalytic sites. Genetic code reprogramming methods have been used to introduce additional functional elements into protein active sites to expand the range of chemistries accessible with designer enzymes. Finally, the recent emergence of powerful protein design tools based on deep learning promises to have a transformative impact on the field by greatly increasing the design speed and model accuracy. By bringing together the latest computational and experimental tools for enzyme design, we are optimistic that the ambition of reliably building useful biocatalysts from scratch is within reach.