蛋白质设计
酶
脚手架
催化作用
蛋白质工程
化学
组合化学
支架蛋白
活动站点
定向进化
合理设计
氨基酸
生化工程
合成生物学
氨基酸残基
定向分子进化
纳米技术
催化效率
酶催化
蛋白质结构
生物催化
计算生物学
结构母题
生物化学
设计策略
反应条件
靶蛋白
计算机科学
设计要素和原则
主题(音乐)
作者
Markus Braun,Adrian Tripp,Morakot Chakatok,Sigrid Kaltenbrunner,C. Fischer,D Stoll,Aleksandar Bijelic,Wael Elaily,Massimo G. Totaro,M. Moser,Shlomo Yakir Hoch,Horst Lechner,Federico Rossi,Matteo Aleotti,Mélanie Hall,Gustav Oberdorfer
出处
期刊:Nature
[Nature Portfolio]
日期:2025-12-03
卷期号:649 (8095): 237-245
被引量:8
标识
DOI:10.1038/s41586-025-09747-9
摘要
Enzymes find broad use as biocatalysts in industry and medicine owing to their exquisite selectivity, efficiency and mild reaction conditions. Custom-designed enzymes can produce tailor-made biocatalysts with potential applications that extend beyond natural reactions. However, current design methods require testing a large number of designs and mostly produce de novo enzymes with low catalytic activities1-3. As a result, they require costly experimental optimization and high-throughput screening to be industrially viable4,5. Here we present rotamer inverted fragment finder-diffusion (Riff-Diff), a hybrid machine learning and atomistic modelling strategy for scaffolding catalytic arrays in de novo proteins. We highlight the general applicability of Riff-Diff by designing enzymes for two mechanistically distinct chemical transformations, the retro-aldol reaction and the Morita-Baylis-Hillman reaction. We show that in both cases, it is possible to generate catalysts that exhibit activities rivalling those optimized by in vitro evolution, along with exquisite stereoselectivity. High-resolution structures of six of the designs revealed near-atomic active site design precision. The design strategy can, in principle, be applied to any catalytically competent amino acid array. These findings lay the basis for practical applicability of de novo protein catalysts in synthesis and describe fundamental principles of protein design and enzyme catalysis.
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