化学
酶
功能(生物学)
生物化学
蛋白质设计
细胞生物学
生物
蛋白质结构
计算生物学
结构功能
底物特异性
肽序列
生物物理学
作者
Dina Listov,Sarel Jacob Fleishman
标识
DOI:10.1016/j.sbi.2026.103252
摘要
The ultimate mission of de novo enzyme design methodology is to develop strategies that produce new-to-nature enzymes that match the efficiency and versatility of natural ones. Until recent years, design methods yielded enzymes with low catalytic efficiencies even for simple reactions, underscoring the need for tighter control over backbone structure and active-site preorganization. Two approaches have recently emerged to address these problems: artificial intelligence-driven design of de novo folds and evolution-guided atomistic design of natural folds. These strategies have produced catalysts with efficiencies approaching natural enzymes, but achieving high catalytic rates and complex, multistep mechanisms remains challenging. We argue that progress toward high-performance enzymes for novel reactions requires more precise control over complex natural folds and close collaboration between designers and computational chemists.
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