定向进化
定向分子进化
蛋白质工程
计算生物学
选择(遗传算法)
酶
蛋白质进化
自然选择
抽象
功能(生物学)
蛋白质设计
蛋白质结构
基因
生物
计算机科学
遗传学
生物化学
突变体
人工智能
哲学
认识论
作者
David Patsch,Thomas Schwander,Moritz Voß,Daniela Schaub,Sean Hüppi,Michael Eichenberger,Peter Stockinger,Lisa Schelbert,Sonja Giger,Francesca Peccati,Gonzalo Jiménez‐Osés,Mojmír Mutný,Andreas Krause,Uwe T. Bornscheuer,Donald Hilvert,Rebecca Buller
标识
DOI:10.1038/s41589-024-01712-3
摘要
Abstract Darwinian evolution has given rise to all the enzymes that enable life on Earth. Mimicking natural selection, scientists have learned to tailor these biocatalysts through recursive cycles of mutation, selection and amplification, often relying on screening large protein libraries to productively modulate the complex interplay between protein structure, dynamics and function. Here we show that by removing destabilizing mutations at the library design stage and taking advantage of recent advances in gene synthesis, we can accelerate the evolution of a computationally designed enzyme. In only five rounds of evolution, we generated a Kemp eliminase—an enzymatic model system for proton transfer from carbon—that accelerates the proton abstraction step >10 8 -fold over the uncatalyzed reaction. Recombining the resulting variant with a previously evolved Kemp eliminase HG3.17, which exhibits similar activity but differs by 29 substitutions, allowed us to chart the topography of the designer enzyme’s fitness landscape, highlighting that a given protein scaffold can accommodate several, equally viable solutions to a specific catalytic problem.
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