热稳定性
Python(编程语言)
计算机科学
脚本语言
定向进化
化学信息学
生物信息学
蛋白质工程
计算生物学
软件工程
生化工程
生物信息学
化学
程序设计语言
酶
工程类
生物
生物化学
突变体
基因
作者
Adrian Tripp,Markus Braun,Florian Wieser,Gustav Oberdorfer,Horst Lechner
标识
DOI:10.1002/cbic.202400092
摘要
Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.
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