工作流程
生化工程
计算机科学
体内
可扩展性
合成生物学
吞吐量
定向进化
生物技术
计算生物学
工程类
化学
生物
无线
生物化学
电信
数据库
突变体
基因
作者
Enrico Orsi,Lennart Schada von Borzyskowski,Stephan Noack,Pablo I. Nikel,Steffen N. Lindner
标识
DOI:10.1038/s41467-024-46574-4
摘要
Abstract Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
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