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Automated in vivo enzyme engineering accelerates biocatalyst optimization

Enrico Orsi,Lennart Schada von Borzyskowski,Stephan Noack,P. Nikel,SN Lindner

Published 2024 in Nature Communications

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. Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. In this Perspective, the authors propose an integrated solution combining growth-coupled selection with machine learning and automated workflows to accelerate development pipelines.

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