Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning.

ABSTRACT: Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning, which we applied to engineer an artificial metalloenzyme (ArM) catalyzing a new-to-nature hydroamination reaction. Combining lab automation and next-generation sequencing, we acquired sequence-activity

SUBMITTER: Vornholt T 

PROVIDER: S-EPMC11273458 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature