ArticleACS central science2024
Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning.
Article in ACS central science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial metalloenzymes in complex biological environments.Nature chemical biology · 2026Review
- Combinatorial Optimization of Protein Systems in Synthetic Cells.ACS synthetic biology · 2026Article
- Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.ACS central science · 2025Review
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- Data-driven protease engineering by DNA-recording and epistasis-aware machine learning.Nature communications · 2025Article
- Designing Enzymatic Reactivity with an Expanded Palette.Chembiochem : a European journal of chemical biology · 2025Review
- Single-Walled Carbon Nanotube Probes for Protease Characterization Directly in Cell-Free Expression Reactions.Analytical chemistry · 2025Article
- Biophysics-based protein language models for protein engineering.bioRxiv : the preprint server for biology · 2025Article
- Active learning-assisted directed evolution.Nature communications · 2025Article
- Single-Walled Carbon Nanotube Probes for Protease Characterization Directly in Cell-Free Expression Reactions.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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 data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds. Critical characteristics of our pipeline include the cost-effective generation of information-rich data sets, the integration of an explorative round to improve the model's performance, and the inclusion of experimental noise. Our approach led to an order-of-magnitude boost in the hit rate while making efficient use of experimental resources. Search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.