ArticleNature communications2025
Accelerated enzyme engineering by machine-learning guided cell-free expression.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed.
- Enhancing prediction accuracy for enzyme activity engineering through sequence co-evolution and epistatic relationship modeling.Nucleic acids research · 2026Article
- Decoding the distribution, structure-function-redox potential relationship and recent advances in fungal laccases: a systematic approach.Bioprocess and biosystems engineering · 2026Review
- Accelerating protein design by scaling experimental characterization.Nature communications · 2026Article
- Transaminases engineering and their cascade systems for natural product synthesis.Applied biochemistry and biotechnology · 2026Review
- Microbial cell-free protein synthesis and its progression toward industrial use.Microbiology (Reading, England) · 2026Review
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Overcoming cellular secretion bottlenecks: advanced secretion engineering and molecular tailoring for next-generation microbial α-amylases with enhanced industrial performance.World journal of microbiology & biotechnology · 2026Review
- Merging Biocatalysis and Chemocatalysis in Flow: State-of-the-Art and Future Directions for Sustainable Synthesis.Angewandte Chemie (International ed. in English) · 2026Review
- Directed evolution of enzymes at the crossroads of tradition and innovation.FEBS open bio · 2026Review
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- Machine learning-driving optimization and spatial assembly of a cell-free system for high-yield liquiritigenin production.Advanced biotechnology · 2026Article
- Design-driven optimization of low-cost reagent formulations for reproducible and high-yielding cell-free gene expression.Nature communications · 2026Article
- Computational rational design of unspecific peroxygenase for C-H oxidation.Science advances · 2026Article
- Toward the Chemoenzymatic Synthesis of DNA-Encoded Libraries.ACS central science · 2026Review
- Green bioconversion of insoluble chitin: chitinase development pathways via multi-strategy synergy.Bioresources and bioprocessing · 2026Review
- Current Progress and Future Directions of Enzyme Technology in Food Nutrition: A Comprehensive Review of Processing, Nutrition, and Functional Innovation.Foods (Basel, Switzerland) · 2026Review
- Accessible biocatalyst development by rapidiScience · 2026Article
- Screening and machine learning-based prediction of translation-enhancing peptides that reduce ribosomal stalling inRSC chemical biology · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions. We apply this platform to engineer amide synthetases by evaluating substrate preference for 1217 enzyme variants in 10,953 unique reactions. We use these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small molecule pharmaceuticals. Over these nine compounds, ML-predicted enzyme variants demonstrate 1.6- to 42-fold improved activity relative to the parent. Our ML-guided, cell-free framework promises to accelerate enzyme engineering by enabling iterative exploration of protein sequence space to build specialized biocatalysts in parallel.
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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.