ArticleNature communications2025
Active learning-assisted directed evolution.
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 48 papers.
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Who cites it
48 citing papers in PubMed.
- Advances in Nanomedicine for Brain Tumors: Overcoming Biological Barriers, Targeting Strategies, and Future Directions.Current neurology and neuroscience reports · 2026Review
- Sequence-free landscape inference for directed evolution.PLoS computational biology · 2026Article
- Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors.Nature biotechnology · 2026Article
- Combinatorial Optimization of Protein Systems in Synthetic Cells.ACS synthetic biology · 2026Article
- Accelerating protein design by scaling experimental characterization.Nature communications · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Directed evolution of a cytochrome P450 monooxygenase for improved perillyl alcohol biosynthesisRSC advances · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- A multi-fidelity tabular prior-data fitted network model for accurate prediction and uncertainty quantification.Nature communications · 2026Article
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 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
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 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
- Deep learning-guided dual-fitness evolution of T7 RNA polymerase for enhanced stability and activity.Nucleic acids research · 2026Article
- From Gene Knockouts to Genome Remodeling: Large DNA Fragment Deletion Technologies in Plants.Plants (Basel, Switzerland) · 2026Review
- Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.Nucleic acids research · 2026Article
- Enzyme Catalytic Parameters and Evolution Across the Dissipation Plane.International journal of molecular sciences · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. We apply ALDE to an engineering landscape that is challenging for DE: optimization of five epistatic residues in the active site of an enzyme. In three rounds of wet-lab experimentation, we improve the yield of a desired product of a non-native cyclopropanation reaction from 12% to 93%. We also perform computational simulations on existing protein sequence-fitness datasets to support our argument that ALDE can be more effective than DE. Overall, ALDE is a practical and broadly applicable strategy to unlock improved protein engineering outcomes.
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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.