ReviewNature methods2019
Machine-learning-guided directed evolution for protein engineering.
Review in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 429 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
429 citing papers in PubMed, 29 citations in OpenAlex.
- Article
- Transaminases engineering and their cascade systems for natural product synthesis.Applied biochemistry and biotechnology · 2026Review
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Advances in amidases and urethanases as depolymerization biocatalysts.Nature chemical biology · 2026Review
- Alkaliphilic microbial cellulases: sources, structural insights, and bioengineering approaches for industrial applications.Archives of microbiology · 2026Review
- Do We Still Need Chemistry for Water-Soluble Prodrugs? What Biocatalysis Already Does, and What It Does Not.Pharmaceutics · 2026Review
- Enhancing protein expression in humans through codon optimization with transformer and contrastive learning.Molecular therapy. Nucleic acids · 2026Article
- Malathion biodegradation and biotransformation in water and sediment systems: bacterial pathways, environmental drivers, ecological impacts, and bioremediation strategies.World journal of microbiology & biotechnology · 2026Review
- Amylosucrase as a Versatile Biocatalyst for Next-Generation Functional Ingredients: Starch Modification and Glycoside Synthesis.Comprehensive reviews in food science and food safety · 2026Review
- Recent advances in AI-driven deep eutectic solvent design for biocatalysis: Co-design of extremozyme and reaction medium.Biodesign research · 2026Review
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Enhancing Enzyme Activity Via Noncovalent Association with DNA-Functionalized Nanoparticles.JACS Au · 2026Article
- Accelerating protein engineering: an integrated framework combines protein language models and epistatic landscape modeling.Signal transduction and targeted therapy · 2026Article
- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Article
- Optimizing protein design through uncertainty-weighted steering of protein language models.Bioinformatics (Oxford, England) · 2026Article
- Mitigating Goodhart's law in epitope-conditioned TCR generation using plug-and-play reward designs.Bioinformatics (Oxford, England) · 2026Article
- Concepts of RNA virus evolution for the design of better antiviral countermeasures.Nature reviews. Microbiology · 2026Review
369 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the underlying physics or biological pathways. Such methods accelerate directed evolution by learning from the properties of characterized variants and using that information to select sequences that are likely to exhibit improved properties. Here we introduce the steps required to build machine-learning sequence-function models and to use those models to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to the use of machine learning for protein engineering, as well as the current literature and applications of this engineering paradigm. We illustrate the process with two case studies. Finally, we look to future opportunities for machine learning to enable the discovery of unknown protein functions and uncover the relationship between protein sequence and function.
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What OpenQuestion holds
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.