ReviewNature communications2024
Automated in vivo enzyme engineering accelerates biocatalyst optimization.
Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Automated synthetic cell-based screening for designed proteins with emergent functions.Nature communications · 2026Article
- High-throughput Optical Analysis to Inform Design of Electrochemical Biosensors.ACS measurement science au · 2026Review
- Multidomain DNA-Protein Mining Reveals Polymorphic Variations in RhlB Enhancing Monorhamnolipid Biosynthesis.ACS synthetic biology · 2026Article
- Advances in synthetic biology for engineering methylotrophic microbial cell factories.Journal of bacteriology · 2026Review
- Computational evolution of poly(U) polymerase for efficient and controlled RNA oligonucleotide synthesis.Nucleic acids research · 2026Article
- Precision hydrolysis: tailored yeast processing enzymes for yeast-based products.Applied microbiology and biotechnology · 2026Review
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Best Practices for Machine Learning-Assisted Protein Engineering.Journal of chemical information and modeling · 2025Review
- Systematic design and evaluation of artificial COSynthetic and systems biotechnology · 2025Article
- A super protein evolution engine.Nature chemical biology · 2025Article
- Directed evolution of hydrocarbon-producing enzymes.Biotechnology for biofuels and bioproducts · 2025Review
- Economic and sustainable revolution to facilitate one-carbon biomanufacturing.Nature communications · 2025Review
- Structural Homology Fails to Predict Secretion Efficiency inInternational journal of molecular sciences · 2025Article
- Analysis of Human Gut Microbiota Enzymes for Biotechnological and Food Industrial Applications.Foods (Basel, Switzerland) · 2025Article
- Growth-coupled continuous directed evolution by MutaT7 enables efficient and automated enzyme engineering.Applied and environmental microbiology · 2025Article
- Genetic Dissection of Cyclic di-GMP Signalling in Pseudomonas aeruginosa via Systematic Diguanylate Cyclase Disruption.Microbial biotechnology · 2025Article
- Computation-aided designs enable developing auxotrophic metabolic sensors for wide-range glyoxylate and glycolate detection.Nature communications · 2025Article
- Navigating the challenges of engineering composite specialized metabolite pathways in plants.The Plant journal : for cell and molecular biology · 2025Review
- Hierarchical metabolic engineering for rewiring cellular metabolism.FEMS microbiology reviews · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
Indexed as
Identifiers
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.