ArticleNature biotechnology2025
Computational scoring and experimental evaluation of enzymes generated by neural networks.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 45 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
45 citing papers in PubMed, 64 citations in OpenAlex.
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- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
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- Sequence redesign of glycosyltransferases for enhanced heterologous expression and glycosylation efficiency in Escherichia coli.Nature communications · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Scalable and cost-efficient custom gene library assembly from oligopools.Science advances · 2026Article
- Deep learning-driven integrated pipeline for de novo design and synthesis of antimicrobial peptides.npj drug discovery · 2026Article
- Functional protein design and enhancement with ontology reinforcement iteration.Nature communications · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
- Design prokaryotic cis-regulatory elements using language model.Nucleic acids research · 2026Article
- Computational evolution of poly(U) polymerase for efficient and controlled RNA oligonucleotide synthesis.Nucleic acids research · 2026Article
- Sequence-based generative AI design of versatile tryptophan synthases.Nature communications · 2026Article
- Enhancing functional proteins through multimodal inverse folding with ABACUS-T.Nature communications · 2025Article
- A protein dynamics-based deep learning model enhances predictions of fitness and epistasis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Before LUCA: unearthing the chemical roots of metabolism.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Rational protein engineering using an omni-directional multipoint mutagenesis generation pipeline.iScience · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
Abstract
In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model. Focusing on two enzyme families, we expressed and purified over 500 natural and generated sequences with 70-90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity. Over three rounds of experiments, we developed a computational filter that improved the rate of experimental success by 50-150%. The proposed metrics and models will drive protein engineering research by serving as a benchmark for generative protein sequence models and helping to select active variants for experimental testing.
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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.