ArticleProceedings of the National Academy of Sciences of the United States of America2023
Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
- Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model.ACS catalysis · 2026Article
- Rational protein engineering using an omni-directional multipoint mutagenesis generation pipeline.iScience · 2025Article
- Combing Directed Enzyme Evolution with Metabolic Engineering to Develop Efficient Microbial Cell Factories.Chem & bio engineering · 2025Review
- The Action of Plastic Degrading Enzyme Is Accelerated Mainly Due to an Increase in Thermal Stability Rather Than by an Inherent Catalytic Effect.Journal of the American Chemical Society · 2025Article
- Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SJournal of the American Chemical Society · 2025Article
- Bioluminescent Systems for Theranostic Applications.International journal of molecular sciences · 2024Review
- Exploring the Light-Emitting Agents inJournal of the American Chemical Society · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.National science review · 2023Review
- Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze
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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.