ArticleJournal of experimental botany2023
Predicting plant Rubisco kinetics from RbcL sequence data using machine learning.
Article in Journal of experimental botany, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine learning-assisted directed evolution of plant Rubisco.Science advances · 2026Article
- Review
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Study on the effects of media-regulated ultrasonic pretreatment combined with elwectrohydrodynamic drying on the drying kinetics, active components, proteomic characteristics and volatile components of garlic.Food chemistry: X · 2026Article
- Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.Communications medicine · 2026Article
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- Rubisco is slow across the tree of life.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Shining light on photosynthesis.Journal of experimental botany · 2025Article
- A SynBio explosion: a whole new world for Rubisco engineering.Journal of experimental botany · 2025Article
- Understanding carboxysomes to enhance carbon fixation in crops.Biochemical Society transactions · 2025Review
- Genetic engineering of RuBisCO by multiplex CRISPR editing small subunits in rice.Plant biotechnology journal · 2025Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- Engineering Rubisco to enhance COSynthetic and systems biotechnology · 2024Review
- UniKP: a unified framework for the prediction of enzyme kinetic parameters.Nature communications · 2023Article
- Rubisco and its regulation-major advances to improve carbon assimilation and productivity.Journal of experimental botany · 2023Article
- Article
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3 authors.
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Abstract
Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) is responsible for the conversion of atmospheric CO2 to organic carbon during photosynthesis, and often acts as a rate limiting step in the later process. Screening the natural diversity of Rubisco kinetics is the main strategy used to find better Rubisco enzymes for crop engineering efforts. Here, we demonstrate the use of Gaussian processes (GPs), a family of Bayesian models, coupled with protein encoding schemes, for predicting Rubisco kinetics from Rubisco large subunit (RbcL) sequence data. GPs trained on published experimentally obtained Rubisco kinetic datasets were applied to over 9000 sequences encoding RbcL to predict Rubisco kinetic parameters. Notably, our predicted kinetic values were in agreement with known trends, e.g. higher carboxylation turnover rates (Kcat) for Rubisco enzymes from C4 or crassulacean acid metabolism (CAM) species, compared with those found in C3 species. This is the first study demonstrating machine learning approaches as a tool for screening and predicting Rubisco kinetics, which could be applied to other enzymes.
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