ArticleProtein science : a publication of the Protein Society2022
Learning deep representations of enzyme thermal adaptation.
Article in Protein science : a publication of the Protein Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Genomic prospecting and biochemical characterization of a novel thermostable 3-quinuclidinone reductase from hot spring metagenomes for efficient biocatalysis.Applied and environmental microbiology · 2026Article
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Molecular Adaptations of Thermophilic Proteins-From Mechanistic Understanding to Machine Learning Approaches.ChemistryOpen · 2026Review
- A hot origin of dissimilatory sulfite reduction catalyzed by DsrAB in the Paleoarchean Era.mLife · 2026Article
- Conformational Dynamics and Catalytic Backups in a Hyper-thermostable Engineered Archaeal Protein Tyrosine Phosphatase.JACS Au · 2026Article
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- Improved prediction of microbial optimal growth temperatures with neural networks and protein language models.Frontiers in genetics · 2026Article
- MTD: A cloud-based omics database and interactive platform forSynthetic and systems biotechnology · 2025Article
- Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases.Nature communications · 2025Article
- Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana.The New phytologist · 2025Article
- ProCeSa: Contrast-Enhanced Structure-Aware Network for Thermostability Prediction with Protein Language Models.Journal of chemical information and modeling · 2025Article
- Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature.Briefings in bioinformatics · 2025Article
- LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design.Database : the journal of biological databases and curation · 2025Article
- Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost.Protein science : a publication of the Protein Society · 2025Article
- A general temperature-guided language model to design proteins of enhanced stability and activity.Science advances · 2024Article
- REME: an integrated platform for reaction enzyme mining and evaluation.Nucleic acids research · 2024Article
- Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline.Scientific reports · 2024Article
- Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.International journal of molecular sciences · 2024Article
- High-Temperature Tolerance Protein Engineering through Deep Evolution.Biodesign research · 2024Article
- HybridGCN for protein solubility prediction with adaptive weighting of multiple features.Journal of cheminformatics · 2023Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
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