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• W4306873876 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." @default.
• W4306873876 created "2022-10-20" @default.
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• W4306873876 date "2022-11-22" @default.
• W4306873876 modified "2023-09-29" @default.
• W4306873876 title "Learning deep representations of enzyme thermal adaptation" @default.
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• W4306873876 doi "https://doi.org/10.1002/pro.4480" @default.
• W4306873876 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36261883" @default.
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