Learning deep representations of enzyme thermal adaptation.

Autor: Li G; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden., Buric F; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden., Zrimec J; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.; Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia., Viknander S; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden., Nielsen J; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.; BioInnovation Institute, Copenhagen N, Denmark., Zelezniak A; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.; Life Sciences Centre, Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.; Randall Centre for Cell & Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, SE1 1UL, London, UK., Engqvist MKM; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.; Enginzyme AB, Stockholm, Sweden.
Jazyk: angličtina
Zdroj: Protein science : a publication of the Protein Society [Protein Sci] 2022 Dec; Vol. 31 (12), pp. e4480.
DOI: 10.1002/pro.4480
Abstrakt: 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.
(© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)
Databáze: MEDLINE
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