Learning deep representations of enzyme thermal adaptation.
Protein Sci
; 31(12): e4480, 2022 12.
Article
en En
| MEDLINE
| ID: mdl-36261883
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Ingeniería de Proteínas
/
Proteínas
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Protein Sci
Asunto de la revista:
BIOQUIMICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Suecia
Pais de publicación:
Estados Unidos