Learning deep representations of enzyme thermal adaptation.
Protein Sci
; 31(12): e4480, 2022 12.
Article
em En
| MEDLINE
| ID: mdl-36261883
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Engenharia de Proteínas
/
Proteínas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Protein Sci
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Suécia