Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach.
Microb Cell Fact
; 23(1): 164, 2024 Jun 04.
Article
in En
| MEDLINE
| ID: mdl-38834993
ABSTRACT
BACKGROUND:
Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach.RESULTS:
According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3.CONCLUSIONS:
In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Enzyme Stability
/
Mutagenesis, Site-Directed
/
Deep Learning
Language:
En
Journal:
Microb Cell Fact
Journal subject:
BIOTECNOLOGIA
/
MICROBIOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Reino Unido