Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning.
Molecules
; 28(24)2023 Dec 15.
Article
em En
| MEDLINE
| ID: mdl-38138586
ABSTRACT
The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein engineering.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Engenharia de Proteínas
/
Aminoácidos
Idioma:
En
Revista:
Molecules
Assunto da revista:
BIOLOGIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China