Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning.

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), hydr

SUBMITTER: Li G 

PROVIDER: S-EPMC10746113 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature