Li, G.; Jia, L.; Wang, K.; Sun, T.; Huang, J. Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning. Molecules2023, 28, 8097.
Li, G.; Jia, L.; Wang, K.; Sun, T.; Huang, J. Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning. Molecules 2023, 28, 8097.
Li, G.; Jia, L.; Wang, K.; Sun, T.; Huang, J. Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning. Molecules2023, 28, 8097.
Li, G.; Jia, L.; Wang, K.; Sun, T.; Huang, J. Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning. Molecules 2023, 28, 8097.
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 present an improved innovative sequence–activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this improved approach, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability than that predicted using the existing data. We successfully improved the determination coefficient (R2) of the model from 0.66 to 0.92. In addition, root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were estimated and conformation analysis based on molecular dynamics simulations was performed to verify the enhanced thermal stability of the screened mutants. The improved innov'SAR algorithm enhanced the predictive accuracy, suggesting a method for modifying the stability of AT-ATA, which may help in directed evolutionary screening and open up new avenues for protein engineering.
Chemistry and Materials Science, Chemical Engineering
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