Enhancing Activity and Stability of Transaminase through Integrated Machine Learning, Rational Design, and Directed Evolution Approaches
Xiao-min Yi, Haoran Yu, Hongwei Yu, Lidan Ye · ACS Catalysis · 2025
Transaminases (ATAs) are promising biocatalysts for chiral amine synthesis but often suffer from limited activity and stability, particularly with non-natural substrates. This study integrates machine learning, rational design, and directed evolution to engineer Bacillus megaterium transaminase (BmATA) for synthesizing the Alzheimer’s drug precursor ( S )-1-(3-methoxyphenyl)ethylamine. By employing machine learning algorithms with appropriate feature selection, we identified key mutations that enhanced catalytic properties while maintaining the structural stability. Starting from the wild-type BmATA with merely 4% conversion from 20 mM 3-methoxyacetophenone (1a), the initial engineering efforts yielded a mutant M6X with conversion rates of 95 and 8% from 20 and 50 mM substrates, respectively. Further optimization through disulfide bond design and directed evolution led to the development of the M12X2 mutant with a melting temperature of 77.6 °C, achieving a remarkable conversion rate of 92% from 50 mM 1a. These findings not only underscore the potential of combining computational and experimental approaches in ATA engineering but also highlight the effectiveness of M12X2 as a robust biocatalyst for chiral amine synthesis, paving the way for its future applications in pharmaceutical development.