Enhancing Activity and Stability of Transaminase through Integrated Machine Learning, Rational Design, and Directed Evolution Approaches

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.