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 (BmA...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Xiao-min Yi (21898269) (author)
مؤلفون آخرون:	Hao-ran Yu (21898272) (author), Hong-wei Yu (21898275) (author), Li-dan Ye (21898278) (author)
منشور في:	2025
الموضوعات:	Biophysics Biochemistry Pharmacology Biotechnology Evolutionary Biology Space Science Chemical Sciences not elsewhere classified Information Systems not elsewhere classified integrated machine learning identified key mutations enhanced catalytic properties drug precursor (< chiral amine synthesis appropriate feature selection 6 ° c disulfide bond design 50 mm substrates 50 mm 1a remarkable conversion rate directed evolution led 20 mm 3 directed evolution rational design natural substrates 1a ), robust biocatalyst promising biocatalysts often suffer mutant m6x merely 4 melting temperature limited activity future applications experimental approaches enhancing activity conversion rates combining computational ata engineering alzheimer ’ also highlight
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Enhancing Activity and Stability of Transaminase through Integrated Machine Learning, Rational Design, and Directed Evolution Approaches

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