Computer Modeling Explains the Structural Reasons for the Difference in Reactivity of Amine Transaminases Regarding...

Area of Science:

• Biocatalysis
• Enzymology
• Organic Chemistry

Background:

• Amine transaminases (ATAs) are pyridoxal-5'-phosphate (PLP)-dependent enzymes crucial for catalyzing amino group transfer.
• Enzymatic reductive amination using ATAs has become a preferred industrial method over traditional chemical routes for producing chiral amines.
• Investigating substrate scope and limitations of ATAs is vital for expanding their industrial applications.

Purpose of the Study:

• To evaluate the impact of an α,β-unsaturated system in methylketone substrates on ATA activity and enantioselectivity.
• To compare the performance of wild-type ATAs with an engineered variant (ATA-256) using a model substrate.
• To gain mechanistic insights into substrate recognition and reactivity through computational docking.

Main Methods:

• Enzymatic assays using five wild-type ATAs and one engineered ATA variant (ATA-256).
• Substrate: 1-phenyl-3-butanone (saturated) and 4-phenylbut-3-en-2-one (α,β-unsaturated).
• Amino donor: isopropylamine (IPA).
• Analysis of conversion rates and enantiomeric excess (ee).
• Computational docking simulations of enzyme-substrate interactions.

Main Results:

• Wild-type ATAs exhibited high conversion (>80%) and enantioselectivity (>78% ee) for the saturated substrate 1-phenyl-3-butanone.
• The α,β-unsaturated substrate 4-phenylbut-3-en-2-one drastically reduced wild-type ATA conversion (<10%) but maintained enantioselectivity.
• The engineered ATA-256 demonstrated robust activity on the unsaturated substrate, achieving 87% conversion with >99% ee.
• Docking simulations revealed distinct substrate orientations and interactions within the active sites, explaining reactivity differences.

Conclusions:

• The presence of an α,β-unsaturation in methylketone substrates poses a significant challenge for wild-type ATA catalysis.
• Engineered ATA variants, such as ATA-256, can overcome limitations associated with substrate unsaturation, enabling efficient chiral amine synthesis.
• Computational modeling provides valuable insights into enzyme-substrate interactions, guiding the development of improved biocatalysts.