Structure-Based Design of Small Imine Reductase Panels for Target Substrates

Area of Science:

• Biocatalysis and enzyme engineering
• Computational chemistry and cheminformatics
• Pharmaceutical sciences

Background:

• Biocatalysis is crucial for pharmaceutical discovery, development, and manufacturing.
• Identifying suitable enzymes for specific chemical transformations often requires extensive screening.
• Existing methods for enzyme discovery are resource-intensive, demanding significant time and laboratory effort.

Purpose of the Study:

• To develop and validate a structure-based computational workflow, IREDFisher, for prioritizing enzyme sequences.
• To reduce the need for extensive laboratory-based screening in biocatalyst discovery.
• To accelerate the identification of imine reductases (IREDs) for pharmaceutical applications.

Main Methods:

• Developed a computational workflow (IREDFisher) to score protein sequences based on predicted activity on substrates.
• Utilized imine reductases (IREDs) as a model enzyme class to demonstrate the workflow's application.
• Validated the workflow using published data and computationally screened 1400 IRED sequences for reductive amination reactions.

Main Results:

• IREDFisher successfully retrieved the best enzymes and increased hit rates by identifying top-ranked sequences.
• The computational screening identified highly active IREDs, requiring only 20 in vitro tests.
• The workflow demonstrated high efficiency, ranking 85 sequences in 90 minutes and utilizing an established database of 591 IRED sequences.

Conclusions:

• IREDFisher significantly accelerates the discovery of imine reductases (IREDs) for synthesis and directed evolution.
• The computational approach minimizes time and resource expenditure in biocatalyst screening.
• The workflow is adaptable for other enzyme families by modifying its scoring function, paving the way for broader applications.