AI-Driven Industrial Biocatalysis

Industrial biocatalysts may need to tolerate:

• High substrate concentrations
• High product concentrations
• Elevated temperatures
• Non-neutral pH
• Organic solvents
• High salt concentrations
• Oxidative environments
• Low water activity
• Long reaction times
• High enzyme loading
• Complex feedstocks

At the same time, the enzyme must deliver the desired activity, selectivity, stability, productivity, and yield.
AI-driven protein engineering helps navigate these competing requirements and identify variants better suited to industrial biocatalysis.

Catalytic Efficiency
Increase catalytic performance and reaction productivity.
Substrate Specificity
Engineer enzymes toward desired substrates and reduce unwanted activity.
Enantioselectivity
Improve stereoselective synthesis of valuable chiral compounds.
Regioselectivity
Direct enzymatic reactions toward the desired position on a substrate.
Thermostability
Maintain enzyme activity at elevated process temperatures.
pH Stability
Improve performance under the pH conditions required by the reaction.
Solvent Tolerance
Engineer enzymes for compatibility with organic solvents and other challenging reaction environments.
Cofactor Specificity
Modify or optimize cofactor preferences for better process integration.
Operational Stability
Extend enzyme lifetime during prolonged industrial reactions.
Expression Yield
Increase recombinant production of the biocatalyst.
Protein Solubility
Improve soluble enzyme production and concentration.
Space-Time Yield
Increase volumetric productivity and overall process efficiency.

Pharmaceutical Manufacturing
Engineer highly selective enzymes for the synthesis of pharmaceutical intermediates and active pharmaceutical ingredients.
Fine Chemicals
Develop biocatalysts for selective and efficient synthesis of specialty chemicals.
Food & Beverage
Optimize enzymes used in food processing, ingredient production, and fermentation.
Bio-Based Chemicals
Engineer enzymes for conversion of renewable feedstocks into valuable chemicals and materials.
Biomass Conversion
Improve cellulases, xylanases, and other enzymes involved in lignocellulosic biomass processing.
Polymer Recycling
Develop enzymes capable of selectively degrading and recycling polymeric materials.
Detergents
Engineer enzymes for improved activity and stability under formulation and washing conditions.
Pulp & Paper
Optimize enzymes for pulp processing, bleaching, fiber modification, and biomass conversion.
Textiles
Develop biocatalysts for textile processing and more sustainable manufacturing.

1. Define the Reaction
Identify the substrate, desired product, operating conditions, and performance targets.
2. Analyze the Enzyme
Evaluate the starting sequence, available experimental data, and relevant structural information.
3. Predict
Use machine learning and protein language models to identify potentially beneficial mutations.
4. Prioritize
Select variants using computational mutagenesis, fitness landscape prediction, and multi-objective optimization.
5. Build & Test
Experimentally produce and characterize prioritized variants.
6. Learn
Use experimental measurements to refine the predictive model.
7. Iterate
Run successive Design-Build-Test-Learn cycles toward the desired industrial phenotype.
The result is a data-driven approach to biocatalyst optimization that can reduce unnecessary library construction and experimental screening.

AI-assisted biocatalyst engineering can help reduce this burden by:

• Prioritizing mutations computationally
• Exploring sequence space before synthesis
• Designing focused libraries
• Predicting mutation effects
• Identifying promising combinations
• Learning from small experimental datasets
• Selecting informative variants for subsequent rounds

More computational exploration. More focused experiments. Faster engineering cycles.

• Artificial intelligence
• Machine learning
• Protein language models
• Computational protein engineering
• Computational mutagenesis
• Virtual Deep Mutational Scanning
• Protein fitness landscape prediction
• Epistasis prediction
• Higher-order mutation prediction
• Active learning
• Bayesian optimization
• Multi-objective optimization
• Design-Build-Test-Learn workflows

Our role is to help biocatalysis teams make better decisions about which enzyme variants to build, test, and optimize next.