AI-Driven Industrial Enzyme Engineering

Commercial enzymes may need to operate under:

• High temperatures
• Extreme or variable pH
• High substrate concentrations
• High product concentrations
• Organic solvents
• High salt concentrations
• Oxidative conditions
• Low water activity
• Long reaction times
• Repeated process cycles
• Complex industrial feedstocks

At the same time, the enzyme needs sufficient catalytic activity, stability, selectivity, expression, and manufacturability.
AI-guided enzyme engineering allows these competing requirements to be optimized together rather than sequentially.

Catalytic Efficiency
Increase enzyme activity and catalytic productivity for improved process performance.
Thermostability
Engineer enzymes that maintain activity at elevated operating temperatures.
pH Stability
Optimize enzyme performance across the pH range required by the industrial process.
Solvent Tolerance
Improve enzyme stability and activity in organic solvents and other challenging reaction environments.
Substrate Specificity
Optimize enzymes toward desired industrial substrates while reducing unwanted activity.
Regioselectivity
Engineer enzymes to favor the desired reaction position.
Enantioselectivity
Improve stereochemical selectivity for asymmetric synthesis.
Cofactor Specificity
Switch or optimize cofactor preferences to improve process compatibility.
Protein Solubility
Improve soluble expression and enzyme concentration.
Expression Yield
Optimize sequences for higher recombinant production.
Aggregation Resistance
Reduce aggregation and improve enzyme stability during production and use.
Operational Stability
Extend enzyme lifetime under process conditions.
Space-Time Yield
Improve overall biocatalytic productivity and manufacturing output.

Food & Beverage
Engineer enzymes for food processing, ingredient production, and improved process efficiency.
Detergents
Develop enzymes with improved activity, stability, and performance under formulation and washing conditions.
Biofuels & Biomass Conversion
Optimize enzymes for degradation and conversion of lignocellulosic biomass and other feedstocks.
Pulp & Paper
Engineer enzymes for pulping, bleaching, fiber modification, and biomass processing.
Textiles
Develop enzymes for textile processing, finishing, and more sustainable manufacturing.
Animal Feed
Optimize enzymes for feed processing and performance under relevant biological conditions.
Biocatalysis
Engineer selective and efficient enzymes for chemical synthesis and industrial manufacturing.
Pharmaceutical & Fine Chemical Manufacturing
Develop highly selective biocatalysts for pharmaceutical intermediates and specialty chemicals.
Polymer Recycling
Engineer enzymes for degradation and recycling of polymers and plastics.
Waste Management
Optimize enzymes for degradation and transformation of difficult waste streams.
Water Treatment
Develop enzymes for biological treatment and degradation of target contaminants.

1. Define
Translate process requirements into measurable enzyme engineering objectives.
2. Analyze
Evaluate the starting enzyme sequence, available experimental data, and relevant structural information.
3. Predict
Use machine learning and protein language models to identify promising mutations and variants.
4. Prioritize
Select focused candidates using computational mutagenesis, fitness landscape prediction, and multi-objective optimization.
5. Build & Test
Experimentally produce and characterize selected enzyme variants.
6. Learn
Feed experimental results back into the model to improve subsequent predictions.
7. Iterate
Repeat the DBTL cycle with progressively better candidates.

The result is a more focused approach to directed evolution, where computational prediction helps determine which variants are most worth building and testing.

For example, an enzyme with higher catalytic activity may:

• Express poorly
• Aggregate
• Lose activity at process temperature
• Have insufficient solvent tolerance
• Have poor operational stability
• Produce unwanted products

Conversely, a highly stable enzyme may have insufficient catalytic activity.
Neoncorte Bio uses AI-driven multi-objective optimization to search for balanced enzyme variants rather than optimizing individual properties in isolation.
The objective is not simply to create the most active enzyme.
It is to engineer an enzyme that performs effectively under the conditions where the industrial process actually operates.

• 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 approach complements experimental enzyme engineering by helping teams answer a critical question:
Which enzyme variants should we build and test next?