We engineer enzymes with superior thermostability, pH tolerance, and solvent resistance using ML-guided prediction and rational design. Our platform delivers process-ready biocatalysts that perform under demanding industrial conditions.
Enzyme instability is the primary bottleneck in industrial biocatalysis. Our ML-guided approach predicts stabilizing mutations with unprecedented accuracy, enabling simultaneous improvement of multiple stability parameters.
Protein language models trained on thermostability data predict stabilizing mutations without experimental screening of thousands of variants.
Balance thermostability with catalytic activity. Our models optimize for stability without compromising enzyme function.
Engineered for real manufacturing conditions: high temperatures, organic solvents, extreme pH, and proteolytic environments.
Our ML-guided designs show consistent improvements in enzyme stability across diverse enzyme classes and applications.
Combining protein language models with physics-based design for superior stability predictions
Enginoma protein language models and custom thermostability-trained engines predict stabilizing mutations without needing experimental training data for your specific enzyme.
Structure-based and sequence-based models calculate folding free energy changes to rank mutations by predicted stabilization effect.
Account for epistatic interactions between mutations to predict synergistic and antagonistic effects in multi-site variants.
Increase melting temperature (Tm) by 10-20°C+ through intelligent design of stabilizing mutations. Engineering approaches include disulfide bonds, salt bridges, hydrophobic core packing, and proline substitutions.
Expand operational pH range from acidic (pH 3) to alkaline (pH 11) conditions. Engineering surface charge, pKa values, and ion binding sites for robust performance in diverse process environments.
Enable catalysis in up to 40%+ organic solvents for challenging substrate solubility. Engineering surface hydrophobicity and structural rigidity for non-aqueous process conditions.
Engineer resistance to proteolytic degradation and oxidative inactivation for extended operational lifetime. Surface charge engineering and strategic disulfide placement protect against degradation.
ML-guided workflow for rapid and accurate stability engineering
Define target stability parameters, baseline Tm, and operational conditions. Establish success criteria and screening assays.
Apply protein language models and ΔΔG predictors to rank stabilizing mutations. Generate focused library of 20-50 predicted stabilizers.
Express variants and measure Tm via DSF, thermal denaturation curves, and operational half-life under process conditions.
Combine top stabilizing mutations. Validate multi-site variants for preserved activity and maximum stability gain.
"Their stability engineering delivered a lipase variant stable in 40% DMSO, enabling a completely new synthetic route that reduced our process waste by 60%."
"We needed an enzyme for high-temperature laundry detergent. The engineered variant maintained 90% activity after 30 minutes at 70°C, far exceeding our targets."
"The ML-predicted mutations achieved a 15°C increase in Tm while actually improving kcat by 25%. The stability-activity trade-off was solved elegantly."
Stable enzymes for demanding process conditions across industries
High-temperature synthesis, organic solvent reactions, and multi-step biocatalytic cascades requiring stable enzymes.
Continuous flow reactors, high-temperature processes, and solvent-based reactions for bulk chemical production.
Thermostable enzymes for high-temperature washing, protease-stable formulas, and oxidative environments.
Lignocellulose processing requiring thermostable cellulases and hemicellulases for high-temperature saccharification.
Heat-stable phytases and proteases surviving feed pelleting at 80-90°C for enhanced nutritional availability.
Stable cellulases, proteases, and catalases for high-temperature textile processing and leather bating.
Our platform builds on peer-reviewed methods in ML-guided stability engineering
Zimmerman, L. et al. Context-dependent design of induced-fit enzymes using deep learning. PNAS 121, e2313809121 (2024). https://doi.org/10.1073/pnas.2313809121
CoSaNN strategy achieving >30% Tm improvements.Liu, J. et al. GeoEvoBuilder: A deep learning framework for functional and thermostable protein design. PNAS 122, e2504117122 (2025). https://doi.org/10.1073/pnas.2504117122
Zero-shot simultaneous stability and activity improvement.Chen, X. et al. Novel insights into enzymatic thermostability: The "Short Board" theory. Advanced Science (2024). https://doi.org/10.1002/advs.202402441
B domain engineering achieving ~12°C Tm improvement.Bian, J. et al. PRIME: Temperature-guided language model for protein engineering. mLife (2024). https://doi.org/10.1002/mlf2.12151
13-mutation variant with +10.19°C Tm and ~655x half-life.Sun, J. et al. Structure-based self-supervised learning for ultrafast protein stability prediction. The Innovation (2025). https://doi.org/10.1016/j.xinn.2024.100750
Pythia: 10^5-fold speedup in ΔΔG prediction.Our ML-guided designs achieve significant thermostability improvements. The actual gain depends on the starting enzyme and the target conditions. We use multi-objective optimization to balance stability with catalytic activity.
Our ML models balance stability and activity simultaneously. We often achieve both improved stability AND maintained or enhanced catalytic efficiency through intelligent multi-objective optimization.
We engineer thermostability, pH tolerance, organic solvent resistance, protease resistance, oxidative stability, and operational half-life under process conditions.
Yes. We deliver comprehensive stability data including Tm measurements via DSF, half-life curves under target conditions, pH activity profiles, solvent tolerance curves, and accelerated/real-time stability studies.
Whether you need higher operating temperatures, extreme pH tolerance, or solvent resistance, our ML-guided platform can deliver enzymes engineered for your process conditions.
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