Precisely control substrate binding, stereoselectivity, and regioselectivity through AI-guided pocket engineering and binding site redesign. Our platform enables unprecedented control over enzyme selectivity for demanding biocatalytic applications.
Traditional enzyme engineering relies on random mutagenesis and extensive screening. Our AI-driven approach enables precise prediction of selectivity-determining residues, dramatically reducing development time while achieving superior selectivity profiles.
AI-guided mutation prediction reduces screening libraries from millions to hundreds. Our integrated approach identifies optimal variants faster than traditional directed evolution.
Engineer enzymes for non-natural substrates and synthetic chemistry applications that natural enzymes have not evolved to recognize.
Our multi-objective AI models balance selectivity improvements with catalytic activity, ensuring engineered variants maintain or exceed wild-type performance.
Engineered enzymes are validated under process-relevant conditions, not just ideal laboratory assays, ensuring real-world performance.
Our AI-driven platform enables precise control over enzyme specificity for targeted biocatalytic applications.
AI-guided engineering of binding pocket architecture to alter substrate acceptance and selectivity profiles through structure-based design.
Switch enzyme specificity to accept entirely non-natural substrates through targeted mutation design validated by molecular dynamics.
Achieve high enantioselectivity (ee) for asymmetric synthesis in pharmaceutical and fine chemical production through pocket chirality control.
Modify access tunnels and binding orientation to control regioselective transformations in multi-functional molecule synthesis.
Engineer substrate access channels to control which substrates can enter the active site and their binding orientation.
AI models predict activity-selectivity tradeoffs to maintain catalytic efficiency during specificity changes.
Enginoma combines structure prediction with deep learning to design precise modifications that alter enzyme specificity while maintaining catalytic activity.
We analyze enzyme 3D structures using Enginoma Structure predictions and experimental PDB data to identify selectivity-determining residues and binding pocket architecture.
Our protein language models trained on sequence-function datasets predict how mutations affect substrate binding, selectivity, and catalytic activity.
We express variants and characterize selectivity profiles against your substrate panel using LC-MS/MS analysis for comprehensive profiling.
Comprehensive enzyme specificity engineering across all major selectivity dimensions
Broaden or narrow substrate acceptance
Redesign binding pockets to accommodate new substrates while maintaining catalytic geometry. We engineer enzymes for pharmaceutical intermediates, specialty chemicals, and bio-based materials.
Achieve high enantiomeric excess
Engineer pocket chirality and hydrogen bonding networks to achieve high ee values for chiral synthesis. Suitable for ketoreductases, lipases, epoxide hydrolases, and transaminases.
Control site of modification
Modify access tunnels and binding orientation to control which functional groups are modified in multi-functional molecules. Critical for polysaccharide and complex natural product derivatization.
Control which functional group reacts
Engineer enzymes to selectively react with one functional group in the presence of others. Essential for multi-step synthesis and protecting group-free routes.
A systematic approach combining structural analysis with AI-driven design and experimental validation.
Enginoma Structure-guided analysis of enzyme structure, binding pocket architecture, and substrate recognition determinants.
Deep learning models identify selectivity-determining residues and predict how mutations affect substrate binding.
Structure-guided design of pocket modifications targeting desired specificity profiles with activity preservation.
Comprehensive screening against your substrate panel with full kinetic and selectivity characterization.
Our enzyme specificity engineering services support diverse biocatalytic transformations.
Engineer stereoselective enzymes for chiral drug synthesis, API production, and regioselective transformations that reduce synthetic steps.
Create regioselective biocatalysts for complex molecule synthesis, flavor compound production, and fragrance material manufacture.
Switch enzyme specificity for alternative substrate utilization in biorefinery applications, including polysaccharide hydrolysis.
Engineer specificity for detection of target analytes in diagnostic applications and point-of-care testing platforms.
Modify enzyme specificity for flavor compound production, nutritional enhancement, and food processing applications.
Engineer enzymes to act on non-natural pollutants for bioremediation of industrial waste and contaminated sites.
Our pipeline builds on peer-reviewed methods published in leading journals.
Zhao, H. et al. Enzyme specificity prediction using cross-attention graph neural networks. Nature (2025). https://doi.org/10.1038/s41586-025-09697-2
Advanced methods for enzyme specificity prediction using deep learning.Alzoubi, S. et al. AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications. Molecules 31, 45 (2026). https://doi.org/10.3390/molecules31010045
Comprehensive review of AI applications in enzyme engineering.Mazurenko, S. et al. Machine Learning in Enzyme Engineering. ACS Catalysis 10, 1210-1223 (2020). https://doi.org/10.1021/acscatal.9b04321
Foundational review of ML approaches for enzyme engineering.Devi, B.L.A.P. et al. Protein engineering of enzymes for selectivity and activity: An interlinked journey. Biotechnology Advances 70, 108293 (2024). https://doi.org/10.1016/j.biotechadv.2024.108293
Strategic approaches for enzyme selectivity engineering.Winkler, C.K. et al. Protein engineering of selectivity in lipases and esterases. Current Opinion in Chemical Biology 71, 102216 (2022). https://doi.org/10.1016/j.cbpa.2022.102216
Lipase and esterase selectivity engineering methods.Common questions about our enzyme specificity engineering services.
Yes. We have successfully switched enzyme specificity to accept entirely non-natural substrates across multiple enzyme classes including hydrolases, oxidoreductases, and transferases. Our AI models predict mutations that reshape binding pockets for new substrate recognition while maintaining catalytic geometry.
We routinely achieve high enantiomeric excess (ee) levels for ketoreductases, lipases, epoxide hydrolases, and transaminases. Enginoma combines pocket chirality analysis with directed evolution to optimize stereoselectivity beyond wild-type levels.
Regioselectivity engineering involves modifying access tunnels and binding orientation residues. We use molecular dynamics simulation to predict substrate positioning and validate results through LC-MS/MS product analysis.
Yes. Our AI models predict activity-selectivity tradeoffs using multi-objective optimization. We design mutations that achieve target selectivity while maintaining catalytic efficiency above threshold levels of wild-type enzyme.
Yes. We test engineered variants against your real substrate panel, not just model compounds. This ensures the engineered enzymes perform under conditions relevant to your actual manufacturing or research process.
Partner with our team to achieve precise control over substrate binding and selectivity for your biocatalytic applications.
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