Our machine learning-guided directed evolution platform accelerates enzyme optimization by predicting the most beneficial mutations before wet lab screening. Reduce library sizes by 100x while achieving superior enzyme variants in a fraction of the traditional timeline.
Traditional directed evolution requires screening millions of random variants across multiple iterative rounds. Our ML-guided approach learns from sequence-function relationships to predict beneficial mutations upfront, dramatically reducing experimental burden while achieving equal or superior results.
ML models predict top 0.1-1% of variants, reducing screening from millions to thousands. Lower library sizes mean faster turnaround and significantly reduced costs.
Complete projects efficiently. ML predictions eliminate many iterative rounds of random mutagenesis and screening.
Balance competing properties simultaneously. Our Pareto-optimal approaches find variants excelling in activity, stability, selectivity, and expression.
ML models generalize from training data to predict beneficial mutations in unexplored sequence space. Access improvements that random mutagenesis rarely finds.
We combine state-of-the-art protein language models with adaptive learning to predict and validate enzyme improvements.
Large-scale protein language models trained on billions of sequences learn evolutionary patterns and can predict how mutations affect function without requiring explicit structural information.
Our platform implements Bayesian optimization with acquisition functions tailored for protein engineering. Each round of experimental data updates the model for smarter next-round predictions.
Predicted variants are validated in our automated high-throughput screening platform. Deep mutational scanning and FACS-based selection complement traditional plate-based assays.
Our ML-guided directed evolution platform supports optimization across all major enzyme properties.
Improve kcat, lower Km, or optimize kcat/Km for your target substrate. We handle both steady-state kinetics and specific activity improvements under process conditions.
Increase melting temperature (Tm) by 10-25°C or extend half-life at elevated temperatures. Ideal for process enzymes requiring thermal tolerance.
Engineer stereoselectivity (ee%), regioselectivity, or substrate specificity. Transform moderate selectivity into >99% preference for your target.
Expand operational pH range or add tolerance to organic solvents, detergents, or harsh process conditions. Achieve performance where wild-type enzymes fail.
Improve soluble expression in bacterial, yeast, or mammalian systems. Increase biomass conversion or reduce purification costs through higher expression titers.
Balance multiple competing properties simultaneously using Pareto optimization. Find the best trade-offs between activity, stability, selectivity, and expression.
We combine computational ML predictions with rigorous experimental validation in an iterative optimization loop.
We begin by analyzing your starting enzyme sequence in the context of our training datasets. Homolog sequences, functional annotations, and structural predictions inform initial mutation strategy.
Enginoma protein language models are performance-calibrated on your enzyme's fitness landscape. Initial screening data trains adaptive models that improve with each experimental round.
Models predict activity scores for all possible single and combinatorial mutations. Variants are ranked by predicted improvement, and top candidates proceed to experimental validation.
Top-ranked variants are expressed and characterized. Kinetic parameters, stability metrics, and process-relevant assays validate ML predictions.
Experimental data feeds back into model training. Each round improves model accuracy and focuses exploration on the most promising regions of sequence space.
Lead variants undergo comprehensive characterization including detailed kinetics, long-term stability studies, and scale-up feasibility assessment before delivery.
From initial consultation to validated variants, our streamlined process delivers results efficiently.
Define target properties, establish screening methods, and set success criteria. Initial enzyme characterization data accelerates model training.
Train or performance-calibrate Enginoma models on available data. Predict and rank beneficial mutations across the target sequence space.
Express and screen top-ranked variants (typically 100-500 clones). Identify lead candidates for further optimization.
Train updated models with Round 1 data. Predict second-generation variants combining beneficial mutations from different parents.
Common questions about our ML-guided directed evolution services.
Traditional directed evolution requires screening millions of variants through iterative rounds of random mutagenesis. ML-guided evolution uses trained models to predict beneficial mutations, focusing screening on the top 0.1-1% of variants. This reduces library sizes from millions to thousands while achieving equal or better improvements.
Our platform optimizes catalytic activity, substrate specificity, stereoselectivity, thermostability, pH stability, solvent tolerance, and expression levels. Multi-objective optimization is supported for balancing competing properties.
Our workflow combines computational ML predictions with efficient wet lab validation. Contact us for a detailed project timeline based on your specific requirements.
Activity improvements of 5-50x are common. Thermostability improvements of 10-25°C in Tm are routinely achieved. Selectivity improvements often reach >99% enantiomeric excess from starting materials with modest selectivity.
Yes. Full variant sequences, predicted structures, and mutation annotations are provided. We also offer codon-optimized gene sequences for expression in your system of choice.
Our services support research and development at universities, pharmaceutical companies, and biotech startups worldwide.
Our ML-guided directed evolution platform is built on peer-reviewed research and methodologies.
Hie, B. et al. Efficient evolution of human antibodies from general protein language models. Nat. Biotechnol. 42, 275–283 (2024). https://doi.org/10.1038/s41587-023-01763-2
Yang, L. et al. STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning. ACS Omega 8, 44751–44756 (2023). https://doi.org/10.1021/acsomega.3c04832
Braun, M. et al. Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design. ACS Catal. 13, 14454–14469 (2023). https://doi.org/10.1021/acscatal.3c03417
Xie, W.J. et al. Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences. Proc. Natl. Acad. Sci. U.S.A. 120, e2312848120 (2023). https://doi.org/10.1073/pnas.2312848120
Marshall, L.R., Bhattacharya, S. & Korendovych, I.V. Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes. JACS Au 3, 2402–2412 (2023). https://doi.org/10.1021/jacsau.3c00315
Contact us today to discuss how ML-guided directed evolution can transform your protein engineering pipeline.
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