Unlock the vast enzymatic potential of uncultured microorganisms through AI-powered metagenomic mining and sequence-to-function prediction. Our platform transforms environmental DNA into actionable enzyme candidates for your specific applications.
Less than 1% of environmental microorganisms can be cultured in laboratory conditions. Metagenomic sequencing reveals the hidden enzymatic diversity of the microbial world, enabling discovery of enzymes with unique properties unattainable through traditional screening methods.
Access enzymes from the 99% of microorganisms that cannot be cultured using traditional methods. Environmental DNA reveals millions of novel sequences.
Discover thermophiles, psychrophiles, acidophiles, and alkaliphiles with exceptional stability properties ideal for industrial biocatalysis.
Find enzymes with unique catalytic activities not present in sequenced genomes. Natural evolution has produced solutions for chemistry we haven't yet imagined.
Our deep learning models dramatically accelerate the discovery process, predicting function from sequence with unprecedented accuracy.
Our AI-driven platform transforms metagenomic data into actionable enzyme candidates for your specific applications.
AI-powered analysis of environmental DNA from diverse habitats to identify novel enzyme sequences with high confidence functional annotations.
Deep learning models predict enzyme function from sequence, surpassing traditional homology-based methods in accuracy and scope.
Specialized analysis of carbohydrate-active enzymes using protein language models for accurate classification and substrate prediction.
Sequence-to-function prediction models to identify high-value candidates with desired properties before experimental validation.
Enginoma Structure-guided analysis to understand enzyme mechanisms and optimize expression in heterologous hosts.
AI prediction of stability, solvent tolerance, and other industrial-relevant properties to prioritize candidates.
Enginoma combines advanced sequencing analysis with state-of-the-art deep learning to discover novel enzymes from the vast untapped genetic diversity of environmental microorganisms.
We analyze metagenomic datasets from diverse environments, identifying enzyme-encoding sequences through comprehensive bioinformatics pipelines.
Protein language models trained on millions of sequences predict function with accuracy far exceeding traditional homology-based approaches.
Gene synthesis, heterologous expression, and functional screening to confirm activity against your target substrates.
Comprehensive enzyme discovery across all major enzyme classes and industrial applications
Carbohydrate-active enzymes
Discover glycoside hydrolases, glycosyltransferases, carbohydrate esterases, and auxiliary enzymes for biomass conversion and food applications.
Redox enzymes
Discover laccases, peroxidases, monooxygenases, and oxidases for textile processing, bioremediation, and biosynthesis.
Decomposition enzymes
Discover lipases, proteases, esterases, and phosphatases for detergent, food, and pharmaceutical applications.
Group-moving enzymes
Discover transaminases, kinases, and glycosyltransferases for pharmaceutical synthesis and metabolite engineering.
A systematic approach from metagenomic data to validated enzyme candidates.
Curate metagenomic datasets from environmental samples or client-provided sources including soil, marine, and extreme environments.
Deep learning models identify novel enzymes, predict functional properties, and rank candidates by predicted industrial suitability.
Multi-parameter scoring prioritizes candidates based on predicted activity, stability, and expression feasibility.
Gene synthesis, expression, and functional screening to confirm activity against your target substrates.
Novel enzymes discovered through metagenomic mining enable diverse industrial applications.
Discover novel cellulases, xylanases, and other enzymes for efficient biomass conversion and biofuel production from lignocellulosic feedstocks.
Identify unique biocatalysts for drug synthesis, including enzymes from extreme environments with exceptional stability and novel activities.
Mine enzymes for food processing, flavor development, nutritional enhancement, and ingredient manufacturing applications.
Discover enzymes for biopesticides, soil remediation, animal feed additives, and crop protection solutions.
Identify enzymes for eco-friendly textile processing, including cellulases, proteases, and esterases for fabric modification.
Discover enzymes capable of degrading pollutants, including plastic-degrading enzymes, oil-degrading biocatalysts, and pesticide-detoxifying enzymes.
Our pipeline builds on peer-reviewed methods published in leading journals.
Thurimella, K. et al. Protein language models uncover carbohydrate-active enzyme function in metagenomics. BMC Bioinformatics 26, 285 (2025). https://doi.org/10.1186/s12859-025-06286-y
Deep learning for CAZyme discovery and classification from metagenomic sequences.Maranga, G. et al. Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method. mSystems 8, e01178-22 (2023). https://doi.org/10.1128/msystems.01178-22
Deep learning approaches for comprehensive functional annotation of metagenomic data.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 discovery and engineering.Berlec, A. et al. Novel enzymes from metagenomics. Current Opinion in Biotechnology 75, 102708 (2022). https://doi.org/10.1016/j.copbio.2022.102708
Strategies and challenges in discovering novel enzymes from environmental sources.Singh, R. et al. Protein function prediction using deep learning from metagenomics. Briefings in Bioinformatics 24, bbad265 (2023). https://doi.org/10.1093/bib/bbad265
Deep learning approaches for enzyme function prediction from metagenomic sequences.Common questions about our novel enzyme discovery services.
Metagenomic enzyme discovery involves mining DNA from environmental samples (soil, water, extreme environments) to identify novel enzymes from uncultured microorganisms that cannot be grown in laboratory conditions. This approach accesses the vast genetic diversity of the microbial world.
We can discover enzymes across all major classes including hydrolases, oxidoreductases, transferases, lyases, isomerases, and ligases, as well as specialized CAZymes like glycoside hydrolases and carbohydrate esterases. Our AI models can predict function across diverse enzyme families.
Discovered enzymes are validated through gene synthesis, heterologous expression in suitable hosts, and functional assays to confirm activity against your target substrates. We provide comprehensive characterization data including kinetic parameters.
We work with client-provided samples or curated databases. Common sources include agricultural soil, marine environments, freshwater systems, extreme habitats (hot springs, deep-sea vents, acidic mines), and industrial settings with unique microbial communities.
Yes. Our AI models predict sequence-to-function relationships, including catalytic efficiency, substrate specificity, pH optima, temperature stability, and solvent tolerance. This allows us to prioritize candidates before experimental validation.
Partner with our team to unlock the enzymatic potential of uncultured microorganisms for your specific applications.
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