AI-Driven Protein Engineering for Novel Biocatalytic Transformations
Engineering Enzymes for Non-Natural Reactions with Artificial Intelligence
Many valuable industrial chemical transformations cannot be efficiently catalyzed by naturally occurring enzymes. Advances in artificial intelligence, computational protein engineering, and machine learning are expanding the ability to redesign enzymes for non-natural reactions, enabling new opportunities in pharmaceutical manufacturing, industrial biotechnology, synthetic biology, and sustainable chemistry.
Neoncorte Bio develops AI-guided workflows that support the engineering of enzymes for novel catalytic activities, expanded substrate scope, and improved industrial performance while complementing experimental Design-Build-Test-Learn (DBTL) cycles.
Why Engineer Enzymes for Non-Natural Reactions?
Natural enzymes evolved to catalyze reactions that benefit living organisms—not necessarily the transformations required for industrial manufacturing.
Protein engineering makes it possible to redesign enzymes to:
- Accept non-natural substrates
- Catalyze new reaction types
- Improve catalytic efficiency
- Expand substrate compatibility
- Increase reaction selectivity
- Support sustainable manufacturing
Industrial Challenges
Organizations developing enzymes for non-natural reactions often seek improvements in:
- Novel catalytic activity
- Expanded substrate scope
- Catalytic efficiency
- Regioselectivity
- Enantioselectivity
- Chemoselectivity
- Cofactor specificity
- Thermostability
- Solvent tolerance
- Oxidative stability
- Expression yield
- Protein stability
- Manufacturability
AI-Guided Enzyme Engineering Workflow
Neoncorte Bio combines computational biology and machine learning to support rational enzyme engineering.
Our workflow may include:
- Protein sequence analysis
- Structure-informed enzyme modeling
- Active-site redesign
- Protein language models
- Computational mutagenesis
- Virtual Deep Mutational Scanning
- Protein fitness landscape prediction
- Epistasis prediction
- Higher-order mutation prediction
- Active learning
- Bayesian optimization
- Smart library design
- Multi-parameter optimization
- Design-Build-Test-Learn (DBTL) methodologies
Application Areas
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Pharmaceutical Manufacturing
Engineer enzymes capable of catalyzing synthetic transformations used in API and pharmaceutical intermediate production.
Benefit: Expand access to selective and sustainable biocatalytic manufacturing. -
Industrial Biocatalysis
Develop enzymes for reactions not efficiently performed by naturally occurring catalysts.
Benefit: Increase manufacturing flexibility while reducing dependence on traditional chemical catalysis. -
Synthetic Biology
Design enzymes that extend the catalytic capabilities of engineered metabolic pathways.
Benefit: Enable production of novel molecules and improve pathway performance. -
Specialty Chemicals
Optimize enzymes for selective synthesis involving synthetic feedstocks and non-natural intermediates.
Benefit: Improve product selectivity and process efficiency. -
Sustainable Chemistry
Support replacement of conventional chemical synthesis with selective enzymatic transformations where appropriate.
Benefit: Reduce process intensity and improve environmental performance.
Engineering Objectives
Depending on the application, enzymes may be engineered for:
- New catalytic functions
- Higher catalytic activity
- Improved catalytic efficiency
- Expanded substrate recognition
- Better stereoselectivity
- Improved regioselectivity
- Enhanced chemoselectivity
- Optimized cofactor utilization
- Greater thermostability
- Improved solvent tolerance
- Increased oxidative stability
- Higher recombinant expression
- Reduced aggregation
- Improved manufacturability
Design-Build-Test-Learn (DBTL) Integration
Engineering enzymes for non-natural reactions benefits from iterative computational prediction and laboratory validation.
Neoncorte Bio supports:
- Protein sequence and structural analysis
- AI-guided mutation prioritization
- Variant design
- Experimental characterization
- Machine learning model refinement
- Successive Design-Build-Test-Learn (DBTL) cycles
What Neoncorte Bio Delivers
- AI-guided enzyme engineering
- Computational enzyme design
- Active-site redesign
- Computational mutagenesis
- Virtual Deep Mutational Scanning
- Protein fitness landscape prediction
- Smart library design
- Multi-parameter optimization
- Design-Build-Test-Learn (DBTL) workflows
- Confidential B2B protein engineering partnerships
Who We Work With
- Pharmaceutical companies
- Industrial biotechnology companies
- Synthetic biology companies
- Industrial enzyme manufacturers
- Specialty chemical companies
- CROs and CDMOs
- Biotechnology startups
- Academic research organizations
Frequently Asked Questions (FAQs)
Our solutions leverage advanced artificial intelligence and machine learning to design, optimize, and engineer enzymes tailored to your specific applications. Whether you need enhanced catalytic efficiency, improved stability, or novel enzyme functionalities, our platform streamlines the design process, reduces experimental iterations, and accelerates time-to-market.
Our services are ideal for biotechnology and pharmaceutical companies, industrial manufacturers, academic research labs, and any organization involved in enzyme applications. Industries such as biofuels, food processing, personal care, paper and pulp, and environmental management can all benefit from our AI-driven enzyme engineering tools.
Our process begins with your input data—such as enzyme sequences, structural information, and target reaction profiles. Our proprietary AI algorithms generate a library of enzyme variants and use predictive modeling to evaluate their catalytic activity, stability, and specificity. The top-performing candidates are ranked and provided for further experimental validation, significantly reducing the number of lab iterations needed.
Our platform is versatile and can be applied to a wide range of enzymes including hydrolases, oxidoreductases, transferases, and lyases. We focus on both enhancing the performance of existing enzymes and designing entirely new biocatalysts for applications in industrial processes, therapeutics, and research.
Absolutely. We collaborate with you to tailor our AI models and parameters to meet your project’s unique requirements. This includes integrating your proprietary data, adjusting target properties, and aligning the enzyme design with your process conditions to deliver a highly customized solution.
Our software features robust API support and data export options (e.g., CSV, JSON) that enable seamless integration with Laboratory Information Management Systems (LIMS) and other data analytics pipelines. We also offer comprehensive onboarding, training, and technical support to ensure smooth adoption by your team.
Our models are trained on extensive datasets from scientific literature and experimental data. Continuous feedback from laboratory validations helps refine our predictions. Although our AI predictions are highly reliable, we recommend in vitro or in vivo validation to confirm the performance of the engineered enzymes under your specific conditions.
Yes, we provide collaborative support during the validation phase. Our team can assist in designing proof-of-concept studies, advising on experimental protocols, and interpreting validation results to ensure that the engineered enzymes meet your performance criteria.
Our pricing is flexible and depends on the scope, duration, and level of customization required for your project. We offer subscription-based models for ongoing research and project-based pricing for specific campaigns. Please contact us for a personalized consultation and detailed quote.
Our dedicated customer support team is available throughout your journey—from initial consultation and onboarding to ongoing technical support and regular software updates. We offer multiple support channels including email, phone, live chat, and detailed documentation to ensure your success.
Develop Enzymes for Novel Catalytic Functions
Contact us to discuss your enzyme engineering project
Neoncorte Bio
Where AI Meets Biotechnology
Neoncorte Bio is at the forefront of the convergence between artificial intelligence and enzyme engineering. Our team comprises experts in computational biology, bioinformatics, and machine learning, all driven by a mission to accelerate innovation in enzyme design. By leveraging our advanced AI models, we provide unparalleled solutions that enhance efficiency, reduce costs, and push the boundaries of what's possible in enzyme engineering
Proud Member of Leading Global AI Programs
Neoncorte Bio is part of the NVIDIA Inception and Nebius for Startups programs — two of the world’s leading ecosystems for high-performance AI innovation. These partnerships strengthen our ability to deliver next-generation AI-driven protein, enzyme, and aptamer engineering.
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As a member of NVIDIA Inception, Neoncorte Bio gains access to cutting-edge GPU technologies, expert guidance, and a global AI ecosystem that supports companies from prototype to production. The program empowers us to explore new AI opportunities and build high-performance biological design pipelines powered by NVIDIA’s world-class platform. -
Through Nebius for Startups, we gain access to high-performance compute infrastructure optimized for large-scale AI workloads, along with hands-on technical guidance and a strong community of innovative AI companies. Nebius enables us to train and deploy complex biological models more efficiently — accelerating enzyme, protein, and aptamer design while supporting rapid scaling of our R&D pipelines.
Publications
Scientific Publication of Neoncorte Bio Team
- Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5′-3′ exonuclease activity, effective deoxyuracil incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates.
- The transcriptomic data are being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are the data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details about the samples which we would like to control (gender, biological state, treatment, etc.) can be used as style components.
- List of all Neoncorte Bio publications dedicated to Molecular Biology, Biotechnology, Artificial Intelligence and Artificial Neural Networks, published mostly by Nikolay Russkikh, CEO of Neoncorte Bio
Our Expertise in Action
With extensive experience in AI applications and software engineering tailored to the life sciences, we specialize in solving complex challenges and delivering innovative solutions for our customers. Our work demonstrates a deep understanding of cutting-edge technologies and their application in the real world.
Here are examples of the types of projects we have successfully delivered:
Here are examples of the types of projects we have successfully delivered:
- Automated NGS Data Analysis:Designed a production-grade solution for the automated processing, annotation, and analysis of Next-Generation Sequencing (NGS) data.
- Single-Cell Data Integration:Built state-of-the-art tools for integrating multimodal single-cell data, achieving recognition for technical excellence.
- Metagenomic Classification Algorithms:Developed advanced methods for classifying sequencing reads in metagenomics research.
- High-Throughput Image Processing Pipelines:Engineered an efficient pipeline to process millions of sequencing images with exceptional accuracy.
- Cell Counting via AI:Created a computer vision solution for precise cell counting in microphotography images, streamlining data analysis.
Get in touch with our team
Phone: +1-503-754-3958
Email: contact@neoncorte.com
Email: contact@neoncorte.com
© All Rights Reserved. Neoncorte Bio. 2024-2026
contact@neoncorte.com
contact@neoncorte.com