AI-Driven Platform for Protein Design, Optimization, and Computational Engineering
AI-Driven Protein Engineering Platform
Modern protein engineering requires navigating vast protein sequence spaces while balancing multiple performance objectives, including stability, activity, manufacturability, affinity, and expression.
Neoncorte Bio's Protein Engineering Platform combines artificial intelligence, machine learning, computational biology, and protein language models to help research teams prioritize promising protein variants and accelerate Design-Build-Test-Learn (DBTL) workflows.
The platform supports enzyme engineering, antibody optimization, therapeutic protein development, synthetic biology, and industrial biotechnology projects through AI-assisted computational analysis.
Why Use a Protein Engineering Platform?
Traditional protein engineering often relies on generating and screening large mutation libraries, which can be expensive, time-consuming, and experimentally demanding.
An AI-driven protein engineering platform helps researchers:
- Prioritize promising mutations
- Reduce unnecessary experimental screening
- Optimize multiple protein properties simultaneously
- Accelerate Design-Build-Test-Learn (DBTL) cycles
- Support data-driven engineering decisions
- Improve research productivity
Platform Capabilities
Neoncorte Bio's Protein Engineering Platform supports a broad range of computational workflows, including:
- Protein sequence analysis
- Structure-informed protein modeling
- Protein language models (PLMs)
- Zero-shot mutation prediction
- Computational mutagenesis
- Virtual Deep Mutational Scanning (DMS)
- Protein fitness landscape prediction
- Epistasis prediction
- Higher-order mutation prediction
- Active learning
- Bayesian optimization
- Smart library design
- Multi-objective optimization
- Design-Build-Test-Learn (DBTL) support
Protein Engineering Objectives
The platform helps prioritize variants for optimization of:
- Catalytic activity
- Catalytic efficiency
- Protein stability
- Thermostability
- Melting temperature (Tm)
- Binding affinity
- Protein developability
- Manufacturability
- Expression yield
- Aggregation resistance
- Protein solubility
- pH stability
- Solvent tolerance
- Oxidative stability
- Freeze-thaw stability
- Cofactor specificity
- Substrate specificity
- Enantioselectivity
- Multi-parameter optimization
Core Platform Modules
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AI-Guided Mutation PrioritizationIdentify mutations predicted to improve target protein properties before laboratory validation. -
Computational MutagenesisEstimate the potential effects of amino acid substitutions using AI-assisted computational models. -
Virtual Deep Mutational ScanningEvaluate extensive mutational landscapes computationally to identify high-priority variants. -
Protein Fitness Landscape AnalysisExplore sequence-function relationships and identify favorable evolutionary trajectories. -
Smart Library DesignCreate focused mutation libraries that maximize information while minimizing screening effort. -
Multi-Parameter OptimizationSimultaneously optimize activity, stability, manufacturability, expression, and other engineering objectives.
Industries We Support
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Enzyme Engineering
Optimize industrial enzymes for catalytic performance, stability, and manufacturing. -
Synthetic Biology
Optimize proteins for metabolic engineering and engineered biological systems. -
Therapeutic Proteins
Support engineering of biologics with improved stability and manufacturability. -
Diagnostics
Develop proteins with enhanced stability and analytical performance. -
Industrial Biotechnology
Engineer proteins for sustainable manufacturing, biocatalysis, and specialty chemicals.
Design-Build-Test-Learn (DBTL) Collaboration
The Protein Engineering Platform supports every phase of iterative protein optimization.
Design
Analyze protein sequences, structures, and engineering objectives.
Build
Prioritize mutations and focused libraries for experimental evaluation.
Test
Incorporate laboratory results into predictive models.
Learn
Continuously refine computational models using newly generated experimental data.
This iterative workflow helps improve engineering efficiency throughout the project lifecycle.
Analyze protein sequences, structures, and engineering objectives.
Build
Prioritize mutations and focused libraries for experimental evaluation.
Test
Incorporate laboratory results into predictive models.
Learn
Continuously refine computational models using newly generated experimental data.
This iterative workflow helps improve engineering efficiency throughout the project lifecycle.
What Neoncorte Bio Delivers
- AI-guided protein engineering
- Computational protein engineering
- Protein sequence analysis
- Protein language model integration
- Computational mutagenesis
- Virtual Deep Mutational Scanning
- Protein fitness landscape prediction
- Epistasis prediction
- Active learning
- Bayesian optimization
- Smart library design
- Multi-parameter optimization
- Flexible integration with existing R&D workflows
Who Uses the Platform?
The Protein Engineering Platform is designed for:
- Biotechnology companies
- Pharmaceutical companies
- Industrial enzyme manufacturers
- Antibody developers
- Synthetic biology organizations
- CROs and CDMOs
- Diagnostics developers
- Academic research institutions
Frequently Asked Questions (FAQs)
Our platform harnesses advanced artificial intelligence and machine learning to design, optimize, and engineer proteins tailored to your specific requirements. Whether you need improved enzyme efficiency, novel protein functions, or tailored biotherapeutics, our solutions streamline the protein design process and reduce time-to-market.
Our services are ideal for biotechnology and pharmaceutical companies, academic research labs, industrial manufacturers, and any organization involved in protein-based product development. Applications include enzyme optimization, biocatalyst development, therapeutic protein design, and more.
The process begins with your input data, such as protein sequences, structural information, and desired functional attributes. Our AI algorithms then generate a library of potential protein variants and use predictive modeling to evaluate their stability, activity, and specificity. The top candidates are ranked and provided for further experimental validation, thereby reducing the number of required lab iterations.
Our platform is versatile and can be applied to a wide range of proteins, including enzymes, structural proteins, and therapeutic proteins. We can optimize existing proteins or design novel ones for applications in catalysis, biopharmaceuticals, diagnostics, and more.
Absolutely. We work closely with you to tailor our AI models and algorithms to your target proteins and desired outcomes. This includes integrating proprietary data and fine-tuning parameters to ensure that our platform delivers the most relevant and high-performing protein candidates for your application.
Our platform offers robust API support and standard data export formats (e.g., CSV, JSON) to seamlessly integrate with your Laboratory Information Management Systems (LIMS) and other data analytics pipelines. We also provide comprehensive onboarding and training to ensure a smooth adoption process.
Our models are trained on extensive datasets of protein interactions, structural data, and experimental results. We continuously refine our algorithms with real-world feedback to improve prediction accuracy. While our computational predictions are highly reliable, we recommend subsequent in vitro or in vivo validation to confirm the performance of the engineered proteins.
Yes, we provide collaborative support throughout the validation process. Our team can help design proof-of-concept studies and coordinate with your wet lab partners to verify the performance of our predicted protein variants.
We offer flexible pricing models based on the scope and duration of your project. Options include subscription-based plans for ongoing R&D, as well as project-specific pricing for one-off protein engineering campaigns. Please contact us for a personalized consultation and detailed quote.
Our dedicated customer support team is available to assist you at every stage—from initial onboarding and technical training to troubleshooting and ongoing optimization. We offer support via email, phone, and live chat, along with detailed documentation and a comprehensive FAQ section.
Accelerate AI-Driven Protein Engineering
Contact Neoncorte Bio to learn how our Protein Engineering Platform can support your R&D projects
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