AI-Driven Optimization of Multiple Protein Properties Simultaneously
AI-Driven Multi-Objective Protein Optimization
Engineering high-performance proteins rarely involves optimizing a single property. Improvements in catalytic activity, binding affinity, or expression can sometimes reduce stability, manufacturability, or developability. Successfully navigating these trade-offs requires balancing multiple objectives simultaneously.
Neoncorte Bio combines artificial intelligence, protein language models, and computational protein engineering to optimize multiple protein properties within a unified AI-driven workflow.
Our computational platform helps researchers identify variants that achieve balanced improvements across multiple performance metrics while reducing experimental screening and accelerating Design-Build-Test-Learn (DBTL) cycles.
Why Multi-Objective Optimization Matters
Modern protein engineering involves competing objectives rather than single optimization targets.
Examples include:
- Higher activity without sacrificing stability
- Improved binding affinity while maintaining manufacturability
- Increased expression without increasing aggregation
- Better thermostability while preserving catalytic efficiency
- Improved developability without reducing biological function
- Enhanced protein solubility while maintaining structural integrity
- Instead of optimizing one characteristic at a time, AI enables simultaneous evaluation of multiple engineering objectives.
AI Technologies Behind Our Platform
Neoncorte Bio integrates advanced computational technologies including:
- Protein language models (PLMs)
- Artificial intelligence
- Machine learning
- Structure-informed protein modeling
- Computational mutagenesis
- Virtual Deep Mutational Scanning (DMS)
- Protein fitness landscape prediction
- Epistasis prediction
- Higher-order mutation prediction
- Active learning
- Bayesian optimization
- Pareto optimization
Multi-Objective Optimization Capabilities
Our AI platform supports simultaneous optimization of:
Protein Stability
Improve structural robustness under experimental and manufacturing conditions.
Catalytic Activity
Increase enzymatic performance while maintaining overall protein quality.
Binding Affinity
Optimize molecular recognition for antibodies, receptors, and therapeutic proteins.
Protein Solubility
Improve recombinant protein production and downstream processing.
Expression Yield
Increase recombinant protein production efficiency.
Manufacturability
Support scalable production and downstream processing.
Developability
Reduce downstream development risks through balanced molecular optimization.
Aggregation Resistance
Lower aggregation propensity while preserving biological activity.
Thermostability
Improve operational stability under elevated temperatures.
Sequence Liability Reduction
Reduce sequence features associated with degradation or chemical modification.
Improve structural robustness under experimental and manufacturing conditions.
Catalytic Activity
Increase enzymatic performance while maintaining overall protein quality.
Binding Affinity
Optimize molecular recognition for antibodies, receptors, and therapeutic proteins.
Protein Solubility
Improve recombinant protein production and downstream processing.
Expression Yield
Increase recombinant protein production efficiency.
Manufacturability
Support scalable production and downstream processing.
Developability
Reduce downstream development risks through balanced molecular optimization.
Aggregation Resistance
Lower aggregation propensity while preserving biological activity.
Thermostability
Improve operational stability under elevated temperatures.
Sequence Liability Reduction
Reduce sequence features associated with degradation or chemical modification.
Protein Properties Optimized Simultaneously
Our computational workflows support balancing combinations of:
- Catalytic activity
- Catalytic efficiency
- Protein stability
- Thermostability
- Melting temperature (Tm)
- Protein solubility
- Aggregation resistance
- Aggregation propensity
- Expression yield
- Manufacturability
- Developability
- Binding affinity
- Substrate specificity
- Cofactor specificity
- Enantioselectivity
- Regioselectivity
- pH stability
- Oxidative stability
- Freeze-thaw stability
- Solvent tolerance
- Sequence liability reduction
AI-Assisted Optimization Workflow
Our computational workflow combines multiple complementary approaches.
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Computational MutagenesisPredict the effects of mutations before laboratory experiments. -
Virtual Deep Mutational ScanningEvaluate millions of sequence variants computationally. -
Protein Fitness Landscape PredictionModel sequence-function relationships to identify promising evolutionary trajectories. -
Epistasis PredictionPredict interactions among multiple mutations. -
Higher-Order Mutation PredictionEvaluate combinations of mutations that may outperform single substitutions. -
Active LearningImprove prediction accuracy as new experimental data become available.
Applications
Our platform supports optimization of:
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Industrial Enzymes
Improve productivity while balancing stability, activity, and manufacturability. -
Therapeutic Proteins
Optimize efficacy alongside developability and formulation characteristics. -
Synthetic Biology
Engineer proteins supporting efficient metabolic pathways. -
Monoclonal Antibodies
Balance affinity, viscosity, aggregation resistance, and manufacturability. -
Diagnostic Proteins
Improve stability, sensitivity, and recombinant expression.
Design-Build-Test-Learn (DBTL) Collaboration
Our computational workflow integrates predictive modeling with experimental validation.
Design
Analyze sequences, structures, and engineering objectives.
Build
Prioritize mutations and focused variant libraries.
Test
Experimentally evaluate engineered proteins.
Learn
Update predictive models using newly generated experimental data.
Each DBTL cycle increases prediction accuracy while reducing unnecessary laboratory screening.
Analyze sequences, structures, and engineering objectives.
Build
Prioritize mutations and focused variant libraries.
Test
Experimentally evaluate engineered proteins.
Learn
Update predictive models using newly generated experimental data.
Each DBTL cycle increases prediction accuracy while reducing unnecessary laboratory screening.
Why Choose Neoncorte Bio?
Neoncorte Bio combines expertise in:
- Artificial intelligence
- Machine learning
- Computational protein engineering
- Protein language models
- Structural biology
- Computational mutagenesis
- Virtual Deep Mutational Scanning
- Protein fitness landscape prediction
- Epistasis prediction
- Higher-order mutation prediction
- Active learning
- Bayesian optimization
- Pareto optimization
- Multi-objective optimization
- Design-Build-Test-Learn (DBTL)
Who We Work With
Our platform supports:
- Biotechnology companies
- Pharmaceutical companies
- Antibody developers
- Industrial enzyme manufacturers
- Synthetic biology companies
- CROs and CDMOs
- Industrial biotechnology organizations
- 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.
Optimize Multiple Protein Properties with AI
Contact Neoncorte Bio to discuss your multi-objective protein optimization 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