AI-Driven Deep Mutational Scanning for Protein Engineering and Directed Evolution
AI-Driven Deep Mutational Scanning (DMS) for Protein Engineering
Deep Mutational Scanning (DMS) enables systematic evaluation of the functional effects of thousands to millions of protein variants. Combined with artificial intelligence and machine learning, DMS supports efficient identification of beneficial mutations while reducing experimental screening requirements.
Neoncorte Bio combines computational protein engineering with AI-guided Deep Mutational Scanning to prioritize high-value variants for experimental validation and accelerate protein optimization.
What is Deep Mutational Scanning?
Deep Mutational Scanning is a protein engineering methodology that measures or predicts how amino acid substitutions influence protein properties.
Traditional experimental DMS generates large datasets through high-throughput mutagenesis and functional screening.
Computational and AI-assisted DMS extends this approach by predicting mutation effects in silico, enabling researchers to focus laboratory resources on the most promising variants.
Traditional experimental DMS generates large datasets through high-throughput mutagenesis and functional screening.
Computational and AI-assisted DMS extends this approach by predicting mutation effects in silico, enabling researchers to focus laboratory resources on the most promising variants.
Why Use AI-Driven Deep Mutational Scanning?
Protein engineering projects often involve an enormous sequence space.
For a protein containing 300 amino acids:
- Single mutations exceed 5,700 possible variants.
- Double mutations increase into the millions.
- Higher-order combinations rapidly become experimentally impractical.
Application Areas
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Enzyme Engineering
Optimize industrial enzymes for catalytic activity, stability, and manufacturability.
Benefit: More efficient identification of promising enzyme variants. -
Antibody Engineering
Analyze mutation effects across antibody variable regions.
Benefit: Support affinity maturation and developability optimization. -
Protein Therapeutics
Evaluate mutation effects on therapeutic proteins.
Benefit: Improved stability and manufacturability during development. -
Synthetic Biology
Engineer proteins for metabolic pathway optimization.
Benefit: Accelerated optimization of engineered biological systems. -
Directed Evolution
Focus experimental libraries on the highest-priority variants.
Benefit: More efficient screening campaigns.
AI-Guided Deep Mutational Scanning Workflow
Neoncorte Bio combines multiple computational approaches to analyze protein sequence space.
Our workflow may include:
- Protein sequence analysis
- Structural modeling
- Protein language models
- Machine learning
- Computational mutagenesis
- Single mutation prediction
- Combinatorial mutation analysis
- Epistasis prediction
- Higher-order mutation prediction
- Protein fitness landscape prediction
- Multi-objective optimization
- Active-learning-guided variant selection
- Design-Build-Test-Learn (DBTL) methodologies
Engineering Objectives
Deep Mutational Scanning can support optimization of:
- Catalytic activity
- Catalytic efficiency
- Enantioselectivity
- Binding affinity
- Thermal stability
- Chemical stability
- Solvent tolerance
- Expression yield
- Protein solubility
- Manufacturability
- Developability
- Multi-property performance
Benefits of AI-Guided DMS
Compared with conventional directed evolution workflows, AI-guided DMS may help:
- Prioritize experimentally valuable variants
- Reduce unnecessary laboratory screening
- Explore larger mutation spaces
- Identify beneficial mutation combinations
- Improve Design-Build-Test-Learn efficiency
- Accelerate protein optimization projects
- Support data-driven decision making
Design-Build-Test-Learn (DBTL) Integration
Deep Mutational Scanning is particularly powerful when integrated into iterative DBTL workflows.
Neoncorte Bio supports:
- Protein sequence analysis
- AI-guided mutation prediction
- Variant prioritization
- Experimental validation
- Machine learning model refinement
- Successive Design-Build-Test-Learn cycles
What Neoncorte Bio Delivers
- AI-guided Deep Mutational Scanning
- Computational mutagenesis
- Virtual mutational scanning
- Protein fitness landscape prediction
- Epistasis prediction
- Higher-order mutation prediction
- Active-learning-guided variant prioritization
- Multi-parameter protein optimization
- Design-Build-Test-Learn (DBTL) workflows
- Confidential computational protein engineering partnerships
Who We Work With
- Biotechnology companies
- Pharmaceutical companies
- Protein engineering teams
- Industrial enzyme manufacturers
- Synthetic biology companies
- CROs and CDMOs
- Academic research institutions
- AI-driven drug discovery companies
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 Protein Engineering with AI-Guided DMS
Contact us to discuss your Deep Mutational Scanning 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