Generative AI for Protein Engineering
Designing the Future of Biology with AI
CONTROLOGIX has developed the first large-scale language model purpose-built for designing novel proteins and biological systems. Our 98-billion-parameter generative AI simultaneously reasons over protein sequence, structure, and function—enabling researchers to engineer proteins that would take natural evolution hundreds of millions of years to create.
A New Paradigm in Protein Engineering
Our flagship model represents a fundamental breakthrough in computational biology—a generative AI that understands proteins the way large language models understand human language, but with deep structural and functional awareness built into its architecture.
The CONTROLOGIX Foundation Model
Unlike traditional approaches that treat proteins as simple sequences of amino acids, our 98-billion-parameter model was architected from the ground up to jointly reason over three fundamental aspects of protein biology: the linear amino acid sequence, the three-dimensional folded structure, and the emergent biological function. This multimodal understanding enables the model to generate proteins that are not only structurally sound but also functionally specified.
The model was trained on a carefully curated dataset of over 10 billion natural protein sequences, encompassing the full diversity of life on Earth—from thermophilic archaea thriving in volcanic vents to psychrophilic bacteria surviving in Antarctic ice. This training corpus represents the accumulated wisdom of 3.8 billion years of natural evolution, compressed and distilled into a neural architecture capable of extrapolating far beyond what nature has explored.
Our proprietary training methodology incorporates structural supervision through predicted and experimentally determined protein structures, functional annotations from curated databases, and evolutionary conservation signals from multiple sequence alignments. The result is a model that doesn't just memorize patterns—it learns the underlying principles of protein biochemistry.
Multi-Scale Architecture
Hierarchical attention mechanisms that capture interactions from individual amino acids to entire protein domains, enabling coherent generation at any scale from small peptides to multi-domain complexes.
Real-Time Structure Prediction
Integrated structure prediction modules provide instant feedback on the three-dimensional conformation of generated sequences, allowing for rapid iteration and optimization.
Function-Conditioned Generation
Specify desired properties—binding affinity, enzymatic activity, thermal stability—and the model generates sequences optimized for those exact specifications.
Evolutionary Distance Metrics
Quantify exactly how far generated proteins are from any known natural sequence, with built-in novelty scoring that estimates the evolutionary time required for natural emergence.
Built for Scale, Designed for Precision
Every aspect of our model architecture was engineered to maximize both the breadth of protein space we can explore and the precision with which we can target specific functional properties.
Our foundation model contains 98 billion trainable parameters distributed across 128 transformer layers with a hidden dimension of 16,384. This scale enables the model to capture subtle patterns in protein biochemistry that smaller models miss entirely—the difference between a binding site that works and one that doesn't often comes down to a single residue interaction that only emerges at scale.
We assembled and curated the largest protein sequence dataset ever used for training a generative model. Over 10 billion unique sequences spanning all domains of life, from single-celled organisms to complex multicellular systems. Each sequence was quality-filtered, deduplicated at 50% sequence identity, and annotated with available structural and functional metadata.
An extended context window of 8,192 tokens allows the model to process complete protein sequences in a single pass, including large multi-domain proteins and protein complexes. This end-to-end processing is critical for capturing long-range interactions that determine tertiary structure and allosteric regulation.
Optimized inference infrastructure enables sub-100-millisecond generation of complete protein sequences. Our custom CUDA kernels and model parallelism strategies allow researchers to explore thousands of variants interactively, dramatically accelerating the design-build-test-learn cycle.
What You Can Build
Our platform provides researchers with unprecedented capabilities for protein engineering, from de novo design of entirely new proteins to precision optimization of existing therapeutic candidates.
De Novo Protein Design
Generate completely novel proteins that have never existed in nature. Specify desired fold topologies, binding interfaces, or catalytic sites, and the model will generate sequences predicted to achieve those properties. Our de novo designs have been experimentally validated to fold correctly at rates exceeding 70%—a dramatic improvement over previous computational methods.
- Novel fold topologies not found in nature
- Custom binding proteins for any target
- Designed enzymes for non-natural reactions
- Symmetric protein assemblies and cages
- Membrane proteins with specified topology
Sequence Optimization
Take any existing protein—whether a therapeutic antibody, an industrial enzyme, or a research tool—and systematically optimize it for desired properties. Our multi-objective optimization framework allows simultaneous improvement of binding affinity, stability, expression level, and immunogenicity while maintaining core function.
- Affinity maturation for antibodies and binders
- Thermostability enhancement
- Expression optimization for manufacturing
- Immunogenicity reduction
- Solubility improvement
- Half-life extension
Variant Library Design
Design intelligent protein variant libraries that maximize the probability of finding improved variants while minimizing experimental screening burden. Instead of random mutagenesis or exhaustive combinatorial libraries, our model generates focused libraries enriched for functional sequences.
- Machine learning-guided directed evolution
- Epistasis-aware library design
- Diversity optimization for screening
- Fitness landscape navigation
- Minimal library size for target coverage
Structure-Function Mapping
Understand the relationship between protein sequence, structure, and function at unprecedented resolution. Identify critical residues, predict the effects of mutations, and map the functional landscape around any protein of interest. These insights accelerate rational design and reduce experimental iteration.
- Critical residue identification
- Mutation effect prediction
- Functional annotation transfer
- Binding site prediction
- Allosteric site discovery
Multi-Protein Systems
Design proteins that work together as coordinated systems—from simple bimolecular interactions to complex multi-component assemblies. Specify interaction interfaces, stoichiometry, and cooperative binding behavior. Enable the creation of synthetic signaling pathways and metabolic circuits.
- Protein-protein interface design
- Heteromeric complex engineering
- Cooperative binding systems
- Synthetic signaling cascades
- Metabolic pathway optimization
Property Prediction
Before synthesizing a single molecule, predict key properties with high accuracy. Our model provides reliable estimates of expression level, solubility, stability, aggregation propensity, and more—allowing researchers to prioritize candidates and reduce wet-lab iteration.
- Folding stability prediction
- Expression level estimation
- Aggregation propensity scoring
- Post-translational modification prediction
- Cross-reactivity assessment
A Novel Fluorescent Protein 500 Million Years Beyond Nature
In our most significant demonstration to date, CONTROLOGIX generated a novel fluorescent protein so distant from any known natural sequence that computational phylogenetic analysis estimates it would require over 500 million years of natural evolution to emerge through random mutation and selection. Despite this unprecedented novelty, the protein folds correctly, exhibits bright green fluorescence, and demonstrates remarkable thermal stability—validating that our model has learned the fundamental principles of protein biochemistry rather than simply memorizing natural sequences.
This achievement represents more than an academic milestone. It demonstrates that the CONTROLOGIX platform can explore regions of protein sequence space that nature has never visited, opening up vast new territories for therapeutic and industrial applications. The proteins we design tomorrow may have no analogs in the natural world—purpose-built for human needs rather than constrained by evolutionary history.
"The ability to generate functional proteins that diverge so dramatically from natural sequences fundamentally changes what's possible in protein engineering. We're no longer limited to optimizing what evolution has already created—we can design from first principles."
Transforming Industries Through Protein Design
Our platform enables breakthrough applications across therapeutics, materials science, sustainable chemistry, and fundamental research. Wherever proteins are the solution, CONTROLOGIX accelerates discovery.
Therapeutic Development
Accelerate drug discovery with AI-designed protein therapeutics. Our platform enables rapid generation and optimization of antibodies, enzymes, and novel protein formats targeting previously undruggable diseases. Design bispecific antibodies with optimal binding geometry, engineer cytokines with reduced toxicity, or create entirely new therapeutic modalities that exploit unique protein architectures.
From target identification to lead optimization, CONTROLOGIX compresses timelines that traditionally span years into months. Our therapeutic designs have demonstrated improved efficacy, reduced immunogenicity, and enhanced manufacturability compared to conventionally developed candidates. Partner with us to bring the next generation of protein therapeutics to patients faster.
Advanced Materials
Proteins are nature's most sophisticated materials—stronger than steel, self-assembling, and fully biodegradable. Our platform unlocks the design of protein-based materials with properties impossible to achieve through traditional polymer chemistry. Create self-healing hydrogels, ultra-strong fibers, responsive smart materials, and precisely ordered nanostructures.
Design materials that respond to specific stimuli, self-organize into complex architectures, or interface seamlessly with biological systems. From biomedical implants to sustainable packaging, protein materials represent a trillion-dollar opportunity that CONTROLOGIX is uniquely positioned to capture.
Sustainable Chemistry
Replace energy-intensive chemical processes with enzyme-catalyzed reactions that operate at ambient temperature and pressure. Our platform designs enzymes for non-natural reactions—transformations that no existing enzyme can perform—enabling green chemistry solutions for industrial processes that currently rely on harsh conditions and toxic catalysts.
From carbon capture and utilization to plastic degradation, from sustainable fuel synthesis to green pharmaceutical manufacturing, designed enzymes offer a path to decarbonizing the chemical industry. CONTROLOGIX-designed biocatalysts have demonstrated activity levels exceeding their natural counterparts by orders of magnitude, making enzymatic processes economically viable at industrial scale.
Agricultural Biotechnology
Design proteins that enhance crop yields, confer pest resistance, and improve nutritional content without relying on traditional genetic modification approaches. Our platform enables precision engineering of plant proteins for improved function, as well as the creation of novel biopesticides and biofertilizers that reduce reliance on synthetic chemicals.
Address global food security challenges with proteins designed for specific agricultural applications—from nitrogen fixation enhancement to drought tolerance factors. CONTROLOGIX technology enables sustainable intensification of agriculture, producing more food with fewer inputs and reduced environmental impact.
Diagnostic Tools
Create highly specific protein-based biosensors and diagnostic reagents that detect diseases earlier and more accurately. Our platform designs binding proteins with exquisite selectivity for biomarkers of interest, enabling point-of-care diagnostics that rival laboratory-based assays in performance while dramatically reducing cost and complexity.
From rapid pathogen detection to continuous glucose monitoring, from early cancer screening to environmental contamination sensing, designed proteins are transforming how we detect and respond to health and environmental challenges. CONTROLOGIX-designed diagnostics achieve sensitivity and specificity levels that enable earlier intervention and improved outcomes.
Research Tools
Accelerate fundamental biological research with custom-designed protein tools. Our platform generates precisely specified reporters, actuators, and modulators that enable experiments previously impossible with natural proteins. From optogenetic tools to synthetic transcription factors, from split-protein systems to designed molecular machines.
Empower your lab to probe biological systems with unprecedented precision. CONTROLOGIX-designed research tools have enabled discoveries in cell signaling, gene regulation, protein dynamics, and countless other areas—publications featuring our designed proteins span the full breadth of the biological sciences.
How Our Model Understands Proteins
The CONTROLOGIX foundation model represents years of research into how to effectively apply modern AI techniques to the unique challenges of protein engineering. Here we provide a deeper look into the scientific principles underlying our technology.
Proteins are the workhorses of biology—molecular machines that catalyze reactions, transmit signals, provide structure, and perform countless other functions essential to life. A protein's function emerges from its three-dimensional structure, which in turn is determined by its amino acid sequence. This sequence-structure-function relationship is at the heart of protein engineering, and understanding it is key to designing proteins with desired properties.
Traditional approaches to protein engineering have relied on either rational design—using detailed knowledge of protein physics to manually engineer specific changes—or directed evolution, which mimics natural selection in the laboratory to iteratively improve proteins. Both approaches have significant limitations: rational design requires deep expertise and is limited by our incomplete understanding of protein physics, while directed evolution is time-consuming and can only explore a tiny fraction of possible sequence space.
The CONTROLOGIX approach combines the best aspects of both paradigms while transcending their limitations. By training a massive language model on the evolutionary record of proteins, we create a system that has learned the implicit rules of protein design from billions of examples—rules that emerge from physics and chemistry but that we don't need to explicitly program. This learned understanding enables rapid, accurate generation of novel proteins that satisfy complex functional specifications.
The Language of Proteins
Just as human languages follow grammatical rules that govern how words combine to form meaningful sentences, proteins follow "grammatical" rules—encoded in the physics of amino acid interactions—that govern how sequences fold into functional structures. Our model learns these rules not through explicit programming but through exposure to billions of evolutionary examples, developing an implicit understanding of protein grammar that enables it to generate novel, grammatically correct protein "sentences."
The 20 amino acids serve as the "alphabet" of this protein language, and meaningful protein sequences are far from random—they contain statistical signatures that reflect the physical constraints of folding and function. Our model captures these signatures at multiple scales, from local secondary structure preferences to global fold topologies, enabling it to generate sequences that are far more likely to fold and function than random sequences.
Evolutionary Information
The 3.8 billion years of evolution on Earth have generated an enormous diversity of proteins, each representing a successful solution to the problem of survival and reproduction. By training on this evolutionary record, our model learns which sequence patterns are compatible with folding and function—information that would be prohibitively expensive to obtain through experimental screening.
Importantly, evolution has not explored all possible sequences. The vast majority of sequence space remains uncharted, and our model can extrapolate into these unexplored regions by combining patterns learned from natural proteins in novel ways. This is how we generate proteins that are evolutionarily distant from any natural sequence while still satisfying the constraints of proper folding and function.
Multi-Scale Architecture
Proteins exhibit hierarchical organization: local secondary structures (helices, sheets) combine to form domains, which assemble into complete folded structures that may further oligomerize into complexes. Our model architecture mirrors this hierarchy, with different attention mechanisms capturing interactions at different scales—from the hydrogen bonds that stabilize individual helices to the hydrophobic cores that drive domain folding.
This multi-scale architecture enables coherent generation across all levels of protein organization. When generating a novel protein, the model simultaneously considers local sequence preferences, secondary structure propensities, tertiary packing constraints, and quaternary assembly requirements. The result is proteins that are consistent across all scales—a coherence that single-scale models fail to achieve.
Structure-Aware Training
While sequence data is abundant—billions of protein sequences are known—structural data is more limited, with only hundreds of thousands of experimentally determined structures. We augment experimental structures with state-of-the-art structure predictions to create a hybrid training signal that teaches the model the relationship between sequence and structure.
Structure-aware training enables the model to reason about spatial relationships that are not apparent from sequence alone. Residues that are distant in sequence may be adjacent in the folded structure, forming critical interactions for stability or function. By training on structure-sequence pairs, our model learns to generate sequences that will fold into structures compatible with specified functional requirements.
Function Conditioning
The ultimate goal of protein engineering is not just to generate sequences that fold, but to generate sequences with specific functions. Our model incorporates functional annotations during training, learning the relationships between sequence patterns and functional properties such as enzyme activity, binding specificity, and thermal stability.
At generation time, researchers can condition the model on desired functional properties, guiding it to produce sequences optimized for specific applications. This function-conditioned generation dramatically reduces the search space, enabling rapid identification of sequences likely to satisfy complex, multi-objective specifications without exhaustive experimental screening.
Validation Pipeline
AI-generated predictions are only valuable if they translate to experimental success. We have developed a comprehensive validation pipeline that subjects generated sequences to multiple computational checks before experimental synthesis, including structure prediction, molecular dynamics simulation, and comparison to known protein families.
This multi-stage validation dramatically improves experimental success rates. In blind tests, CONTROLOGIX-generated proteins fold correctly at rates exceeding 70%, compared to 10-20% for naive computational methods. This high success rate makes AI-guided protein design practical and economical, enabling the rapid iteration required for real-world applications.
From Concept to Validated Protein
Our platform streamlines the entire protein design process, from initial specification through computational design to experimental validation. Here's how researchers use CONTROLOGIX to create breakthrough proteins.
Define Your Objective
Begin by specifying the functional properties you need. Our platform accepts natural language descriptions of desired function, structural constraints from homologous proteins, quantitative specifications for binding affinity or enzymatic activity, and any combination of these inputs. The more precisely you can define your objective, the more focused the design process—but our model can also explore broadly when the goal is to discover unexpected solutions. Our scientific team works with you to translate your research goals into specifications that maximize the probability of experimental success.
Generate Candidate Sequences
Our foundation model generates thousands of candidate sequences conditioned on your specifications. Each sequence is accompanied by predicted properties including structure confidence scores, stability estimates, and functional annotations. The generation process explores diverse regions of sequence space, producing a portfolio of candidates that represents multiple potential solutions to your design challenge. You can interactively adjust generation parameters to explore trade-offs between novelty and conservation, specificity and cross-reactivity, or any other relevant dimensions.
Computational Validation
Candidate sequences pass through our multi-stage validation pipeline. Structure prediction confirms that sequences are likely to fold into the intended conformation. Molecular dynamics simulations assess stability under relevant conditions. Binding predictions verify that designed interfaces are complementary to intended targets. Sequences that fail any validation stage are filtered out or flagged for redesign. Only sequences that pass all computational checks advance to experimental testing, dramatically improving wet-lab success rates and reducing wasted experimental effort.
Prioritize & Rank
Validated candidates are ranked according to your priorities. Need the highest possible binding affinity? Best expression characteristics? Maximum distance from existing patents? Our multi-objective optimization framework identifies Pareto-optimal candidates that represent the best available trade-offs. You review ranked candidates through our interactive interface, examining predicted structures, sequence alignments, and property profiles to select the most promising designs for synthesis. Our team provides expert guidance on candidate selection and experimental strategy.
Experimental Testing
Selected candidates are synthesized and tested experimentally. We provide optimized DNA sequences for direct gene synthesis, along with expression protocols tailored to each design. Our integration with leading gene synthesis and protein production partners streamlines the path from computational design to physical protein. Experimental results feed back into the platform, enabling iterative optimization if initial candidates don't fully meet specifications. Most projects converge on successful designs within 2-3 rounds of design-test-learn iteration.
Optimize & Deploy
Successful initial hits undergo further optimization to maximize performance for your specific application. Our model generates variant libraries focused around promising sequences, enabling rapid affinity maturation, stability improvement, or expression optimization. Final optimized sequences are production-ready, with all necessary annotations for intellectual property protection and regulatory submissions. From initial concept to deployable protein, the entire CONTROLOGIX workflow can be completed in weeks rather than the years required by traditional approaches.
Pioneering the Future of Protein Engineering
Founded by AI Research Scientists, Driven by Impact
CONTROLOGIX was founded by a team of scientists who previously led AI research at major technology companies and academic institutions. United by a shared vision of applying cutting-edge machine learning to the most important challenges in biology, we came together to build the protein design platform we wished existed—one that combines the power of modern AI with deep expertise in protein science to enable breakthrough applications in therapeutics, materials, and sustainability.
We organized CONTROLOGIX as a public benefit corporation because we believe that transformative biotechnology should serve the broad public interest. Our corporate structure legally commits us to considering impact alongside profit, ensuring that our work advances human welfare even as we build a successful business. This commitment shapes everything we do, from the applications we prioritize to the partnerships we form.
Our team combines expertise in machine learning, computational biology, protein biochemistry, and drug development. We've published foundational papers in AI and biology, led teams at leading technology companies, and developed therapeutic candidates that have advanced to clinical trials. This blend of capabilities—rare in either pure-play AI companies or traditional biotech—positions CONTROLOGIX to translate AI breakthroughs into real-world impact.
Our Mission
To democratize protein engineering by making AI-powered design capabilities accessible to researchers everywhere. We believe that the next generation of therapeutics, materials, and sustainable solutions will be built on designed proteins—and we're committed to providing the tools that make this future possible.
Let's Build the Future Together
Whether you're exploring protein design for therapeutics, materials, or research applications, we'd love to discuss how CONTROLOGIX can accelerate your work. Reach out to start a conversation.
Contact Information
Our team is ready to discuss your protein engineering challenges and explore how our AI platform can help you achieve breakthrough results. We work with pharmaceutical companies, biotech startups, academic researchers, and industrial partners across the full spectrum of protein applications.
Complete the contact form to schedule a consultation, request a platform demonstration, or discuss potential collaboration opportunities. We typically respond within one business day.
Sanford, FL 32771