Design allosteric switches and ligand-binding domains from first principles using AI. No natural template required. Create custom biosensors for detection of small molecules, peptides, and proteins.
Our AI-driven biosensor design platform enables the creation of allosteric protein switches from first principles—no natural template required. By leveraging machine learning-designed ligand binding domains and conformational entropy modulation, we engineer highly sensitive and specific biosensors for diverse applications.
Generate entirely synthetic protein switches without relying on natural allosteric proteins. Machine learning models design novel conformational transitions responsive to target molecules.
Design binding domains targeting specific molecules—from small metabolites to large proteins. Sub-nanomolar affinity achieved through ML-guided optimization.
Engineer the thermodynamic coupling between ligand binding and functional output. Precise control over dynamic range and sensitivity.
Combine multiple binding domains for logic gate integration (YES, AND, OR gates). Create sophisticated detection schemes for complex analytical needs.
State-of-the-art machine learning and protein design algorithms integrated into a seamless workflow
ML-designed binding domains that target specific molecules—from small metabolites to large proteins. No natural template required.
Inverse the flow of information through designed protein switches. Ligand binding triggers functional output through thermodynamic coupling.
Enginoma Structure validation ensures designed proteins fold correctly. ML models predict and optimize dynamic range and sensitivity.
Multiple design strategies for different detection needs and output formats
Single-component allosteric switches
Insert circularly permuted binding domains into reporter proteins (beta-lactamase, dehydrogenases, luciferases) to create single-component sensors with dose-dependent response.
Modular two-component sensors
Design lock proteins that associate with key binders upon target binding. Reconstitute split reporters (luciferase, fluorescent proteins) for signal generation.
Real-time imaging and detection
Engineer GCaMP-like calcium indicators, neurotransmitter sensors, and fluorescent biosensors with optimized kinetics and dynamic range using ML-guided mutagenesis.
Whole-cell biosensing systems
Design allosteric transcription factors for metabolite detection and dynamic pathway regulation. ML-guided DBTL optimization for context-dependent performance.
AI-designed biosensors for diagnostics, research, and industrial monitoring
Cardiac biomarkers (Troponin I), hormone detection (Cortisol, 17-OHP), cancer biomarkers
Neurotransmitter sensors (Dopamine, Serotonin), calcium indicators, neural activity monitoring
High-throughput screening, compound detection, ADME-TOX assessment
Toxin detection, pathogen sensing, pollutant monitoring
Metabolite tracking, fermentation monitoring, quality control
Dynamic pathway regulation, logic gates, cell-based biosensors
Protein-protein interaction detection, real-time signaling monitoring
Wearable sensors, paper-based assays, portable diagnostics
Define target molecule, desired detection range, and output format requirements
Generate binding domains and allosteric switch designs using ML models
Validate structures and predict binding affinity and dynamic range
Clone, express, and characterize sensor performance
Our biosensor design platform is built on peer-reviewed research and validated methodologies
Watson, J.L. et al. De novo design of protein structure and function with Enginoma Backbone. Nature 620, 1089-1100 (2023). https://doi.org/10.1038/s41586-023-06415-8
Chen, J., Vishweshwaraiah, Y.L. & Dokholyan, N.V. Design and engineering of allosteric communications in proteins. Current Opinion in Structural Biology 72, 102334 (2022). https://doi.org/10.1016/j.sbi.2022.102334
Yin, R., Feng, B.Y., Varshney, A. & Pierce, B.G. Benchmarking Enginoma Structure for protein complex modeling reveals accuracy determinants. Protein Science 31, e4379 (2022). https://doi.org/10.1002/pro.4379
Wait, S.J., Rappleye, M., Lee, J.D., Goy, M.E., Smith, N. & Berndt, A. Machine learning-guided engineering of genetically encoded fluorescent calcium indicators. Nature Computational Science 4, 224-236 (2024). https://doi.org/10.1038/s43588-024-00611-w
The Enginoma platform supports detection of small molecules (steroids, metabolites, drugs), peptides (hormones, cytokines), and proteins (antibodies, receptors, toxins). We have successfully designed sensors for cortisol, 17alpha-hydroxyprogesterone, Bcl-2, Her2, Botulinum neurotoxin, cardiac Troponin I, and SARS-CoV-2 RBD.
Detection limits vary by target and application requirements. Our integrated design and validation workflow enables optimization for specific sensitivity and dynamic range needs.
Yes. Genetically encodable sensors (GCaMP-like indicators, luciferase-based switches) can be expressed in cells for real-time monitoring. Whole-cell biosensors based on transcription factors are also available for cell-based detection and screening applications.
The project timeline varies based on complexity, target molecules, and required validation scope. Contact us for a detailed project plan tailored to your specific needs.
Yes. Our integrated platform includes full wet lab validation services: protein expression, purification, functional characterization, binding assays (ITC, SPR), and performance optimization.
Transform your detection needs into reality with AI-designed allosteric switches. Contact us to discuss your target molecule and detection requirements.
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