Design and engineer high-affinity protein-protein interactions using our deep learning platform. From binding interface prediction to affinity maturation, we deliver production-ready protein complexes for therapeutics, diagnostics, and research applications.
Protein-protein interactions are fundamental to virtually all biological processes. Our AI-driven platform enables the design of novel interactions and optimization of existing ones with unprecedented precision.
Our deep learning models analyze protein surfaces to identify optimal binding interfaces, predict hot spot residues, and model interaction geometries with high accuracy.
Through structure-guided mutation design combined with directed evolution simulation, we achieve dramatic affinity improvements while maintaining selectivity and developability.
We validate designed interactions using surface plasmon resonance, isothermal titration calorimetry, and structural biology to ensure your complexes form as designed.
We combine state-of-the-art deep learning with rigorous experimental validation to deliver reliable protein interaction designs.
We start with available structures or generate accurate models using Enginoma Structure, enabling rational design of interface residues.
Our neural networks trained on thousands of PPI structures predict binding energies, interface hot spots, and optimal mutations.
We analyze co-evolving residues across protein families to identify functionally important interface positions and avoid disrupting native interactions.
Every design undergoes rigorous experimental validation including binding assays, thermal stability, and complex formation verification.
From de novo interaction design to affinity maturation of existing complexes, our platform covers the full spectrum of protein interaction engineering.
Create novel protein binding pairs
Design entirely new protein-protein interactions for synthetic biology circuits, biosensors, and therapeutic scaffolds. We engineer both binding partners simultaneously for optimal geometry and affinity.
Enhance binding strength 10-1000x
Improve binding affinity of existing interactions through hotspot identification, structure-guided mutagenesis, and combinatorial library screening. Ideal for antibody affinity maturation and receptor-ligand optimization.
Discriminate between targets
Engineer binding specificity to distinguish between closely related protein family members. Critical for therapeutic applications where off-target binding must be minimized.
Engineer complex networks
Design multi-protein complexes and signal transduction cascades with controlled stoichiometry and spatial organization. Applications include synthetic biology circuits and multi-specific therapeutics.
Enginoma integrates multiple deep learning architectures and structural biology tools for comprehensive protein interaction design.
We leverage Enginoma Complex for accurate prediction of protein complex structures, enabling structure-guided design of interaction interfaces.
Our specialized neural networks trained on PDB complexes predict binding interfaces, hot spot residues, and interaction energetics.
We apply physics-based molecular dynamics simulations and binding free energy calculations to validate and refine AI predictions.
Our integrated approach combines cutting-edge AI with deep expertise in protein biochemistry to deliver reliable protein interaction designs.
Enginoma interface prediction leverages Enginoma Complex and specialized neural networks to identify functional binding interfaces, significantly reducing experimental screening burden.
Our integrated computational and experimental workflow enables efficient PPI design from initial concept to validated constructs.
Our designs consider developability metrics including expression levels, thermal stability, aggregation propensity, and viscosity for therapeutic applications.
Every design is validated experimentally using SPR, ITC, and analytical ultracentrifugation to ensure complexes form with predicted stoichiometry and affinity.
See how our PPI design platform has delivered success for researchers and companies across different fields.
Designed a novel protein binder targeting a membrane receptor for cancer immunotherapy. Achieved sub-nanomolar affinity through two rounds of AI-guided affinity maturation.
Engineered protein-protein interactions to stabilize a multi-enzyme cascade for industrial biocatalysis. Improved operational stability at elevated temperatures for extended production cycles.
Created a de novo protein scaffold for a fluorescent biosensor. Designed both the anchoring interaction and the conformational switch mechanism for signal transduction.
Our systematic approach ensures reliable delivery of protein interaction designs.
We analyze target proteins, define binding requirements (affinity, specificity, geometry), and establish validation assays and success criteria.
Our AI models predict optimal binding interfaces, identify hot spot residues, and generate candidate designs ranked by predicted affinity.
Lead designs are converted to variant libraries for experimental validation. We express and screen variants using binding assays and complex formation tests.
Lead candidates undergo detailed binding characterization including SPR kinetics, ITC thermodynamics, and structural validation.
Our services support researchers and companies pushing the boundaries of protein science.
Our PPI design methods are grounded in peer-reviewed research from leading journals.
Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein-protein interactions using Enginoma Structure. Nature Communications 13, 1265 (2022). https://doi.org/10.1038/s41467-022-28865-w
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
Si, Y. & Yan, C. Protein complex structure prediction powered by multiple sequence alignments of interologs from multiple taxonomic ranks and Enginoma Structure. Briefings in Bioinformatics 23, bbac208 (2022). https://doi.org/10.1093/bib/bbac208
Common questions about our protein-protein interaction design services.
We design and engineer a wide range of PPIs including antibody-antigen complexes, cytokine-receptor interactions, enzyme-inhibitor pairs, protein scaffolds for synthetic biology, and multi-protein assemblies. Our platform handles both de novo interaction design and optimization of existing interfaces.
We use an ensemble of deep learning methods including Enginoma Complex for complex structure prediction, specialized interface prediction neural networks trained on PDB complexes, and molecular dynamics simulations for interface validation. Our pipeline integrates co-evolution analysis and deep mutational scanning data.
The project timeline varies based on complexity, target characteristics, and required deliverables. Contact us for a detailed project plan tailored to your specific needs.
Yes, all PPI design projects include experimental validation. We offer surface plasmon resonance (SPR) for binding kinetics, isothermal titration calorimetry (ITC) for thermodynamics, co-immunoprecipitation, analytical ultracentrifugation, and SEC-MALS for complex formation verification.
Absolutely. Our affinity maturation platform combines computational hotspot identification, structure-guided mutation design, and directed evolution simulation. We typically achieve 10-1000x affinity improvements while maintaining specificity and developability characteristics suitable for therapeutic applications.
Start your project today and leverage our AI-driven platform for precise, reliable protein-protein interaction design.
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