Protein engineering projects often require extensive wet‑lab cycles to assess stability, affinity, or functional activity, leading to high costs and long timelines. Existing computational tools are either limited to narrow tasks or lack integration with modern AI models, making it difficult for researchers to reliably predict the impact of sequence changes. Consequently, the development of biotherapeutics and industrial enzymes remains slow and resource‑intensive.
Funding
Funding not disclosed
Founders
Product
Problem
Protein engineering projects often require extensive wet‑lab cycles to assess stability, affinity, or functional activity, leading to high costs and long timelines. Existing computational tools are either limited to narrow tasks or lack integration with modern AI models, making it difficult for researchers to reliably predict the impact of sequence changes. Consequently, the development of biotherapeutics and industrial enzymes remains slow and resource‑intensive.
Solution
Cyrus Biotechnology offers a modular software suite that combines machine‑learning predictors with physics‑based simulations to evaluate protein stability, optimize antibody‑antigen affinity, and generate novel protein backbones. Users input amino‑acid sequences or structural templates, and the platform returns quantitative metrics such as ΔΔG, binding free‑energy estimates, and confidence scores for designed variants. The system leverages pretrained deep‑learning models trained on large structural databases and integrates with established tools like Rosetta and AlphaFold for high‑resolution modeling. Results are presented through an interactive web UI and can be accessed programmatically via RESTful APIs, enabling seamless incorporation into existing R&D pipelines. By automating iterative design cycles, the suite reduces the number of required experimental assays and shortens time‑to‑candidate for therapeutics and enzymes.
Target Audience
The primary customers are pharmaceutical and biotech R&D teams, industrial enzyme developers, and academic laboratories focused on protein engineering and antibody discovery. The platform is also suited for contract research organizations that need scalable computational design capabilities.
Features
- Deep‑learning stability predictor delivering ΔΔG estimates with < 1 kcal/mol RMSD across diverse protein families
- Antibody affinity optimizer that simulates somatic hypermutation pathways and ranks variants by predicted binding free energy
- De novo protein design engine using generative adversarial networks to create scaffolds meeting user‑defined functional constraints
- Integrated Rosetta/AlphaFold workflow for high‑accuracy structure refinement and validation of designed sequences
- Cloud‑native compute environment supporting parallel batch processing of thousands of variants per job
- REST API and Python SDK for automated workflow orchestration and data exchange with LIMS or ELN systems
- Interactive 3D visualization module with mutation mapping, energy heat‑maps, and downloadable PDB files
- Enterprise‑grade security with encrypted data storage, role‑based access control, and audit logging