Cradle
Cradle, a leading platform in protein engineering, revolutionizes protein design using sophisticated machine learning. By streamlining the design process, it enables researchers to create proteins wit
What is Cradle?
Cradle is a protein engineering platform that uses machine learning to design proteins with specific properties. Rather than relying on traditional trial-and-error approaches, the tool generates predictions for genetic changes needed to achieve desired protein characteristics. Researchers input their requirements, and Cradle's models suggest optimised sequences and modifications. The platform is designed for biotech companies, research institutions, and laboratories working on protein-based solutions. It covers applications in therapeutics, industrial chemicals, biomaterials, and food production. Cradle allows you to optimise multiple protein properties in a single design cycle, reducing iteration time compared to conventional protein engineering methods.
Key Features
Machine learning-based protein sequence design
generates optimised genetic alterations based on specified protein properties
Multi-property optimisation
design for several protein characteristics simultaneously rather than sequentially
Data security and IP protection
built-in safeguards for sensitive research data and intellectual property
Integration with Ginkgo Bioworks
connects with the bioengineering platform for simplified workflows
Web-based interface
accessible directly through a browser without complex installation
Predictive modelling
forecasts how genetic changes will affect protein behaviour before laboratory validation
Pros & Cons
Advantages
- Reduces design cycles by predicting promising protein variants computationally
- Handles multiple design objectives at once, saving time on iterative refinement
- User-friendly interface suitable for researchers without machine learning expertise
- Integrates with established bioengineering workflows through Ginkgo Bioworks partnership
Limitations
- Predictions still require laboratory validation; computational designs may not always behave as predicted in practice
- Effectiveness depends on quality and relevance of training data for your specific protein class
- Limited details publicly available on pricing tiers and feature restrictions between free and paid versions
Use Cases
Pharmaceutical companies designing therapeutic proteins with improved efficacy or reduced immunogenicity
Industrial biotech optimising enzymes for chemical manufacturing processes
Research labs engineering proteins for biosensor or diagnostic applications
Food technology companies developing proteins for plant-based or fermentation-based food products
Materials science teams designing proteins for structural or functional biomaterials