Cradle
ABOUT THE Cradle
Cradle empowers biologists to design improved proteins in record time using powerful predictive algorithms and AI-driven suggestions. Save significant time by visualizing, analyzing, and predicting the potential of candidate proteins. Leverage the results of each experimental round to train the Cradle AI, building a customized model to refine candidate proteins. Achieve impressive results in 96-well plates with Cradle for any project, even without high-throughput experimentation. Select what to improve and generate high-quality candidate proteins with a single click. Features such as predicting protein 3D structure, generating new sequences with improved thermostability, and codon optimization are coming soon. Private, secure, and entirely yours. Cradle protects the privacy and security of your sequences and data, while you retain full ownership of all intellectual property.
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What is Cradle Design Better Proteins?
Cradle is a protein engineering platform leveraging machine learning to design improved protein variants. It allows users to quickly generate optimized protein sequences for enhanced stability and activity, significantly accelerating the research and development process. The platform streamlines protein engineering by reducing guesswork and increasing efficiency.
Problem
- Traditional protein engineering methods are time-consuming and expensive.
- Predicting the effects of mutations on protein properties remains challenging.
Pain Points:
- Researchers spend significant time and resources on trial-and-error experimentation.
- Difficulty in identifying optimal protein variants leads to project delays and increased costs.
Solution
Cradle provides a user-friendly, machine-learning-powered platform for designing improved protein variants. Users input their target protein sequence and desired properties (e.g., increased stability or activity), and Cradle generates a set of optimized variants for experimental validation.
Value Proposition:
Accelerate protein engineering research and reduce costs by leveraging machine learning to design superior protein variants quickly and efficiently.
Problem Solving:
Reduces the time and resources required for protein optimization by automating the design process.
Improves the accuracy of variant prediction, reducing the need for extensive experimental screening.
Customers
Global users, aged 25-65+
Unique Features
- User-friendly interface accessible to researchers without extensive computational biology expertise.
- Integration with existing experimental data to refine predictions and improve accuracy.
- Proprietary machine learning algorithms for accurate and efficient variant prediction.
- Intuitive design workflow streamlining the protein engineering process.
- Scalable platform capable of handling large datasets and complex protein sequences.
- Robust algorithms ensuring the prediction of stable and functional protein variants.
User Comments
- Use Case: Designing a more stable enzyme for industrial applications.