CatPred
A deep learning framework for predicting enzyme kinetic parameters (kcat, Km, Ki).
CatPred is a deep learning framework for predicting in vitro enzyme kinetic parameters, including turnover numbers (kcat), Michaelis constants (Km), and inhibition constants (Ki). It addresses key challenges such as the lack of standardized datasets, performance evaluation on enzyme sequences that are dissimilar to those used during training, and model uncertainty quantification.
Estimated resource cost: 0.000527
Categories: Protein Design
Key Capabilities
- Predicts turnover numbers (kcat), Michaelis constants (Km), and inhibition constants (Ki).
- Provides query-specific uncertainty estimates.
- Uses pretrained protein language models (PLM).
- Performs competitively with existing methods while offering reliable uncertainty quantification.
Runtime Statistics
| Metric | Value |
|---|---|
| runtime_mean | 9 |
| runtime_median | 9 |
| runtime_std | 12 |
| runtime_90th_percentile | 10 |
| runtime_max | 401 |
Similar Tools
- DLKcat Kcat Prediction
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Review your configuration, then confirm the estimated credit cost before you run the job. Note that credit estimates are not guaranteed and runtime can vary depending on inputs and settings.
Estimated Credits: 0.000527
Invite-only, limited-time access. Please contact ztang@getantibody.com.
Invite-only, limited-time access. Please contact ztang@getantibody.com.