CatPred
A deep learning framework for predicting enzyme kinetic parameters (kcat, Km, Ki).
Overview
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.
Run CatPred on Neurosnap
The CatPred online webserver allows anybody with a Neurosnap account to run and access CatPred, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- 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.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access CatPred using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using CatPred in publications or research outputs.
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Boorla, V.S., Maranas, C.D. CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters. Nat Commun 16, 2072 (2025). https://doi.org/10.1038/s41467-025-57215-9 |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |