---
license: mit
tags:
- enzymes
- kinetics
- biochemistry 
- deep-learning
- protein
---

# CatPred-DB: Enzyme Kinetic Parameters Database

- **Paper:** [CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters](https://www.nature.com/articles/s41467-025-57215-9)
- **GitHub:** https://github.com/maranasgroup/CatPred-DB

### Dataset Description

CatPred-DB contains the benchmark datasets introduced alongside the CatPred deep learning framework for predicting in vitro enzyme kinetic parameters. The datasets cover three key kinetic parameters:

| Parameter | Description | Datapoints |
| --- | --- | --- |
| *k*cat | Turnover number | 23,197 |
| *K*m | Michaelis constant | 41,174 |
| *K*i | Inhibition constant | 11,929 |

These datasets were curated to address the lack of standardized, high-quality benchmarks for enzyme kinetics prediction, with particular attention to coverage of out-of-distribution enzyme sequences.

## Uses

### Direct Use
This dataset is intended for training, evaluating, and benchmarking machine learning models that predict enzyme kinetic parameters from protein sequences or structural features.

### Downstream Use
The dataset can be used to train or benchmark other machine learning models for enzyme kinetic parameter prediction, or to reproduce and extend the experiments described in the CatPred publication. 

### Out-of-Scope Use
This dataset reflects in vitro measurements and may not generalize to in vivo conditions. It should not be used as a sole basis for clinical or industrial enzyme selection without additional experimental validation.

## Dataset Structure

The repository contains:
- datasets/ – CSV files for *k*cat, *K*m, and *K*i with train/test splits
- scripts/ – Preprocessing and utility scripts

## Data Fields

Each entry typically includes:

## Source Data

Data was compiled and curated from public biochemical databases, including BRENDA and SABIO-RK, as described in the CatPred publication. Splits were designed to evaluate generalization to enzyme sequences dissimilar to those seen during training.

## Citation

If you use this dataset, please cite:

**BibTeX:**
```bibtex
@article{boorla2025catpred,
  title={CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters},
  author={Boorla, Veda Sheersh and Maranas, Costas D.},
  journal={Nature Communications},
  volume={16},
  pages={2072},
  year={2025},
  doi={10.1038/s41467-025-57215-9}
}
```

**APA:**  
Boorla, V. S., & Maranas, C. D. (2025). CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters. *Nature Communications*, 16, 2072. https://doi.org/10.1038/s41467-025-57215-9

## License

MIT - see [LICENSE](https://github.com/maranasgroup/CatPred-DB/blob/main/LICENSE)

## Model Card Authors [optional]

[More Information Needed]