--- license: cc-by-4.0 tags: - chemistry - biology pretty_name: CatPred A comprehensive framework for deep learning in vitro enzyme kinetic parameters repo: https://github.com/maranasgroup/CatPred-DB citation_bibtex: "@article{Boorla2025,title = {CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters},volume = {16},ISSN = {2041-1723},url = {http://dx.doi.org/10.1038/s41467-025-57215-9},DOI = {10.1038/s41467-025-57215-9},number = {1},journal = {Nature Communications},publisher = {Springer Science and Business Media LLC},author = {Boorla, Veda Sheersh and Maranas, Costas D.},year = {2025},month = feb}" citation_apa: "Boorla, V. S., & Maranas, C. D. (2025). CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters. Nature Communications, 16(1), 2072. doi:10.1038/s41467-025-57215-9" configs: - config_name: kcat data_files: - split: train path: kcat/kcat_train.csv - split: test path: kcat/kcat_test.csv - split: val path: kcat/kcat_val.csv - config_name: ki data_files: - split: train path: ki/ki_train.csv - split: test path: ki/ki_test.csv - split: val path: ki/ki_val.csv - config_name: km data_files: - split: train path: km/km_train.csv - split: test path: km/km_test.csv - split: val path: km/km_val.csv dataset_info: - config_name: kcat features: - name: sequence dtype: string - name: sequence_source dtype: string - name: uniprot dtype: string - name: reaction_smiles dtype: string - name: value dtype: float64 - name: reaction_mw_diff_perc dtype: float64 - name: temperature dtype: float64 - name: ph dtype: float64 - name: ec dtype: string - name: taxonomy_id dtype: float64 - name: log10_value dtype: float64 - name: reactant_smiles dtype: string - name: product_smiles dtype: string - name: log10kcat_max dtype: float64 - name: group dtype: string - name: pdbpath dtype: string - name: reactant_smiles_20cluster dtype: int64 - name: sequence_20cluster dtype: int64 - name: reactant_smiles_40cluster dtype: int64 - name: sequence_40cluster dtype: int64 - name: reactant_smiles_60cluster dtype: int64 - name: sequence_60cluster dtype: int64 - name: reactant_smiles_80cluster dtype: int64 - name: sequence_80cluster dtype: int64 - name: reactant_smiles_99cluster dtype: int64 - name: sequence_99cluster dtype: int64 - config_name: km features: - name: sequence dtype: string - name: sequence_source dtype: string - name: uniprot dtype: string - name: substrate_smiles dtype: string - name: value dtype: float64 - name: temperature dtype: float64 - name: ph dtype: float64 - name: ec dtype: string - name: taxonomy_id dtype: float64 - name: log10_value dtype: float64 - name: log10km_mean dtype: float64 - name: group dtype: string - name: pdbpath dtype: string - name: substrate_smiles_20cluster dtype: int64 - name: sequence_20cluster dtype: int64 - name: substrate_smiles_40cluster dtype: int64 - name: sequence_40cluster dtype: int64 - name: substrate_smiles_60cluster dtype: int64 - name: sequence_60cluster dtype: int64 - name: substrate_smiles_80cluster dtype: int64 - name: sequence_80cluster dtype: int64 - name: substrate_smiles_99cluster dtype: int64 - name: sequence_99cluster dtype: int64 - config_name: ki features: - name: sequence dtype: string - name: sequence_source dtype: string - name: uniprot dtype: string - name: substrate_smiles dtype: string - name: value dtype: float64 - name: temperature dtype: float64 - name: ph dtype: float64 - name: ec dtype: string - name: taxonomy_id dtype: float64 - name: log10_value dtype: float64 - name: log10ki_mean dtype: float64 - name: group dtype: string - name: pdbpath dtype: string - name: substrate_smiles_20cluster dtype: int64 - name: sequence_20cluster dtype: int64 - name: substrate_smiles_40cluster dtype: int64 - name: sequence_40cluster dtype: int64 - name: substrate_smiles_60cluster dtype: int64 - name: sequence_60cluster dtype: int64 - name: substrate_smiles_80cluster dtype: int64 - name: sequence_80cluster dtype: int64 - name: substrate_smiles_99cluster dtype: int64 - name: sequence_99cluster dtype: int64 - name: canonical_smiles dtype: string --- # 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: | Field | Description | |---|---| | `sequence` | Enzyme amino acid sequence | | `sequence_source` | Source of the sequence | | `uniprot` | UniProt identifier | | `substrate_smiles` | Substrate chemical structure in SMILES format | | `value` | Raw measured kinetic parameter value | | `log10_value` | Log10-transformed kinetic value (use this for modeling) | | `log10km_mean` | Log10 mean Km value for the enzyme-substrate pair | | `temperature` | Assay temperature (°C) | | `ph` | Assay pH | | `ec` | Enzyme Commission (EC) number | | `taxonomy_id` | NCBI taxonomy ID of the source organism | | `group` | Train/val/test split assignment | | `pdbpath` | Path to associated PDB structural file (if available) | | `sequence_40cluster` | Sequence cluster ID at 40% identity threshold | | `sequence_60cluster` | Sequence cluster ID at 60% identity threshold | | `sequence_80cluster` | Sequence cluster ID at 80% identity threshold | | `sequence_99cluster` | Sequence cluster ID at 99% identity threshold | --- ## 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. All SMILES were sanitized with RdKit. Broken SMILES were removed. --- ## Dataset Splits Each kinetic parameter (kcat, km, ki) has two split strategies, described below. ### Split strategies **Random splits** divide the data without regard to sequence similarity. These are useful for a general baseline but tend to overestimate real-world model performance, since training and test enzymes may be closely related. **Sequence-similarity splits** (`seq_test_sequence_XXcluster`) ensure that test set enzymes share less than XX% sequence identity with any enzyme in the training set. This is the more rigorous benchmark — a model that performs well here is genuinely generalizing to novel enzymes rather than recognizing similar sequences it has effectively seen before. Five strictness levels are provided: | Cluster threshold | Test set stringency | | --- | --- | | 20% | Hardest — test enzymes are very dissimilar to training data | | 40% | Hard | | 60% | Moderate | | 80% | Easy | | 99% | Easiest — nearly any sequence may appear in test | ### File Naming Each split file is named `{parameter}-{strategy}_{subset}.csv`. The subsets are: | Subset | Contents | When to use | |---|---|---| | `train` | Training data only | Model development and hyperparameter tuning | | `val` | Validation data only | Monitoring training, early stopping | | `test` | Test data only | Final benchmark evaluation | | `trainval` | Train + val combined | Retrain final model after hyperparameters are locked in | | `trainvaltest` | All data combined | Train a release model once all evaluation is complete | --- ## Quickstart Usage ### Install HuggingFace Datasets package Each subset can be loaded into python using the HuggingFace [datasets](https://huggingface.co/docs/datasets/index) library. First, from the command line, install the `datasets` library ```bash >>> pip install datasets ``` ### Load a subset ```python >>> from datasets import load_dataset # Options: "kcat", "km", "ki" >>> ds = load_dataset("RosettaCommons/CatPred-DB", "kcat") >>> train = ds["train"] >>> val = ds["validation"] >>> test = ds["test"] ``` ``` kcat-random_train.csv: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28.0M/28.0M [00:04<00:00, 6.29MB/s] kcat-random_trainval.csv: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 31.1M/31.1M [00:04<00:00, 6.81MB/s] kcat-random_trainvaltest.csv: 100%|██████████████████████████████████████████████████████████████████████████████████████| 34.5M/34.5M [00:05<00:00, 6.86MB/s] Generating train split: 100%|█████████████████████████████████████████████████████████████████████████████████| 18789/18789 [00:00<00:00, 67580.51 examples/s] Generating validation split: 100%|████████████████████████████████████████████████████████████████████████████| 20877/20877 [00:00<00:00, 78951.22 examples/s] Generating test split: 100%|██████████████████████████████████████████████████████████████████████████████████| 23197/23197 [00:00<00:00, 74253.91 examples/s] ``` ### Key columns | Column | Description | | --- | --- | | `sequence` | Enzyme amino acid sequence | | `uniprot` | UniProt identifier | | `reactant_smiles` | Substrate SMILES | | `value` | Raw kinetic value | | `log10_value` | Log₁₀-transformed value — **use this as your target** | | `temperature` | Assay temperature (°C), nullable | | `ph` | Assay pH, nullable | | `ec` | EC number | | `sequence_40cluster` | Cluster ID at 40% identity — use for similarity-based splits | ### Recommended split workflow ``` train + val → tune architecture and hyperparameters trainval + test → final benchmark (report results here) trainvaltest → train the final released model on all available data ``` This three-stage approach is standard practice in ML: you only touch the test set once, and the combined files make it easy to retrain on progressively more data as you move from experimentation to deployment. ### Basic training setup ```python >>> df = ds["train"].to_pandas() >>> X_seq = df["sequence"] >>> X_sub = df["reactant_smiles"] >>> y = df["log10_value"] # Drop rows with missing targets or substrates >>> mask = y.notna() & X_sub.notna() >>> df = df[mask] ``` --- ## Citation If you use this dataset, please cite: **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) --- ## Dataset Card Authors Jessica Lin, Kuniko Hunter, Manasa Yadavalli, McGuire Metts