| --- |
| language: |
| - en |
| license: bsd-3-clause |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - graph-ml |
| pretty_name: TEDBench-CATH |
| tags: |
| - protein |
| - structure |
| - fold-classification |
| - tedbench |
| - cath |
| - experimental |
| --- |
| |
| # TEDBench-CATH (CATH 4.4 external test set) |
|
|
| External test set derived from **CATH 4.4** experimental structures (40 % |
| non-redundant set), used to evaluate TEDBench models on crystallographic data. |
|
|
| This dataset is part of the **TEDBench** benchmark, introduced in the paper [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552). |
|
|
| **Links:** |
| - **Code:** [https://github.com/BorgwardtLab/TEDBench](https://github.com/BorgwardtLab/TEDBench) |
| - **Paper:** [https://huggingface.co/papers/2605.18552](https://huggingface.co/papers/2605.18552) |
|
|
| ## Dataset statistics |
|
|
| | Split | Structures | |
| |-------|----------:| |
| | Test | 28,010 | |
|
|
| **965 CATH topology (T-level) classes** — same label space as |
| [TEDBench/ted](https://huggingface.co/datasets/TEDBench/ted). |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `name` | `string` | PDB chain identifier in `<pdbid>.<chain>` format (e.g. `1abc.A`) | |
| | `sequence` | `string` | Amino-acid sequence (single-letter code) | |
| | `coords` | `[L, 3, 3]` float32 | Backbone N/Cα/C coordinates (Å) from experimental structure | |
| | `plddt` | `[L]` float32 | Per-residue confidence proxy (set to 100 for experimental structures) | |
| | `residue_index` | `[L]` int64 | Author residue number from the PDB file | |
| | `seq_ids` | `[L]` int64 | ESM-tokenised sequence IDs | |
| | `label` | `ClassLabel` | CATH topology class index (same label space as TEDBench) | |
|
|
| Protein identifiers follow the `<pdbid>.<chain>` convention (e.g. `1abc.A`), |
| matching the CATH domain annotation files. The `label` `ClassLabel` uses the |
| same CATH topology strings and integer indices as the TEDBench training set. |
|
|
| ## Usage |
|
|
| ### Load from HuggingFace |
|
|
| ```python |
| from datasets import load_dataset |
| import torch |
| |
| cath = load_dataset("TEDBench/cath", split="test") |
| sample = cath[0] |
| |
| coords = torch.tensor(sample["coords"]) # [L, 3, 3] |
| label = sample["label"] # int index |
| cath_code = cath.features["label"].int2str(label) |
| ``` |
|
|
| ### Use in TEDBench training scripts |
|
|
| ```bash |
| python main_test_ted.py datamodule=hf_cath_test pretrained_model_path=<ckpt> |
| ``` |
|
|
| ## Source data |
|
|
| CATH 4.4 40 % non-redundant representative set from [CATHDB](https://www.cathdb.info/wiki?id=data:index). Structures fetched from |
| PDB-REDO / RCSB, quality-filtered, and converted to single-chain PDB files. The `plddt` column is set to |
| 100.0 for all residues (these are experimental structures, not AlphaFold |
| predictions). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{chen2026tedbench, |
| title={Protein Fold Classification at Scale: Benchmarking and Pretraining}, |
| author={Chen, Dexiong and Manolache, Andrei and Niepert, Mathias and Borgwardt, Karsten}, |
| booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| BSD-3-Clause |
