---
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