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license: cc-by-4.0
pretty_name: ProteinChameleon Structure–Function Dataset
language:
- en
size_categories:
- 100K<n<1M
task_categories:
- text-generation
tags:
- protein
- biology
- protein-structure
- protein-function
- structure-tokens
- geobpe
- multimodal
configs:
- config_name: alignment
data_files:
- split: train
path: alignment/train.parquet
- split: validation
path: alignment/val.parquet
- split: test
path: alignment/test.parquet
- config_name: interleaved
data_files:
- split: train
path: interleaved/train.parquet
- split: validation
path: interleaved/val.parquet
- split: test
path: interleaved/test.parquet
---
# ProteinChameleon Structure–Function Dataset
A leakage-free protein dataset pairing **3D-structure tokens** and amino-acid
sequences with **natural-language function descriptions**. It was built to train and
evaluate [ProteinChameleon](https://github.com/), an early-fusion protein–text model
that ingests discrete structure tokens alongside sequence. Structure is encoded with
**GeoBPE**, a geometric byte-pair tokenizer that turns a protein's 3D backbone into a
sequence of ~2,100 discrete tokens (merged multi-residue structural motifs plus
quantized dihedral/angle bins).
The distinguishing feature of this release is its **split protocol**: train / validation
/ test are separated by *connected components* over both sequence homology and function
text, so the test set measures genuine generalization rather than near-neighbor recall.
## Configurations
| Config | Task | Train | Validation | Test |
|---|---|---:|---:|---:|
| `alignment` | structure + sequence → function text | 243,127 | 13,504 | 13,506 |
| `interleaved` | multimodal protein narratives (domain-segmented) | 63,117 | 4,303 | 4,206 |
### `alignment` fields
| Field | Type | Description |
|---|---|---|
| `accession` | string | UniProt accession |
| `organism` | string | Source organism |
| `sequence` | string | Amino-acid sequence |
| `function_text` | string | Curated natural-language function description |
| `structure_tokens` | list[int] | GeoBPE structure token IDs for the protein's 3D backbone |
### `interleaved` fields
| Field | Type | Description |
|---|---|---|
| `accession` | string | UniProt accession |
| `organism` | string | Source organism |
| `sequence` | string | Amino-acid sequence |
| `narrative` | string | Multimodal narrative interleaving text with domain-level context |
| `n_domains` | int | Number of annotated domains in the narrative |
## Usage
```python
from datasets import load_dataset
# structure -> function
ds = load_dataset("<your-username>/proteinchameleon-structure-function", "alignment")
print(ds["test"][0]["function_text"])
print(ds["test"][0]["structure_tokens"][:16])
# multimodal narratives
di = load_dataset("<your-username>/proteinchameleon-structure-function", "interleaved")
```
The original NumPy artifacts are also included under [`raw/`](./raw) for exact
reproducibility (`np.load(..., allow_pickle=True)`).
## Data Collection & Curation
### Sources
- **Sequences & function annotations** — [UniProtKB / Swiss-Prot](https://www.uniprot.org)
(manually reviewed entries). `function_text` is derived from curated UniProt function
annotations; `organism` and `accession` are taken directly from the entry.
- **3D structures** — [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk),
tokenized with **GeoBPE**. Each backbone is quantized into geometric tokens and merged
into structural-motif tokens, yielding the `structure_tokens` sequence.
### Leakage-free split protocol
Naïve per-accession splits leak badly, because protein databases are highly redundant and
UniProt function text is shared verbatim across orthologs. Measured on the original splits:
- **Homology leakage** — 94% of test proteins had a ≥30%-identity homolog in train/val.
- **Function-text leakage** — 79% of test function descriptions appeared verbatim in train.
- **Cross-task leakage** — 91% of the interleaved-test proteins also appeared in
alignment-train (the two tasks were split independently but trained jointly).
To remove all three, the splits in this release (`clean2`) are built as follows:
1. **Union of both tasks.** Pool every protein appearing in either the alignment or
interleaved task into a single set.
2. **Homology graph.** Cluster all sequences with **MMseqs2** at **30% sequence identity**
and **80% coverage** (`mmseqs easy-cluster --min-seq-id 0.3 -c 0.8`). Add an edge
between any two proteins in the same cluster.
3. **Function-text graph.** Add an edge between any two proteins that share an *identical*
function description.
4. **Connected components.** Take connected components of the combined graph (union-find).
Homologous proteins and proteins sharing boilerplate function text therefore always fall
in the same component.
5. **Component-level assignment.** Assign whole components to train / validation / test
(~90 / 5 / 5%), so no protein — and no close homolog or shared description — ever
crosses the split boundary. The **same assignment is applied to both tasks**, eliminating
cross-task leakage.
A machine-readable `split_manifest_clean2.json` and the per-protein
`split_assignment_clean2.json` are included under `raw/` for auditing.
### Verification
After construction, the splits were re-checked for all three leakage channels; homology,
function-text, and cross-task overlap between test and train/val are driven to **zero**
under the 30%-identity criterion.
## Limitations & Intended Use
- `structure_tokens` are **GeoBPE codes**, not 3D coordinates; they are only meaningful with
a model trained on the same GeoBPE vocabulary. They are not interchangeable with other
structure-token schemes (e.g. Foldseek 3Di, ESM3 structure tokens).
- Function text is curated prose and may contain UniProt-style boilerplate; it is intended
for conditional generation / understanding, not as a controlled ontology (use the EC/CATH
ontologies for label-based tasks).
- The dataset reflects the coverage and biases of Swiss-Prot and AlphaFold DB.
## License & Attribution
Released under **CC-BY-4.0**, consistent with the source databases. If you use this dataset,
please attribute the underlying resources:
- The UniProt Consortium, *UniProt: the Universal Protein Knowledgebase* (CC-BY-4.0).
- Varadi et al., *AlphaFold Protein Structure Database* (CC-BY-4.0).
## Citation
```bibtex
@misc{proteinchameleon_dataset,
title = {ProteinChameleon Structure--Function Dataset},
author = {Steven},
year = {2026},
note = {Leakage-free structure-token / function-text splits derived from
UniProt/Swiss-Prot and AlphaFold DB via GeoBPE tokenization}
}
```
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