stevensu123/proteinchameleon-structure-function

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, 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

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/ for exact reproducibility (np.load(..., allow_pickle=True)).

Data Collection & Curation

Sources

• Sequences & function annotations — UniProtKB / Swiss-Prot (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, 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

@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}
}