README.md

---
dataset_info:
  features:
  - name: sequence
    dtype: string
  - name: annotation
    list: int64
  - name: description
    dtype: string
  - name: labels
    dtype: int64
  splits:
  - name: train
    num_bytes: 71733637
    num_examples: 60666
  - name: valid
    num_bytes: 3939369
    num_examples: 3316
  - name: test
    num_bytes: 3960144
    num_examples: 3420
  download_size: 53137709
  dataset_size: 79633150
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: valid
    path: data/valid-*
  - split: test
    path: data/test-*
license: cc-by-4.0
language:
- en
tags:
- biology
- protein
- protein-language-model
- embeddings
- representation-learning
- uniprot
- gene-ontology
pretty_name: PLAT (Protein Language Alignment Tuples)
task_categories:
- feature-extraction
- sentence-similarity
size_categories:
- 10K<n<100K
---

# PLAT: Protein Language Alignment Tuples

Every row of PLAT describes one protein three ways: its amino acid sequence, the typed functional
annotations UniProt curators gave it, and the free text they wrote about it. The three views share no
input, so PLAT is a test bed for asking whether the embedding spaces of protein language models,
annotation encoders and text encoders can be aligned or translated into one another. It holds 67,402
UniProtKB proteins with curated GO terms, one function-annotated representative per 40% identity cluster, split by
cluster into train, validation and test. It was built for the Gleghorn Lab's study of embedding
translation between protein representation spaces, whose frozen embeddings of these rows are in
[GleghornLab/vec2vec_embeddings](https://huggingface.co/datasets/GleghornLab/vec2vec_embeddings).

| | |
|---|---|
| Task | Cross-view alignment and retrieval: sequence, annotation, description |
| Rows | 60,666 train, 3,316 valid, 3,420 test |
| Homology | Clustered at 40% identity before the split; not disjoint under a stricter later screen (below) |
| Source | UniProtKB entries with manually curated GO terms, release 2025_04 |
| Licence | CC BY 4.0 |

## Homology leakage

**Intended: 40% identity, by cluster.** Proteins were clustered with CD-HIT at 40% identity and whole
clusters were assigned to one split, so no sequence appears in two splits.

**Measured afterwards: not homology-disjoint.** CD-HIT's greedy clustering does not guarantee that
members of different clusters fall below the threshold, and the cluster assignment was not kept. A
later screen with CD-HIT-2D, counting a pair when it reached 40% global identity over at least 80% of
both sequences, found:

| Held-out split | Proteins with a qualifying neighbor | Share |
|---|---|---|
| valid, in train | 423 of 3,316 | 12.76% |
| test, in train | 430 of 3,420 | 12.57% |
| test, in valid | 50 of 3,420 | 1.46% |
| test, in train or valid | 454 of 3,420 | 13.27% |

No exact sequence is shared between splits. For a strictly remote-homology evaluation, drop the 454
test proteins with a neighbor, or recluster.

## How it was built

1. Read the UniProtKB entries with manually curated GO annotations, exported on 18 November 2025 (for
   annotations) and 2 December 2025 (for free text) from release 2025_04; 1,380,840 entries each.
2. Built the typed annotation list of each entry from eight fields: GO biological process, cellular
   component and molecular function, EC number, cofactor, InterPro, Gene3D and UniProt keywords. Each
   term became a typed key such as `GO:0016310_bp` or `1.1.1.1_ec`, and each key an integer id.
3. Built the description of each entry from the Function, Miscellaneous, Subcellular location and
   Domain comments, each under its own header, with PubMed citations, ECO evidence codes and the
   comment prefixes removed.
4. Removed exact duplicate sequences.
5. Clustered the sequences with CD-HIT at 40% identity (word size 5) and assigned whole clusters to
   train, validation and test in the proportions 90, 5 and 5, after a shuffle.
6. From each cluster, kept the members that had both annotations and a description containing
   Function text, and drew one of them at random (seed 42). A cluster with no such member was dropped,
   so PLAT leans toward well characterized families.
7. Added a constant `labels` column so generic training loops that expect one run unchanged.

## Columns

| Column | Type | What it is |
|---|---|---|
| `sequence` | string | Canonical amino acid sequence, single-letter code; median length 403, longest 8,923 |
| `annotation` | list of int | Sorted, deduplicated typed annotation ids; median 18 per protein, 56,868 distinct ids in use |
| `description` | string | UniProt curator text under `Function:` and, when present, `Miscellaneous:`, `Subcellular Localization:` and `Domain:` headers; median 339 characters |
| `labels` | int | Always 0; a placeholder, not a label |

The integer ids index the Annotation Vocabulary (Hallee et al., 2024), here built from this UniProt
release with about 88,000 typed keys across the eight annotation types. The id to key mapping is not
yet published with this dataset.

## Example rows

From the test split, sequences and lists shortened.

| sequence | annotation | description |
|---|---|---|
| `MDKKEILKEFSADPDKYYNV...` (889 aa) | 31 ids: 6315, 8144, 24978, ... | Function: Catalyzes the attachment of alanine to tRNA(Ala) in a two-step reaction... |
| `MSHQTGIKGNVELISFFGKC...` (344 aa) | 18 ids: 9957, 10213, 11596, ... | Function: Actin-binding protein involved in motile and morphological processes. Inhibits actin polymerization... Subcellular Localization: Cytoplasm, cell cortex. Cytoplasm, cytoskeleton. |
| `MTISMISPSSIRLSSDKRDS...` (410 aa) | 23 ids: 9619, 15796, 18993, ... | Function: Promotes excretion of zinc from intestinal cells into the intestinal lumen in response to increased dietary zinc... |

```python
from datasets import load_dataset

plat = load_dataset("GleghornLab/plat_data")
row = plat["test"][0]
row["sequence"], row["annotation"], row["description"]
```

## What a model trained on it may say

A model trained on PLAT maps one view of a protein to another: a sequence embedding to an annotation or
description embedding, or back. A good retrieval score says the views of the same protein can be found
from each other among held-out proteins. It does not say the model understands protein function: a
sequence-only control can retrieve well through shared input alone, and about one held-out protein in
eight has a close relative in train. Every protein has curated GO terms and Function text, so the
dataset says little about poorly characterized proteins.

## Sources and licence

All sequences, annotations and descriptions come from
[UniProtKB](https://www.uniprot.org), redistributed under its
[CC BY 4.0 licence](https://www.uniprot.org/help/license). The annotation types draw on the Gene
Ontology, the IUBMB Enzyme Commission, InterPro, CATH-Gene3D and the UniProt keywords. If you use PLAT,
please cite UniProt:

> The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2025. *Nucleic Acids Research*, 2025.

Limits worth knowing: only entries with manually curated GO terms were read, so taxa that curators have
studied little are thin here; each cluster contributes one protein, so paralogs and species variants are absent; the
descriptions lose their citations and evidence codes; and the data reflect one UniProt release.

## Files

| File | What it is |
|---|---|
| `data/train-00000-of-00001.parquet` | 60,666 training rows |
| `data/valid-00000-of-00001.parquet` | 3,316 validation rows |
| `data/test-00000-of-00001.parquet` | 3,420 test rows |