Datasets:

DeepFoldProtein
/

uniref50_processed

Normalizes sequences (uppercase, * removed), filters by length and ambiguity, and deduplicates by MD5.
Splits by UniRef50 cluster ID to prevent leakage.
Uses the official ESM validation headers as the entire valid split (no sampling).
Provides JSONL.zst shards for efficient streaming with 🤗 datasets.

If you need the exact preprocessing script: see Reproducibility below.

Source

Upstream data: UniProt / UniRef50 (2018_03 snapshot).
Evaluation headers: uniref201803_ur50_valid_headers.txt from the ESM paper.

Please respect UniProt terms when using or redistributing this derivative dataset.

Splits

Split	Definition	Notes
`train`	All clusters not in ESM valid and not hashed into test	Majority of UniRef50
`valid`	Only clusters in ESM’s validation header list	Field `is_esm_valid=true` for all records
`test`	Hash‐based holdout by cluster: `xxhash64(cluster_id) % 100 == 2`	Small random holdout

Splitting by cluster_id avoids train/val/test contamination across cluster members.

Features (Schema)

Field	Type	Description
`id`	string	Stable ID = `cluster_id	md5[:8]`
`sequence`	string	Normalized AA sequence (uppercase; `*` removed)
`length`	int32	Sequence length after normalization
`cluster_id`	string	UniRef50 cluster ID (e.g., `UniRef50_Q8WZ42-5`)
`description`	string?	Optional description parsed from FASTA header (after `Cluster:`)
`seq_md5`	string	MD5 of normalized sequence
`is_esm_valid`	bool	`true` iff the record belongs to the ESM validation header set

Ambiguous residues: records with ambiguity fraction > 5% (non-canonical AAs) are filtered out by default.

Preprocessing & Filters

Normalization: uppercase, remove terminal/internal *.
Length filter: keep 30 ≤ L ≤ 1024.
Ambiguity filter: keep sequences with ≤ 5% non-canonical residues (ACDEFGHIKLMNPQRSTVWY are canonical).
Deduplication: exact dedup by MD5 of normalized sequence (global).
Splitting: by cluster_id as described above.
Headers: FASTA lines like >UniRef50_Q8WZ42-5 Cluster: Isoform 5 of Titin → cluster_id="UniRef50_Q8WZ42-5", description="Isoform 5 of Titin".

Intended Use

Self-supervised training of protein LMs/encoders that must be robust to substitutions and indels (e.g., OT/UOT objectives).
Evaluation aligned with the ESM paper by using the official validation header set for valid.

Not intended for clinical use. No personal data.

How to Load (Streaming & Local)

Streaming (recommended for large shards)

from datasets import load_dataset

repo = "DeepFoldProtein/uniref50_processed"  # replace with your namespace
ds_train = load_dataset(repo, split="train", streaming=True)
row = next(iter(ds_train))
print(row["cluster_id"], row["length"])

Extract ESM-valid subset (within `valid`)

from datasets import load_dataset

ds_valid = load_dataset(repo, split="valid", streaming=True)
esm_valid = ds_valid.filter(lambda x: x["is_esm_valid"])
print(next(iter(esm_valid)))

Non-streaming load (small splits only)

from datasets import load_dataset
ds_test = load_dataset(repo, split="test")  # materializes locally
print(len(ds_test))

Quick Stats Helper

Use this helper to print length statistics per split:

from datasets import load_dataset
import math

def stats(split):
    ds = load_dataset("DeepFoldProtein/uniref50_processed", split=split, streaming=True)
    n=s=s2=0; mn=10**9; mx=0
    for r in ds:
        L = int(r.get("length", len(r["sequence"])))
        n += 1; s += L; s2 += L*L; mn = min(mn, L); mx = max(mx, L)
    mean = s/n if n else float("nan")
    std = math.sqrt(max(0.0, s2/n - mean*mean)) if n else float("nan")
    return {"count": n, "min": mn, "max": mx, "mean": mean, "std": std}

print(stats("train"))
print(stats("valid"))
print(stats("test"))

Licensing

Data source: UniProt / UniRef50. Follow the UniProt license and attribution requirements: https://www.uniprot.org/help/license
Derivative dataset: You must attribute UniProt and include a link to their license when redistributing.
Code (preprocessing): Provide your own license for the script if you distribute it.

Citation

If you use this dataset, please cite UniProt and (optionally) ESM:

UniProt:

The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. (2018)

ESM:

Rives et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science (2023).

Known Limitations

Snapshot drift: This mirrors UniRef50 (2018_03) conventions; later UniRef releases may differ.
Non-random validation: valid is defined by ESM’s curated header list (by design).
Ambiguity handling: Sequences with >5% ambiguous residues are dropped; adjust if you need broader coverage.
Dedup scope: Deduplication is by normalized sequence only (not by cluster consensus).

Changelog / Versioning

v1.0: Initial release — ESM-valid set defines valid; hash-based test; JSONL.zst shards; manifest schema above.
Future updates will be tagged with semantic versions and described here.

Contact

Issues: Please open a GitHub issue or HF discussion on this dataset repo.

If you’d like, I can also generate a minimal dataset_info.yaml with this schema so the Hub shows the features immediately.

UniRef50 (Processed, ESM-valid as Validation)

Dataset Summary