DeepFoldProtein/uniref50_processed

UniRef50 (Processed, ESM-valid as Validation)

Dataset Summary

This dataset is a preprocessed UniRef50 snapshot tailored for unsupervised protein representation learning. It:

• 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.

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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.

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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.

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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.

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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".

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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.

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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))

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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"))

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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.

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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).

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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).

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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.

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Contact

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

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