Optimal pH (optimal_ph)

Enzyme optimum-pH (pHopt) regression dataset. Each row is a single enzyme with its UniProt accession, amino acid sequence, and the experimentally reported pH at which its catalytic activity is maximal. This is a re-release of the exact train/valid/test split used in the EpHod paper (Gado et al., 2025), mirrored from the authors' Zenodo deposit and reformatted for the Hugging Face datasets library.

• Task: regression (predict labels ∈ ℝ from seqs)
• Input: single-chain protein sequence (amino acids, length 32–1022)
• Target: optimum pH (typically ~1.0 to ~12.5, centered near 7)
• n = 9,855 enzymes over 7,124 train / 760 valid / 1,971 test

Columns

column	dtype	description
seqs	string	single-letter amino acid sequence
labels	float	experimental pHopt (mean over BRENDA entries)
Accession	string	UniProt accession
Organism	string	source organism (species) from UniProt
EC Number	string	Enzyme Commission number

Splits

split	rows	note
train	7,124	mmseqs2 clusters at 20% identity, 90% of non-test clusters
valid	760	10% of non-test clusters
test	1,971	held-out 20% random sample of the full dataset

The split is inherited verbatim from the EpHod paper. The test set spans the same pHopt distribution as training (not identity-held-out), so performance on this split does not measure generalisation to dissimilar sequences by itself. For that, use the paper's Test <20% to Train annotation (not included here; available in the Zenodo deposit).

Usage

from datasets import load_dataset

ds = load_dataset("GleghornLab/optimal_ph")
print(ds)
print(ds["test"][0])

Known caveat: sequence-level leakage

The authors' published split contains 24 byte-identical sequences that appear in more than one split (20 train↔test, 4 valid↔test). These are orthologs, paralogs, or reviewed/unreviewed pairs with different UniProt accessions but identical amino acid strings. Some pairs have conflicting pHopt labels or different EC annotations, which puts a ceiling on test RMSE for any model that memorises training examples.

9,855 accessions collapse to 9,774 unique sequences; 24 of the duplicates cross split boundaries.

If you need a leakage-free split, de-duplicate by sequence or re-cluster train+valid+test jointly before training.

Construction

This Hub dataset was built from the authors' raw pHopt_data.csv (Zenodo deposit linked below) with the following one-shot transform:

import pandas as pd
from datasets import Dataset, DatasetDict

SPLIT_MAP = {"Training": "train", "Validation": "valid", "Testing": "test"}
KEEP_COLS = ["seqs", "labels", "Accession", "Organism", "EC Number"]

df = pd.read_csv("pHopt_data.csv", index_col=0)
df = df.rename(columns={"Sequence": "seqs", "pHopt": "labels"})
df = df.drop(columns=["Sample Weight", "Test <20% to Train"])

dsd = DatasetDict({
    new_name: Dataset.from_pandas(
        df[df["Split"] == raw_name][KEEP_COLS].reset_index(drop=True),
        preserve_index=False,
    )
    for raw_name, new_name in SPLIT_MAP.items()
})
dsd.push_to_hub("GleghornLab/optimal_ph")

Source data

• BRENDA (accessed 2022-05-25): 49,227 pH-optimum entries; 11,174 had UniProt sequence mappings. Multiple entries per sequence were averaged if within 1.0 pH unit, else dropped. Sequences <32 or >1022 aa were dropped, leaving 9,855.
• UniProt: canonical sequences for the retained accessions.
• Split: 20% random holdout → test; remaining clustered with MMseqs2 at 20% identity, 90/10 cluster split → train/valid.

License

Released under MIT to match the EpHod code release. Underlying BRENDA and UniProt data are subject to their own terms (BRENDA academic licence; UniProt CC BY 4.0); cite the sources below.

Citations

EpHod paper (Nature Machine Intelligence, 2025):

@article{Gado2025EpHod,
  title   = {Deep learning prediction of enzyme optimum pH},
  author  = {Gado, Japheth E. and Knotts, Matthew and Shaw, Amber M. and
             Marks, Debora and Gauthier, Nicholas P. and Hopf, Thomas A. and
             Beckham, Gregg T.},
  journal = {Nature Machine Intelligence},
  year    = {2025},
  doi     = {10.1038/s42256-025-01026-6},
  url     = {https://www.nature.com/articles/s42256-025-01026-6}
}

bioRxiv preprint:

@article{Gado2023EpHodPreprint,
  title   = {Deep learning prediction of enzyme optimum pH},
  author  = {Gado, Japheth E. and Knotts, Matthew and Shaw, Amber M. and
             Marks, Debora and Gauthier, Nicholas P. and Hopf, Thomas A. and
             Beckham, Gregg T.},
  journal = {bioRxiv},
  year    = {2023},
  doi     = {10.1101/2023.06.22.544776},
  url     = {https://www.biorxiv.org/content/10.1101/2023.06.22.544776v2}
}

Zenodo data deposit (v1):

@dataset{Gado2024EpHodData,
  title     = {EpHod: Deep learning prediction of enzyme optimum pH (dataset)},
  author    = {Gado, Japheth E. and Knotts, Matthew and Shaw, Amber M. and
               Marks, Debora and Gauthier, Nicholas P. and Hopf, Thomas A. and
               Beckham, Gregg T.},
  publisher = {Zenodo},
  year      = {2024},
  doi       = {10.5281/zenodo.14252615},
  url       = {https://doi.org/10.5281/zenodo.14252615}
}

BRENDA (primary data source):

@article{Chang2021BRENDA,
  title   = {BRENDA, the ELIXIR core data resource in 2021: new developments
             and updates},
  author  = {Chang, Antje and Jeske, Lisa and Ulbrich, Sandra and Hofmann,
             Julia and Koblitz, Julia and Schomburg, Ida and Neumann-Schaal,
             Meina and Jahn, Dieter and Schomburg, Dietmar},
  journal = {Nucleic Acids Research},
  volume  = {49},
  number  = {D1},
  pages   = {D498--D508},
  year    = {2021},
  doi     = {10.1093/nar/gkaa1025}
}

UniProt (sequences):

@article{UniProt2023,
  title   = {UniProt: the Universal Protein Knowledgebase in 2023},
  author  = {{The UniProt Consortium}},
  journal = {Nucleic Acids Research},
  volume  = {51},
  number  = {D1},
  pages   = {D523--D531},
  year    = {2023},
  doi     = {10.1093/nar/gkac1052}
}