Update README.md

README.md

@@ -1,35 +1,199 @@
 ---
-dataset_info:
-  features:
-  - name: seqs
-    dtype: string
-  - name: labels
-    dtype: float64
-  - name: Accession
-    dtype: string
-  - name: Organism
-    dtype: string
-  - name: EC Number
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 3461335
-    num_examples: 7124
-  - name: valid
-    num_bytes: 372844
-    num_examples: 760
-  - name: test
-    num_bytes: 954440
-    num_examples: 1971
-  download_size: 4258985
-  dataset_size: 4788619
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: valid
-    path: data/valid-*
-  - split: test
-    path: data/test-*
+license: mit
+task_categories:
+  - tabular-regression
+tags:
+  - protein
+  - enzymes
+  - pH
+  - regression
+  - biology
+pretty_name: Optimal pH (EpHod pHopt)
+size_categories:
+  - 1K<n<10K
 ---
+
+# 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
+
+```python
+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:
+
+```python
+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](https://github.com/jafetgado/EpHod).
+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):
+
+```bibtex
+@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:
+
+```bibtex
+@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):
+
+```bibtex
+@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):
+
+```bibtex
+@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):
+
+```bibtex
+@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}
+}
+```