Update README.md

README.md

@@ -1,84 +1,174 @@
 ---
-license: other
 pretty_name: SKEMPI v2
-size_categories:
-  - 1K<n<10K
-task_categories:
-  - other
-language:
-  - en
+license: other
 tags:
-  - biology
-  - proteins
-  - binding-affinity
-  - mutation
-  - skempi
-  - jsonl
+- biology
+- protein
+- protein-protein-interaction
+- binding-affinity
+- mutation
+- protein-engineering
+- skempi
+- skempi2
+- jsonl
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: tables/labeled_skempi2_skempi_v2.csv.jsonl
 ---
 
 # SKEMPI v2
 
-SKEMPI v2 protein-protein binding affinity mutation dataset, normalized to newline-delimited JSON with row-level provenance.
+SKEMPI v2 is a manually curated benchmark of experimentally measured changes in protein-protein binding affinity, kinetics, and thermodynamics upon mutation. It focuses on structurally resolved protein-protein interactions, linking mutations to PDB complexes and literature-derived binding measurements. The dataset is commonly used to evaluate models that predict mutation effects on protein interfaces, binding free energy changes, and protein complex stability.
 
-Processed and uploaded by the [MegaData](https://github.com/) post-download pipeline
-(internal repo). Original source: <https://life.bsc.es/pid/skempi2>.
+This Hugging Face mirror stores the SKEMPI v2 CSV as normalized JSONL rows with provenance. Each record contains the original upstream row under `row`, plus `dataset_id`, `row_index`, and `source_file`. The upstream table contains 7,085 mutation records, including single and multiple mutations, wild-type and mutant affinities, optional kinetic measurements, optional enthalpy and entropy values, experiment temperature, literature references, protein names, and SKEMPI version labels.
 
-## Statistics
+## Splits
 
-| | |
-|---|---|
-| Table files | 1 |
-| Total rows | 7,085 |
-| Total bytes | 5.86 MiB (6,148,969) |
+- `train`: 7,085 rows
 
-## Tables
+There is no official train/test split in this mirror. Users should define task-specific splits carefully, for example by holding out PDB complexes, protein pairs, or interaction families, to avoid leakage between similar mutations from the same complex.
 
-| Table | Rows | Bytes |
-|---|---:|---:|
-| `labeled_skempi2_skempi_v2.csv.jsonl` | 7,085 | 5.86 MiB |
+## Columns
 
-## Layout
+Every JSONL record has these outer fields:
 
-```
-.
-├── _MANIFEST.json                 # aggregate manifest (per-table counts)
-└── tables/<source_slug>.jsonl    # normalized rows (one JSON object per line)
-```
+- `dataset_id`: dataset identifier, always `skempi2`
+- `row`: raw upstream SKEMPI CSV row stored as a nested JSON object
+- `row_index`: zero-based row index in the upstream source table
+- `source_file`: original source path, `labeled/skempi2/skempi_v2.csv`
+
+The upstream SKEMPI CSV fields are semicolon-separated and include:
+
+- `#Pdb`: PDB complex and chain identifier
+- `Mutation(s)_PDB`: mutation notation using PDB residue numbering
+- `Mutation(s)_cleaned`: cleaned mutation notation
+- `iMutation_Location(s)`: mutation location class, such as core, rim, support, or surface
+- `Hold_out_type`, `Hold_out_proteins`: split/grouping metadata from SKEMPI
+- `Affinity_mut (M)`, `Affinity_mut_parsed`: mutant binding affinity
+- `Affinity_wt (M)`, `Affinity_wt_parsed`: wild-type binding affinity
+- `Reference`: source publication identifier
+- `Protein 1`, `Protein 2`: interacting protein names
+- `Temperature`: experimental temperature
+- `kon_mut`, `kon_wt`: mutant and wild-type association rates when available
+- `koff_mut`, `koff_wt`: mutant and wild-type dissociation rates when available
+- `dH_mut`, `dH_wt`: mutant and wild-type enthalpy values when available
+- `dS_mut`, `dS_wt`: mutant and wild-type entropy values when available
+- `Notes`: free-text notes
+- `Method`: experimental method
+- `SKEMPI version`: source SKEMPI version label
 
-Each line in a `tables/*.jsonl` file is a JSON object with at least
-`dataset_id`, `row` (the raw upstream row), `row_index`, and `source_file`
-fields, so every row carries its upstream provenance.
+## Usage
 
-## Loading
+Download the repository:
 
 ```bash
 hf download LiteFold/SKEMPI2 --repo-type dataset --local-dir ./skempi2
 ```
 
-Programmatic streaming:
+Load the JSONL table with `datasets`:
 
 ```python
-import json
-from pathlib import Path
-from huggingface_hub import snapshot_download
-
-local = snapshot_download(repo_id="LiteFold/SKEMPI2", repo_type="dataset")
-for jsonl in sorted(Path(local, "tables").glob("*.jsonl")):
-    with jsonl.open() as f:
-        for line in f:
-            row = json.loads(line)
-            ...  # row["row"] is the upstream record
+from datasets import load_dataset
+
+ds = load_dataset("LiteFold/SKEMPI2", split="train")
+print(ds[0])
 ```
 
-## License
+The current mirror keeps the original semicolon-separated CSV row as the single value inside `row`. This helper expands it into a normal dictionary:
 
-See upstream SKEMPI v2 license.
+```python
+from datasets import load_dataset
+
+HEADER = (
+    "#Pdb;Mutation(s)_PDB;Mutation(s)_cleaned;iMutation_Location(s);"
+    "Hold_out_type;Hold_out_proteins;Affinity_mut (M);Affinity_mut_parsed;"
+    "Affinity_wt (M);Affinity_wt_parsed;Reference;Protein 1;Protein 2;"
+    "Temperature;kon_mut (M^(-1)s^(-1));kon_mut_parsed;"
+    "kon_wt (M^(-1)s^(-1));kon_wt_parsed;koff_mut (s^(-1));"
+    "koff_mut_parsed;koff_wt (s^(-1));koff_wt_parsed;"
+    "dH_mut (kcal mol^(-1));dH_wt (kcal mol^(-1));"
+    "dS_mut (cal mol^(-1) K^(-1));dS_wt (cal mol^(-1) K^(-1));"
+    "Notes;Method;SKEMPI version"
+)
+
+fields = HEADER.split(";")
+
+def expand_record(record):
+    raw_value = record["row"][HEADER]
+    values = raw_value.split(";")
+    parsed = dict(zip(fields, values))
+    parsed["dataset_id"] = record["dataset_id"]
+    parsed["row_index"] = record["row_index"]
+    parsed["source_file"] = record["source_file"]
+    return parsed
+
+ds = load_dataset("LiteFold/SKEMPI2", split="train")
+row = expand_record(ds[0])
+
+print(row["#Pdb"])
+print(row["Mutation(s)_cleaned"])
+print(row["Affinity_mut_parsed"], row["Affinity_wt_parsed"])
+```
 
-## Citation
+Stream and compute a simple binding-affinity change target:
 
-> Jankauskaite J, et al. SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics, 35(3):462-469, 2019.
+```python
+import math
+from datasets import load_dataset
+
+R = 1.9872036e-3  # kcal mol^-1 K^-1
+DEFAULT_T = 298.15
+
+ds = load_dataset("LiteFold/SKEMPI2", split="train", streaming=True)
+
+for record in ds:
+    row = expand_record(record)
+    kd_mut = float(row["Affinity_mut_parsed"])
+    kd_wt = float(row["Affinity_wt_parsed"])
+    temperature = float(row["Temperature"] or DEFAULT_T)
+    ddg = R * temperature * math.log(kd_mut / kd_wt)
+    print(row["#Pdb"], row["Mutation(s)_cleaned"], ddg)
+    break
+```
 
-## Provenance
+Load directly from the JSONL path:
 
-Built from the local manifest entry `skempi2` of `manifests/atlas_download_plan.json`.
-Pipeline source: `megadata-post normalize --dataset skempi2 --tables-only`.
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "json",
+    data_files="hf://datasets/LiteFold/SKEMPI2/tables/labeled_skempi2_skempi_v2.csv.jsonl",
+    split="train",
+)
+```
+
+## Data Notes
+
+SKEMPI v2 contains experimentally curated values, but it is still a benchmark that needs careful splitting. Random row splits can leak information because many records share the same PDB complex, protein pair, or closely related mutations. For model evaluation, prefer complex-level, protein-pair-level, or interaction-family-level splits depending on the question.
+
+Binding affinities are reported as dissociation constants in molar units. A common derived supervised target is `ddG = R * T * ln(Kd_mut / Kd_wt)`, but users should check units, missing temperatures, censored or non-detectable binding values, and multi-mutation rows before training or benchmarking.
+
+The Hugging Face dataset viewer may fail to preview this mirror because the original CSV row is nested under a long header key. The local JSONL and `datasets` loading examples above are the most reliable way to consume the current upload.
+
+## License
+
+The dataset card metadata uses `license: other` because SKEMPI v2 should be used according to the upstream SKEMPI terms and citation requirements.
+
+# Citation
+
+```bibtex
+@article{jankauskaite2019skempi2,
+  title   = {{SKEMPI} 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation},
+  author  = {Jankauskait{\.e}, Justina and Jim{\'e}nez-Garc{\'i}a, Brian and Dapk{\=u}nas, Justas and Fern{\'a}ndez-Recio, Juan and Moal, Iain H.},
+  journal = {Bioinformatics},
+  volume  = {35},
+  number  = {3},
+  pages   = {462--469},
+  year    = {2019},
+  doi     = {10.1093/bioinformatics/bty635},
+  url     = {https://doi.org/10.1093/bioinformatics/bty635}
+}
+```