---
license: other
pretty_name: SKEMPI v2
size_categories:
  - 1K<n<10K
task_categories:
  - other
language:
  - en
tags:
  - biology
  - proteins
  - binding-affinity
  - mutation
  - skempi
  - jsonl
---

# SKEMPI v2

SKEMPI v2 protein-protein binding affinity mutation dataset, normalized to newline-delimited JSON with row-level provenance.

Processed and uploaded by the [MegaData](https://github.com/) post-download pipeline
(internal repo). Original source: <https://life.bsc.es/pid/skempi2>.

## Statistics

| | |
|---|---|
| Table files | 1 |
| Total rows | 7,085 |
| Total bytes | 5.86 MiB (6,148,969) |

## Tables

| Table | Rows | Bytes |
|---|---:|---:|
| `labeled_skempi2_skempi_v2.csv.jsonl` | 7,085 | 5.86 MiB |

## Layout

```
.
├── _MANIFEST.json                 # aggregate manifest (per-table counts)
└── tables/<source_slug>.jsonl    # normalized rows (one JSON object per line)
```

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.

## Loading

```bash
hf download LiteFold/SKEMPI2 --repo-type dataset --local-dir ./skempi2
```

Programmatic streaming:

```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
```

## License

See upstream SKEMPI v2 license.

## Citation

> 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.

## Provenance

Built from the local manifest entry `skempi2` of `manifests/atlas_download_plan.json`.
Pipeline source: `megadata-post normalize --dataset skempi2 --tables-only`.