README.md

---
dataset_info:
  features:
  - name: A
    dtype: string
  - name: B
    dtype: string
  - name: SeqA
    dtype: string
  - name: SeqB
    dtype: string
  - name: OrgA
    dtype: string
  - name: OrgB
    dtype: string
  - name: NameA
    dtype: string
  - name: NameB
    dtype: string
  - name: GeneA
    dtype: string
  - name: GeneB
    dtype: string
  - name: labels
    dtype: int8
  - name: source_dataset
    dtype: string
  - name: evidence
    dtype: string
  - name: y2h_v1_result
    dtype: string
  - name: y2h_v4_result
    dtype: string
  - name: mappit_result
    dtype: string
  - name: gpca_result
    dtype: string
  - name: score
    dtype: float32
  - name: contact_probability
    dtype: float32
  - name: model_confidence
    dtype: float32
  - name: pDockQ
    dtype: float32
  - name: iPAE
    dtype: float32
  - name: CCC
    dtype: float32
  - name: novel
    dtype: int8
  splits:
  - name: afrf_y2h_yeast
    num_bytes: 542861
    num_examples: 597
  - name: afrf_y2h_human
    num_bytes: 3953209
    num_examples: 4046
  - name: yeri
    num_bytes: 1810228
    num_examples: 1970
  - name: y2h_union_25
    num_bytes: 4625430
    num_examples: 4556
  - name: valbin_25
    num_bytes: 15517114
    num_examples: 12706
  - name: prs_rrs_yeast
    num_bytes: 335754
    num_examples: 306
  - name: prs_rrs_human
    num_bytes: 787687
    num_examples: 729
  - name: orthogonal_assays
    num_bytes: 9027406
    num_examples: 8846
  download_size: 15030614
  dataset_size: 36599689
configs:
- config_name: default
  data_files:
  - split: afrf_y2h_yeast
    path: data/afrf_y2h_yeast-*
  - split: afrf_y2h_human
    path: data/afrf_y2h_human-*
  - split: yeri
    path: data/yeri-*
  - split: y2h_union_25
    path: data/y2h_union_25-*
  - split: valbin_25
    path: data/valbin_25-*
  - split: prs_rrs_yeast
    path: data/prs_rrs_yeast-*
  - split: prs_rrs_human
    path: data/prs_rrs_human-*
  - split: orthogonal_assays
    path: data/orthogonal_assays-*
license: cc-by-4.0
language:
- en
tags:
- protein
- protein-protein-interaction
- interactome
- biology
- alphafold
pretty_name: CCSB AI Interactome Benchmark
---

# CCSB-AI-Interactome

Harmonized protein-protein interaction benchmark built from the Supplementary Data of:

> Lambourne, L., Yadav, A., Wang, Y. et al. **Experimental assessment of AI-based interactome mapping.** *Nature Communications* **17**, 4894 (2026). https://doi.org/10.1038/s41467-026-70942-x

The study screened a near-complete *S. cerevisiae* ORFeome (5,854 sequence-validated ORFs, 99.5% coverage) with an improved yeast two-hybrid assay to produce YeRI, then put proteome-scale AlphaFold/RoseTTAFold predictions through the same wet-lab pipeline. Its headline finding is that high-confidence AI predictions match experimental data in quality but recover far fewer strictly novel interactions in proteome-wide screening.

Every table has been resolved to amino acid sequences and coerced to a single schema, so all splits share identical columns and can be iterated without special-casing.

## Splits

| Split | Rows | Positive | Negative | Unscorable | Organism | What it is |
|---|---|---|---|---|---|---|
| `afrf_y2h_yeast` | 597 | 17 | 559 | 21 | yeast | AlphaFold/RoseTTAFold-predicted pairs, each given a wet-lab Y2H verdict (Supp. Data 19) |
| `afrf_y2h_human` | 4,046 | 402 | 2,916 | 728 | human | Zhang et al. AF/RF human predictions, each given a Y2H verdict (Supp. Data 22) |
| `yeri` | 1,970 | 1,970 | 0 | 0 | yeast | The yeast reference interactome, with AlphaFold metrics and orthogonal assays joined (Supp. Data 16 + 24) |
| `y2h_union_25` | 4,556 | 4,556 | 0 | 0 | yeast | Union of four systematic yeast Y2H maps (Supp. Data 17) |
| `valbin_25` | 12,706 | 12,706 | 0 | 0 | yeast | Validated binary yeast PPIs across structural, literature, and Y2H evidence (Supp. Data 21) |
| `prs_rrs_yeast` | 306 | 108 | 198 | 0 | yeast | scPRS-v2 positive and scRRS-v2 random reference sets, with every assay readout attached (Supp. Data 2, 3, 4, 23) |
| `prs_rrs_human` | 729 | 342 | 387 | 0 | human | hsPRS-v2 / hsRRS-v2 plus the literature and random comparators tested alongside (Supp. Data 22) |
| `orthogonal_assays` | 8,846 | 858 | 6,484 | 1,504 | yeast | Raw MAPPIT and GPCA assay-level results, one row per assayed configuration (Supp. Data 11) |

## The interesting one

`afrf_y2h_yeast` and `afrf_y2h_human` are pairs that a structure-based AI method called an interaction, each carrying an experimental verdict. Every negative is a structural near-miss rather than a random pair, which makes them a far harder discrimination target than the usual random-negative benchmarks, and a direct test of whether a sequence-only model recovers signal that structure-based virtual screening got wrong.

## Schema

All splits share these columns.

| Column | Type | Meaning |
|---|---|---|
| `A`, `B` | string | UniProt accession |
| `SeqA`, `SeqB` | string | Amino acid sequence |
| `OrgA`, `OrgB` | string | Organism binomial |
| `NameA`, `NameB` | string | Systematic ORF name (yeast) or source accession (human) |
| `GeneA`, `GeneB` | string | Common gene name |
| `labels` | int8 | `1` interacting, `0` non-interacting, `-1` not scorable |
| `source_dataset` | string | Provenance set within the paper |
| `evidence` | string | `;`-joined evidence flags, for example `I3D-exp-24;Lit-BM-24` |
| `y2h_v1_result`, `y2h_v4_result` | string | Raw Y2H verdict, empty when not tested |
| `mappit_result`, `gpca_result` | string | Orthogonal assay verdict, empty when not tested |
| `score` | float32 | Assay score, or YeRI manual growth score |
| `contact_probability`, `model_confidence`, `pDockQ`, `iPAE`, `CCC` | float32 | AlphaFold confidence metrics, `NaN` when unavailable |
| `novel` | int8 | `1` if strictly novel at publication, `0` if not, `-1` unknown |

### Label semantics differ by split

This is the one thing to read before using `labels`.

- `afrf_y2h_yeast`, `afrf_y2h_human`, `orthogonal_assays`: the label is the **assay outcome**. This is the adjudication being benchmarked.
- `prs_rrs_yeast`, `prs_rrs_human`: the label is **reference-set membership** (PRS positive, RRS random). The assay outcome is in the result columns instead, since reference-set membership is the ground truth and the assay is what is being calibrated.
- `yeri`, `y2h_union_25`, `valbin_25`: positive-only interaction lists, so every label is `1`. Supply your own negatives.

`labels = -1` marks rows that cannot be scored: autoactivators, failed tests, and clones that failed sequence confirmation. They are retained rather than dropped so nothing is silently lost. Filter with `ds.filter(lambda x: x["labels"] >= 0)`.

## Usage

```python
from datasets import load_dataset

# The AI-adjudicated benchmark
ds = load_dataset("GleghornLab/CCSB-AI-Interactome", split="afrf_y2h_human")
ds = ds.filter(lambda x: x["labels"] >= 0)   # 3,318 scorable pairs

# The yeast reference interactome
yeri = load_dataset("GleghornLab/CCSB-AI-Interactome", split="yeri")
```

## Caveats

- **Y2H negatives are not verified non-interactions.** The assay recovers roughly 20 to 30% of scPRS-v2 positives, so a `Negative` means "not detected in this assay" and not "these proteins do not interact." Treat `afrf_y2h_*` as a Y2H-verifiability benchmark, not a ground-truth interaction benchmark.
- **Yeast is well represented in common PPI training corpora** (STRING, BioGRID). Any evaluation on the yeast splits needs a homology or cluster-level leakage control against the model's training set. The `novel` column on `yeri` marks the 1,446 rows that were strictly novel at publication and are the least likely to be memorized.
- **`yeri` and `prs_rrs_human` contain both-orientation rows.** `yeri` has 1,970 rows over 1,910 unique unordered pairs, matching the paper's reported 1,910 PPIs; `prs_rrs_human` has 729 rows over 590 unique pairs. Deduplicate on a sorted `(NameA, NameB)` key if you need one row per pair.
- **`mappit_result` and `gpca_result` are a pair-level collapse** of the assay-level table, scoring a pair positive if any tested configuration was positive. This does not exactly reproduce the per-assay counts in the paper's Figure 2a, which applies additional quality control that is not recoverable from the released tables. Use the `orthogonal_assays` split to recompute under a different rule.
- **10 rows lack sequences.** Four human accessions referenced by the source tables are now inactive in UniProt (`A0A0C4DGZ8`, `O43930` and `Q8WV35` deleted, `P01562` demerged). Their rows are retained with empty `SeqA` / `SeqB`.

## Provenance

Sequences were resolved from UniProt reference proteomes `UP000002311` (*S. cerevisiae* S288C) and `UP000005640` (*H. sapiens*), with per-identifier lookups for entries outside those proteomes. Yeast ORF names were matched on the UniProt ordered locus name field.

## License and citation

Source Supplementary Data are distributed under CC BY 4.0 as part of the open access article, and this derived dataset carries the same license. Cite the original paper:

```bibtex
@article{lambourne2026interactome,
  title   = {Experimental assessment of AI-based interactome mapping},
  author  = {Lambourne, Luke and Yadav, Anupama and Wang, Yang and others},
  journal = {Nature Communications},
  volume  = {17},
  number  = {1},
  pages   = {4894},
  year    = {2026},
  doi     = {10.1038/s41467-026-70942-x}
}
```

The yeast interaction maps are also browsable at the [Yeast Interactome Portal](https://yeast.interactome-atlas.org/).