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

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:

@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.