EvoBind — moderately-filtered same-species PPI orthologs

What this is

For each of the 17,849 high-confidence human PPI predictions in final_predictions_90.tsv (90% expected precision) from Cong Lab humanPPI, this dataset provides:

• the human pair itself (pair_type = human_pair, ortholog_rank = 0), and
• up to 10 same-species ortholog pairs (A_org, B_org) taken from the Cong Lab protein_omicMSAs, sorted by combined sequence identity to the human queries (descending), with pair_type = natural and ortholog_rank = 1..N.

Layout per PPI:

PPI0000001_R00 (A_human, B_human)
PPI0000001_R01 (A_org1, B_org1)   # closest co-evolved ortholog pair
PPI0000001_R02 (A_org2, B_org2)
... up to R10
PPI0000002_R00 (A_human, B_human)
...

Total: 196,269 rows across 17,849 PPIs (17,849 human_pair

• 178,420 natural).

Filters

filter	value
same-genus exclusion	drop natural rows whose genus matches the query genus (Homo); avoids the assembly-artefact pollution from non-reference Homo assemblies
taxonomic class restriction	Mammalia, Aves, Lepidosauria, Amphibia, Actinopteri, Chondrichthyes (vertebrates only)
identity floor	identity_to_human_{a,b} >= 0.50 on both sides
max orthologs per PPI	10 (closest by combined identity)

This is a "moderately loose" filter that keeps the dataset compact (~34 MB) while still providing meaningful taxonomic diversity beyond just primates.

A separate, larger candidate pool (~5.36M rows × 53 columns, no taxonomic restriction, up to 300 orthologs per PPI, identity floor 0.20) is available on the cursor/loose-natural-pairs-aaab branch's pipeline; this curated dataset is the published default.

Schema

column	type	notes
row_id	string	PPI0000123_R02 — globally unique row identifier
ppi_id	string	PPI0000123 — shared by all rows of one PPI
original_protein_a, original_protein_b	string	Human UniProt accessions
gene_name_a, gene_name_b	string	Gene symbols (from Cong predictions)
rf_prob, af_prob, af_prob5, afm_prob	float	Cong-Lab RF2-PPI / AF2 / AF-Multimer interaction probabilities
source_pipeline, pdb_template, conf_dbs, all_dbs, string_score	various	Cong-Lab prediction provenance & evidence
known_score_a/b, pubmed_count_a/b, locality_a/b, disease_a/b, function_a/b	various	Per-protein annotations
organism_acc_a, organism_acc_b	string	NCBI genome / dataset accession (HUMAN_QUERY for the human row); always identical between A and B for natural rows
taxonomy_a, taxonomy_b	string	Genus:Family:Order:Class:Phylum
genus_a/b, family_a/b, order_a/b, class_a/b, phylum_a/b	string	Parsed from taxonomy
sequence_a, sequence_b	string	Native protein sequence (uppercase aligned + lowercase insertions, gaps stripped)
length_a, length_b	int	Sequence lengths
identity_to_human_a, identity_to_human_b	float	Aligned-position identity to the human query in [0, 1]
pair_type	string	human_pair or natural
ortholog_rank	int	0 for the human row; 1..N for surviving orthologs in identity order (after filtering)
ortholog_rank_loose	int	The rank this row had in the larger loose pool (preserved for traceability)
interaction_label	int	Always 1 (positives only)
taxonomic_distance	int	0 = same genus … 5 = different phylum (always 0 for natural since both sides are the same organism)
source	string	conglab_humanppi_omicmsa_loose
split_random	string	Random 90/5/5, stratified by pair_type
split_ppi_disjoint	string	All rows of a PPI go to the same split
split_protein_disjoint	string	Human proteins in test/valid don't appear in train; cross-split rows fall back to train

Class distribution (natural rows)

Mammalia        177,506
Aves                294
Amphibia            214
Actinopteri         195
Lepidosauria        185
Chondrichthyes       26

Splits (90/5/5)

split_random:           train=176,509   valid=9,906   test=9,854
split_ppi_disjoint:     train=176,662   valid=9,812   test=9,795
split_protein_disjoint: train=195,268   valid=  484   test=  517

Usage

from datasets import load_dataset
import itertools

ds = load_dataset("wjiaqi/evobind", split="train")

# Iterate one PPI at a time — rows are pre-sorted by ppi_id, ortholog_rank
for ppi_id, group in itertools.groupby(ds, key=lambda r: r["ppi_id"]):
    rows = list(group)
    human, *orthologs = rows
    # ortholog_rank == 0 is the human pair; 1..N are same-species orthologs

Provenance & licensing

• Built from the Cong Lab humanPPI dataset (final_predictions_90.tsv + protein_omicMSAs).
• Source CC-BY-4.0 — see LICENSE.

Known limitations

• Only positive interactions; no negatives.
• No cross-species swap rows (will be added once a structure-aware quality filter is in place).
• Sequences come from each ortholog's MSA-aligned sequence with gaps stripped; this means the resulting "native" sequence may be missing residues where the MSA had gaps relative to human. Per-row completeness signals are not stored as columns yet.
• ortholog_rank_loose references the original ranking before filtering; gaps in this sequence indicate intermediate ranks that were dropped by the moderate filters.