evo-final

A multi-source protein complex dataset for self-supervised preference training. Every cross-species swap is ortholog-aligned (swap human_A with yeast_A — the orthologous gene's product — not a random yeast protein) and labelled with three sequence-identity metrics + TimeTree species divergence time.

Identity filter (v3.1): L2/L3/L4 are restricted to ortholog pairs whose BLAST-style identity ≥ 0.80 on both chain A and chain B. Below this the "swap" is too divergent to be an informative dispreferred negative for preference training. Unfiltered parquets remain available locally as *_unfiltered.parquet for ablation studies.

Train/val/test splits (v3.3, strict): L3 and L4 carry a split column ∈ {train, val, test}. Pipeline:

1. mmseqs2 easy-cluster every unique chain across L3 (within + cross-source) at 30% min-seq-id, 80% coverage → 14,944 chains collapse to 1,733 clusters.
2. Each cluster is hash-assigned to a split with 60/20/20 target ratios (1,058 / 344 / 331 clusters; seed evo-final-splits-v9, picked by a Phase 27 sweep that scored cross-source val/test balance).
3. Strict assignment: a row keeps a split only if all 4 chains land in clusters of the same split. Rows whose 4 chains span multiple cluster-splits are dropped (~41% of within-source L3, ~16% of cross-source L3). This guarantees chain-level disjointness — no sequence appearing in a train row also appears in a val/test row.
4. L4 inherits the split via swap_complex_id; L4 rows whose parent L3 row was dropped are dropped too.
5. Verified by mmseqs2 easy-search at high sensitivity (-s 7.5): 0 exact-string overlaps train↔val/test; residual borderline-PID hits (mean 35%, 95th 42–46%) reflect mmseqs2 cluster's heuristic boundary, typical of paper-grade homology-reduced splits.

Dropped rows remain available locally as *_pre_split.parquet for users who want them as additional unsplit training data.

Five-layer structure

Layer	config_name	Rows	Purpose
L1	natives	85,554	All native heterodimers, no filter.
L1ʹ	natives-filtered	50,357	Same, Opisthokonta + NCBI-genus-mappable.
L2	swap_relationships	62,310	FK-only pairs of natives sharing an ortholog group. Within-source. (unsplit)
L2	swap_relationships_cross_source	15,261	Same idea, cross-source (NVIDIA × cong-lab via shared STRING role_keys). (unsplit)
L3	swap_complexes	36,673	wjiaqi/evo-style — 4 sequences inline per row + gene/species/identity/mya labels. Within-source. Split: 27,966 / 4,277 / 4,430.
L3	swap_complexes_cross_source	12,776	Cross-source. Split: 9,502 / 417 / 2,857.
L4	preference_pairs (default)	146,692	Train-ready. Per-row (preferred native, dispreferred chimera) with species+gene labels on every chain. Within-source. Split: 111,864 / 17,108 / 17,720.
L4	preference_pairs_cross_source	51,104	Cross-source. Split: 38,008 / 1,668 / 11,428.

from datasets import load_dataset
prefs   = load_dataset("wjiaqi/evo-final")["train"]                                 # L4 within-source (default)
prefs_x = load_dataset("wjiaqi/evo-final", "preference_pairs_cross_source")["train"]
complexes = load_dataset("wjiaqi/evo-final", "swap_complexes")["train"]             # L3 wjiaqi/evo-style

L1, L2, L3, L4 are mathematically equivalent at the information level — L3 is expand(L2) ⨝ natives, L4 is expand(L3, {AB,BA} × {sp1,sp2}) = 4 × L3. Pick whichever layer matches your consumption pattern.

Three sequence-identity metrics on every L2/L3/L4 row

metric	formula	use case
seq_identity_* (primary)	matches / aligned columns (non-gap)	BLAST-style "% identity"; what's in literature; aligned-region conservation
seq_identity_*_min_len	matches / min(len_a, len_b)	core-domain identity; bounded by shorter chain
seq_identity_*_max_len	matches / max(len_a, len_b)	conservative; penalises length mismatch

Computed via BLOSUM62 global alignment (Biopython PairwiseAligner, open gap −11, extend −1). For orthologs with different lengths (e.g. human UQCRH 91 aa vs yeast QCR6 135 aa), the three values differ — BLAST ≈ 44%, max-len ≈ 30%. Standard literature reports the BLAST number.

Distance label B — TimeTree divergence time

divergence_mya = species-pairwise Median Time from TimeTree, molecular-clock calibrated. Examples:

species pair	mya
Mouse ↔ Rat	13
Human ↔ Mouse	87
Human ↔ Zebrafish	429
S. cerevisiae ↔ S. pombe	543
Fly ↔ Worm	727
Human ↔ Yeast	1,275

We rejected NCBI tree-edge distance as a label: it's cladistic, not chronological, and inverts biological ordering (NCBI: human-fly 58 > human-yeast 38; reality: 686 < 1,275 Mya).

For conglab, where lineage only carries genus, we map each genus to its lowest-taxid species descendant in NCBI before querying TimeTree.

L1 sources and species filter

Source	natives	natives-filtered	Provenance
nvda	69,693	34,620	wjiaqi/evo-afcdb-final — NVIDIA AFDB strict (ipSAE_min ≥ 0.6 & N_clash_backbone ≤ 10).
conglab	15,861	15,737	wjiaqi/evo-conglab-final — Cong-lab human-PPI orthologs filtered through Boltz (iptm ≥ 0.7 & complex_plddt ≥ 70). 124 rows dropped from filtered for no-NCBI-genus.

Filter: Opisthokonta (Metazoa + Fungi = clade ≤ human-yeast distance) AND a valid NCBI scientific-name genus so every row maps to a taxid for TimeTree lookup.

Schemas

L3 — swap_complexes (wjiaqi/evo-style; one row = one cross-species ortholog pair)

Column	Notes
swap_complex_id, relationship_id, source, interaction_group_id	identifiers; source ∈ {nvda, conglab, cross}
species_1, taxid_1, organism_acc_1	sp1 organism metadata (organism_acc filled for conglab only)
protein_A_sp1_{uniprot,gene,seq,len}	chain A of native 1
protein_B_sp1_{uniprot,gene,seq,len}	chain B of native 1
species_2, taxid_2, organism_acc_2, protein_A_sp2_*, protein_B_sp2_*	same for native 2
role_key_A, role_key_B	ortholog group ids — STRING OG_<n> for nvda and cross, canonical human UniProt for conglab
seq_identity_A, seq_identity_A_min_len, seq_identity_A_max_len	three identity metrics for chain A
seq_identity_B, seq_identity_B_min_len, seq_identity_B_max_len	three identity metrics for chain B
divergence_mya	TimeTree species divergence in Mya (NaN where TimeTree has no data)
split	train / val / test; assigned by 30%-PID mmseqs2 cluster, 60/20/20 ratio, mixed-cluster rows → train (no leakage)

L4 — preference_pairs (training-ready)

Column	Notes
preference_id, swap_complex_id, relationship_id, source	identifiers + FKs
crossover	'AB' = dispreferred is (sp1.A, sp2.B); 'BA' = (sp2.A, sp1.B)
preferred_native_id, preferred_species, preferred_taxid, preferred_organism_acc	which native is the positive
preferred_A_{uniprot,gene,seq,len}, preferred_B_*	the native heterodimer
dispreferred_A_species, dispreferred_A_taxid, dispreferred_A_organism_acc	species of the A chain of the swap chimera
dispreferred_A_{uniprot,gene,seq,len}	the A chain of the swap
dispreferred_B_*	same for B (different species — that's the swap!)
seq_identity_A, seq_identity_A_min_len, seq_identity_A_max_len, seq_identity_B, …	identity metrics inherited from L3
divergence_mya	TimeTree distance between the two natives' species (inherited from L3)
split	inherited from L3 via swap_complex_id

L2 — swap_relationships (FK-only, lightweight)

Column	Notes
relationship_id, source, swap_kind	identifiers; swap_kind always 'cross_species_ortholog'
native1_id, native2_id	FK → L1
taxid_1, taxid_2, organism_1, organism_2, role_key_A, role_key_B, interaction_group_id	minimal metadata for filtering / joining
seq_identity_A, seq_identity_A_min_len, seq_identity_A_max_len, seq_identity_B, …, divergence_mya	labels (same values as L3/L4)

L1 — natives / natives-filtered

Vocabulary of native heterodimers; identical schema, the _filtered variant just applies the Opisthokonta + NCBI-genus filter.

Column	Notes
native_id, source	nvda-AF-… or conglab-PPI…@organism_acc
taxid, organism, organism_key, lineage, clade, phylum	taxonomy (NaN where unavailable)
A_{uniprot,gene,seq,len}, B_{uniprot,gene,seq,len}	the two chains
nvda_ipSAE_min, nvda_ipTM, nvda_pDockQ, nvda_n_clash, nvda_complex_id	NVIDIA AF-Multimer scores (NaN for conglab)
boltz_iptm, boltz_plddt, boltz_ptm, boltz_conf, ppi_id	conglab Boltz scores (NaN for nvda)

Within-source vs cross-source

Each layer has two variants:

• within-source (no _cross_source suffix): pairs are NVIDIA × NVIDIA (via STRING ortholog groups) or conglab × conglab (via shared ppi_id). Both natives are from the same upstream dataset.
• cross-source (_cross_source): pairs are NVIDIA × conglab. They exist because 337 STRING ortholog signatures appear in BOTH NVIDIA and conglab (= 30% of NVIDIA signatures, 40% of conglab signatures). For these signatures we get to pair an NVIDIA AF-multimer-verified native with a conglab Boltz-verified native of the same conserved interaction.

Keep them separate so consumers can opt in to one, the other, or both.

Construction summary

NVIDIA AFDB strict + conglab Boltz-filtered  → L1 natives
            ↓
STRING orthologs over 30 Opisthokonta species  → role_keys
            ↓
L2 swap_relationships (within-source: nvda×nvda via STRING, conglab×conglab via ppi_id;
                       cross-source: signature overlap)
            ↓
L3 swap_complexes  ( = L2 ⨝ natives,  4 sequences inline per row)
            ↓
L4 preference_pairs ( = expand L3 to 4 rows: {AB, BA} × {sp1 preferred, sp2 preferred} )

Plus per-row:

• BLOSUM62 global alignment → 3 identity metrics
• TimeTree pairwise Median Time → divergence Mya

Code: w-jiaqi/EvoData (scripts 01–20, plus species_config.py).

Citation

• NVIDIA AFDB: Han Y, Tsenkov MI, Venanzi NAE, et al. bioRxiv (2026), doi:10.64898/2026.03.27.714458.
• STRING: Szklarczyk et al., Nucleic Acids Research (2023).
• UniProt: UniProt Consortium, Nucleic Acids Research (2025).
• TimeTree: Kumar et al., Mol Biol Evol (2022).
• Cong-lab Boltz-filtered PPI set (forthcoming).