README.md

---
configs:
- config_name: preference_pairs
  data_files:
  - split: train
    path: data/preference_pairs/train.parquet
  - split: validation
    path: data/preference_pairs/validation.parquet
  - split: test
    path: data/preference_pairs/test.parquet
  default: true
- config_name: swap_complexes
  data_files:
  - split: train
    path: data/swap_complexes/train.parquet
  - split: validation
    path: data/swap_complexes/validation.parquet
  - split: test
    path: data/swap_complexes/test.parquet
- config_name: swap_relationships
  data_files: data/swap_relationships.parquet
- config_name: natives
  data_files: data/natives.parquet
- config_name: natives-filtered
  data_files: data/natives-filtered.parquet
license: cc-by-4.0
task_categories:
- other
tags:
- protein
- complex
- protein-protein-interaction
- self-supervised
- preference
- swap
- ortholog
- alphafold
size_categories:
- 100K<n<1M
---
# 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.
>
> **Same-species & no-data filter (v3.4):** Rows where both interacting
> partners come from the same organism (`taxid_1 == taxid_2`) are dropped —
> they were leaking through from AFCDB genus-only labels (e.g. `"Homo"` vs
> `"Homo sapiens"`, both taxid 9606) and don't represent a real cross-species
> swap. Rows whose species pair has no TimeTree calibration (e.g. *Alouatta*
> vs other primates) are also dropped. As a result, every shipped row has
> `divergence_mya` non-null and strictly positive (0.29 – 1530 Mya).
>
> **Unified configs (v3.7):** All layers (`swap_relationships`,
> `swap_complexes`, `preference_pairs`) bundle within-source AND
> cross-source rows into single configs. Filter by the `source` column
> ∈ {`nvda`, `conglab`, `cross`} on any layer. The `*_cross_source`
> configs from earlier versions are retired.
>
> **Train/val/test splits (v3.6 — rescued):** L3 and L4 carry a `split`
> column ∈ {`train`, `validation`, `test`}. Pipeline:
> 1. mmseqs2 `easy-cluster` every unique chain across L3 (within +
>    cross-source) at **30% min-seq-id, 80% coverage** → 14,814 chains
>    collapse to 1,716 clusters.
> 2. **Cluster co-occurrence merge:** if any L3 row has chains in two
>    different clusters, union those clusters. The 1,716 clusters collapse
>    to 545 connected components (largest = ~21% of rows).
> 3. **Greedy 80/10/10 bin-pack by component size:** largest-first, place
>    each component into the split with the most remaining capacity.
>    Lands exactly at 80.00% / 10.00% / 10.00%.
> 4. Every L3 row's 4 chains live in a single component → single split.
>    **No rows dropped** (was ~25% drop in v3.5). L4 inherits the parent
>    L3 row's split via `swap_complex_id`.
> 5. **Leak-free guarantee:** every chain belongs to exactly one component,
>    every component belongs to exactly one split, so no chain appears in
>    multiple splits. Verified by `mmseqs2 easy-search` at -s 7.5:
>    0 exact-string overlaps train↔val/test; residual borderline-PID hits
>    (mean ~33%, 95th 39–44%) reflect mmseqs2 cluster's heuristic boundary,
>    typical of paper-grade homology-reduced splits.
>
> The combined swap_complexes row split is exactly **80/10/10**
> (58,008 / 7,251 / 7,251). Earlier *strict-drop* variants are reproducible
> via `Phase 23` + a chosen seed; the rescue path is `Phase 36`.

## 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` | 77,571 | FK-only pairs of natives sharing an ortholog group. Combined within + cross-source (filter via the `source` column). *(unsplit)* |
| L3 | `swap_complexes` | 72,510 | **wjiaqi/evo-style** — 4 sequences inline per row + gene/species/identity/mya labels. Combined within + cross-source. Split: 58,008 / 7,251 / 7,251 (exact 80/10/10). |
| **L4** | `preference_pairs` *(default)* | **290,040** | **Train-ready.** Per-row (preferred native, dispreferred chimera) with species+gene labels on every chain. Combined within + cross-source. Split: 232,032 / 29,004 / 29,004 (exact 80/10/10). |

```python
from datasets import load_dataset
prefs     = load_dataset("wjiaqi/evo-final")["train"]                       # L4, combined (default)
complexes = load_dataset("wjiaqi/evo-final", "swap_complexes")["train"]     # L3, combined
relations = load_dataset("wjiaqi/evo-final", "swap_relationships")["train"] # L2, combined
# Filter by source via the `source` column ∈ {"nvda", "conglab", "cross"} on any layer.
```

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](http://timetree.org), 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](https://huggingface.co/datasets/wjiaqi/evo-afcdb-final) — NVIDIA AFDB strict (ipSAE_min ≥ 0.6 & N_clash_backbone ≤ 10). |
| `conglab` | 15,861 | 15,737 | [wjiaqi/evo-conglab-final](https://huggingface.co/datasets/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](https://github.com/w-jiaqi/EvoData/tree/cursor/build-nvidia-afcdb-dataset-4a41) (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).