---
license: cc-by-4.0
task_categories:
  - other
tags:
  - biology
  - proteins
  - protein-protein-interaction
  - coevolution
pretty_name: EvoBind moderately-filtered same-species PPI orthologs
size_categories:
  - 100K<n<1M
---

# 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](https://conglab.swmed.edu/humanPPI/humanPPI_download.html),
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`](https://github.com/w-jiaqi/EvoBind/tree/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

```python
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](https://conglab.swmed.edu/humanPPI/LICENSE.txt).

## 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.