| # interpro_repeat |
| |
| Protein-level multi-label dataset sourced from InterPro entry metadata and |
| UniProtKB protein-to-InterPro cross-references. |
| |
| ## Intended use |
| |
| Protein repeat annotation prediction from sequence. This evaluates |
| recovery of curated InterPro classifications, not an experimental assay result. |
| |
| ## Source and labels |
| |
| - InterPro metadata: `https://ftp.ebi.ac.uk/pub/databases/interpro/current_release/entry.list` |
| - UniProt REST API: `https://rest.uniprot.org/uniprotkb/stream` |
| - Card generated (UTC): `2026-09-28` |
| - Organism Taxonomy ID: `9606` (`all` = all organisms). |
| - Review status: `reviewed` (`reviewed` = Swiss-Prot, `unreviewed` = TrEMBL, `all` = both). |
| - UniProt query: `(organism_id:9606) AND (reviewed:true)` |
| - UniProt TSV fields: `accession,sequence,xref_interpro` |
| - InterPro entry type: `Repeat` |
| - Keep entries observed in at least 2 proteins and |
| present in the training split. |
| - `targets` is a multi-hot vector ordered by InterPro accession. Accession, |
| display name, and type are recorded in `label_vocabulary.json`. |
| - Proteins without a retained entry before splitting are dropped; after |
| training-only vocabulary filtering, other splits may contain all-zero targets. |
| |
| ## Splits |
|
|
| Whole MMseqs2 `easy-linclust` clusters are assigned to splits targeting |
| `{'train': 0.8, 'validation': 0.1, 'test': 0.1}`, with minimum identity |
| `0.3`, minimum coverage |
| `0.8`, and |
| `1` thread(s). Seed: `1957723`. |
| When `create_split_subsets` is enabled, pooled random, stratified, and |
| hold-cluster-out subsets are also included. Hold-cluster-out subsets require |
| enough MMseqs clusters to populate all three roles. |
| The optional maximum sequence length is `None` and is |
| applied before vocabulary construction and MMseqs2 clustering. |
|
|
| ## Split sizes |
|
|
| - `test`: 1473 rows |
| - `test_cluster`: 1473 rows |
| - `test_random`: 1473 rows |
| - `test_stratified`: 1473 rows |
| - `train`: 11785 rows |
| - `train_cluster`: 11785 rows |
| - `train_random`: 11785 rows |
| - `train_stratified`: 11785 rows |
| - `validation`: 1473 rows |
| - `validation_cluster`: 1473 rows |
| - `validation_random`: 1473 rows |
| - `validation_stratified`: 1473 rows |
|
|
| Vocabulary size: 159. |
|
|
| ## Dataset statistics |
|
|
| [`stats.json`](stats.json) at the dataset root contains row counts by split, |
| columns, and SeqKit sequence-length metrics. |
|
|
