| # 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): `2026-10-07` |
| - 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,organism_id,reviewed` |
| - InterPro entry type: `Repeat` |
| - Consider entries observed in at least 5 proteins. |
| - After splitting, retain entries with at least 20 |
| positives in training, 3 in validation, |
| and 3 in test. Because validation and test |
| support select the entries, scores are conditional on that label selection. |
| - `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 are dropped, both before splitting and |
| again after split-support filtering, so no row has all-zero targets. |
| - `id` is the UniProtKB accession of the first entry carrying each unique sequence. |
| |
| ## Splits |
|
|
| Whole MMseqs2 clusters are assigned to splits targeting |
| `{'train': 0.8, 'validation': 0.1, 'test': 0.1}`. Seed: `1957723`. Clustering settings: |
|
|
| - MMseqs workflow: `easy-cluster` |
| - Minimum sequence identity (`--min-seq-id`): `0.3` |
| - Minimum coverage (`-c`): `0.8` |
| - Coverage mode (`--cov-mode`): `1` |
| - Threads: `1` |
| - Other MMseqs2 options use MMseqs2 defaults. |
|
|
| When `create_split_subsets` is enabled, pooled random, stratified (by each |
| protein's globally rarest positive entry), 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`: 116 rows |
| - `test_cluster`: 116 rows |
| - `test_random`: 116 rows |
| - `test_stratified`: 116 rows |
| - `train`: 850 rows |
| - `train_cluster`: 850 rows |
| - `train_random`: 850 rows |
| - `train_stratified`: 850 rows |
| - `validation`: 118 rows |
| - `validation_cluster`: 118 rows |
| - `validation_random`: 118 rows |
| - `validation_stratified`: 118 rows |
|
|
| Vocabulary size: 10. |
|
|
| ## Dataset statistics |
|
|
| [`stats.json`](stats.json) at the dataset root contains row counts by split, |
| columns, and SeqKit sequence-length metrics. |
|
|
