README.md

metadata

license: mit
tags:
  - enzyme-classification
  - ec-number

Leak CURBER enzyme classification — homology kNN

Predicts one EC number per protein sequence by transferring labels from the most similar training enzymes.

Method

1. Prefilter: TF-IDF-weighted 5-mer presence vectors; cosine similarity against all 186k unique training sequences, top 1,000 candidates per query (sparse matrix product).
2. Re-rank: Smith-Waterman local alignment (BLOSUM62, gap open 11, extend 1) of the query against each candidate with SIMD Opal (pyopal).
3. Vote: ECs of all hits scoring within 10% of the best hit, weighted by alignment score (multi-EC hits split their weight).

Falls back to the k-mer nearest neighbour if the 480 s time budget is reached, and to the most frequent EC if a query has no candidates.

Local validation

Validation sequences have no exact matches in train and very low k-mer overlap (median top-1 cosine 0.05), so a random split is misleading (median 0.65). The local hold-out is 4,915 training sequences drawn from the low-similarity pool, scored against the remaining 181k and importance-weighted to match the validation similarity distribution (effective sample size ~2,300).

Metric	Weighted hold-out
L1	0.943
L2	0.912
L3	0.899
L4	0.746

Runtime

11,367 validation inputs: 233 s, 4.0 GB peak RAM on 2 vCPU.

Files

• predict.py — inference entry point
• kmer.py — sparse k-mer index
• train.py — builds weights/db.parquet from train.parquet
• inputs.py, task.json, ec_vocabulary.json — from the starter
• weights/db.parquet — unique training sequences with their ECs