pepcluster 0.1.0

BLOSUM62-aware anchor-residue clustering for immunopeptides, with a Rust backend

PepCluster

BLOSUM62-aware anchor-residue clustering for immunopeptides — with a fast Rust backend.

PepCluster groups peptides by the similarity of their MHC-I anchor residues (the first 3 + last 3 amino acids) using a BLOSUM62-normalized similarity metric with double weight on the primary anchor positions P2 and PΩ. It is built for large immunopeptidomics datasets: a Rust extension does the heavy lifting (10–100× faster than pure Python), and a pure-Python fallback keeps it working everywhere.

Unlike general-purpose sequence tools (e.g. MMseqs2), PepCluster distinguishes anchor from non-anchor positions, which is what actually drives MHC-I binding specificity — producing biologically interpretable clusters on short (8–14 aa) peptides where full-length estimators break down.

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Install

pip install pepcluster

Prebuilt wheels are published for Linux, macOS, and Windows, so no Rust toolchain is required for end users. If you install on a platform without a wheel, pip builds from source (needs a Rust compiler — see Building from source).

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Quick start

Command line

pepcluster -i examples/peptides.fasta -o out -t 0.6

This writes cluster assignments and per-cluster FASTA files under out/ (see Output files).

Python

import pepcluster

# End-to-end: FASTA in → TSV + per-cluster FASTA out
stats = pepcluster.cluster_fasta("peptides.fasta", "out", threshold=0.6)
print(stats["n_clusters"], "clusters")

# Low-level: cluster a dict of unique 6-mer anchors → frequency
mapping, n_cmp, n_early = pepcluster.cluster_anchors(
    {"YLLAGV": 3, "YMLAGV": 1, "GYAWTK": 2}, 0.6)
# mapping: {anchor -> representative anchor}

# Optional Lloyd-style refinement on top of the greedy result
refined, refine_stats = pepcluster.refine_clusters(
    {"YLLAGV": 3, "YMLAGV": 1}, mapping, 0.6, iterations=3)

pepcluster.HAS_RUST tells you whether the compiled backend is active (cluster_anchors / refine_clusters automatically use Rust when available and fall back to identical pure-Python implementations otherwise).

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CLI options

Flag	Default	Description
-i, --input	required	Input FASTA file
-o, --outdir	anchor_clusters	Output directory
-t, --threshold	0.6	BLOSUM similarity threshold (0.0–1.0)
--min-cluster-size	2	Min members for a per-cluster FASTA
--n-front	3	N-terminal anchor length
--n-back	3	C-terminal anchor length
--refinement	off	Apply Lloyd-style refinement after greedy clustering
--iterations	3	Max refinement passes (with --refinement)
--backend	auto	auto | rust | python
-q, --quiet	—	Suppress progress output

Threshold guide:

Value	Effect
0.8	Strict — mostly exact matches + very conservative substitutions
0.6	Moderate — allows 1–2 conservative substitutions (recommended)
0.4	Relaxed — broader groups for exploratory analysis

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Output files

out/
├── clusters.tsv            # cluster_id, representative_anchor, header, sequence, anchor (every peptide)
├── cluster_summary.tsv     # cluster_id, representative_anchor, size (sorted by size)
├── summary.txt             # run statistics
└── fasta/
    ├── cluster_0.fasta     # per-cluster FASTA, ready for MSA (>= --min-cluster-size members)
    ├── cluster_1.fasta
    └── SHORT_peptides.fasta # peptides too short to form an anchor (if any)

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How it works

1. Anchor extraction. Each peptide is reduced to its 6-residue anchor: the first --n-front (3) and last --n-back (3) amino acids. Peptides shorter than that are set aside in SHORT_peptides.fasta.
2. Deduplicate. Peptides are grouped by their exact anchor, so clustering operates on unique anchors weighted by frequency.
3. Similarity metric. Two anchors are compared position-by-position with a BLOSUM62 score normalized to sim(a,b) = B(a,b) / sqrt(B(a,a)·B(b,b)). Positions P2 and PΩ carry 2× weight (the primary MHC-I anchors); the score is a weighted mean in [−…, 1].
4. Blocking. Unique anchors are bucketed by a reduced 10-letter alphabet at P2 and PΩ (10×10 = 100 bins), so only plausibly-similar anchors are ever compared. High-weight positions are checked first with early termination.
5. Greedy clustering. Within each block, anchors are processed most-frequent-first; each joins the first centroid above threshold or becomes a new centroid.
6. Optional refinement (--refinement). A Lloyd-style pass iterates: medoid update → cross-block reassignment → centroid merging, until stable.

The Rust backend (pepcluster._core) and the pure-Python reference (pepcluster.clustering) implement identical logic and produce identical cluster assignments; the test suite asserts this parity.

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Performance

Dataset	Python	Rust
7K peptides	<1 s	<1 s
2.5M peptides	~3 min	~15 s

Speed comes from anchor deduplication, coarse-alphabet blocking, and weighted early-termination in the similarity check.

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Building from source

Requires a Rust toolchain and maturin.

# one-time
pip install maturin

# build + install into the current environment (editable-ish)
maturin develop --release

# or build a wheel
maturin build --release      # wheel lands in target/wheels/

The project uses maturin's mixed layout: Rust lives in src/lib.rs (compiled to pepcluster._core), Python in python/pepcluster/.

Run the tests with:

pip install pytest
pytest

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Releasing (maintainers)

Wheels are built for Linux / macOS / Windows by .github/workflows/CI.yml and published to PyPI on version tags via PyPI Trusted Publishing (OpenID Connect — no API token or stored secret).

1. One-time: on https://pypi.org/manage/account/publishing/ add a pending publisher — Owner AmirAsgary, Repository PepCluster, Workflow CI.yml (leave the environment blank).

2. Bump the version in pyproject.toml and Cargo.toml.

3. Tag and push:

   git tag v0.1.0
   git push origin v0.1.0
   

The release job then builds all wheels + an sdist and uploads them to PyPI.

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License

MIT © 2026 Amir Asgary

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Citation

If you use PepCluster in your research, please cite this repository:

Asgary, A. PepCluster: BLOSUM62-aware anchor-residue clustering for immunopeptides. https://github.com/AmirAsgary/PepCluster