pepcluster 0.1.6

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
--anchors	"2;3"	Which positions are binding anchors, as FRONT;BACK (1-based per side). See Choosing the anchors
--anchor-weight	2.0	Weight given to anchor positions (all others are 1.0)
--obg-block-search	off	Also compare each anchor against centroids in neighbouring blocks whose upper bound reaches the threshold (fewer, tighter clusters)
--obg-max-probes	0	With --obg-block-search, max blocks searched per anchor incl. own (0 = all)
--obg-min-block-upper-bound	0.0	With --obg-block-search, only search blocks with upper bound ≥ this (effective cut max(threshold, this))
--refinement	off	Apply Lloyd-style refinement after greedy clustering
--iterations	3	Max refinement passes (with --refinement)
--refine-cap	32	Max centroid comparisons per anchor in refinement reassignment (<=0 = no cap). Lower = faster
--no-merge	off	Skip the refinement centroid-merge step (much faster on many-cluster data)
--fast-medoid	off	O(N) medoid instead of exact O(k²) (much faster when a few clusters are huge)
--merge-cap	0	Cap candidate centroids per cluster in the merge step (0 = no cap; e.g. 32 on many-cluster data)
--central-region-profiling	off	Merge clusters by a combined anchor + central-region-k-mer score (see Central-region profiling)
--crr-kmer-size	2	Central-region k-mer length
--crr-bins	3	Number of relative-position bins for central k-mers
--crr-adjacent-bin-smoothing	0.5	Adjacent-bin smoothing weight α
--no-cluster-profile-merge	off	With profiling on, keep the anchor-only merge
--cluster-profile-merge-weight	0.2	Weight of the central-profile term in the merge score
--cluster-profile-merge-threshold	0.6	Combined-score threshold to merge
--threads	1	Worker threads for the Rust backend (1 = serial, 0 = all cores, N = exactly N). Results identical regardless
--backend	auto	auto | rust | python
-q, --quiet	—	Suppress progress output

Making refinement fast

Refinement (--refinement) has three sub-steps, and two of them are quadratic by default — which is fine at moderate thresholds but blows up at the extremes:

Sub-step	Cost (default)	Blows up when…	Fast flag
medoid update	O(k²) per cluster	a few clusters are huge (low threshold)	--fast-medoid → O(N)
reassignment	O(cap) per anchor	(already capped)	--refine-cap
merge	O(clusters × neighbours)	there are many clusters (high threshold)	--merge-cap N (or --no-merge)

• --refine-cap N bounds candidate centroids per anchor in reassignment (own-block-first, largest-cluster-first). Default 32 is near-lossless.
• --fast-medoid replaces the exact all-pairs medoid with an O(N) per-position decomposition. On a single cluster of 30k anchors it is ~470× faster (and the gap grows quadratically with cluster size).
• --merge-cap N bounds candidate centroids per cluster in the merge step (~130× faster with ~1M clusters). --no-merge skips merging entirely.

For a huge dataset, turn all three on so every sub-step is bounded:

pepcluster -i peptides.fasta -o out -t 0.6 --refinement \
    --fast-medoid --refine-cap 32 --merge-cap 32

Defaults are unchanged (exact medoid, uncapped merge) for backward compatibility — the fast paths are opt-in.

Multithreading

--threads N runs the greedy clustering and the refinement medoid / reassignment steps in parallel (Rust backend). 1 = serial (default), 0 = all cores, N = exactly N. Results are bit-identical regardless of thread count — each unit writes its own slot and counts are integer sums.

pepcluster -i peptides.fasta -o out -t 0.8 --refinement --threads 0

On 24 cores the greedy is ~4–9× faster (most at strict thresholds, where it does the most work); the merge step stays serial, and the OBG greedy stays serial (blocks aren't independent there). The Python backend is always serial. Since the tool is otherwise single-threaded, -c 2 on SLURM was enough before — raise it to match --threads.

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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Choosing the anchors

The anchor is the peptide's first --n-front + last --n-back residues. --anchors picks which positions inside that anchor are the binding anchors: they get --anchor-weight (default 2×) in the similarity score and they define the coarse-alphabet blocking.

The format is "FRONT;BACK", where each side is a comma-separated list of 1-based indices into that side's residues. Either side may be empty.

The default "2;3" means the 2nd of the first 3 residues (P2) and the 3rd of the last 3 (PΩ) — the classic MHC-I anchors.

--anchors	Anchor positions (1-based, in the 6-mer)	Meaning
"2;3" (default)	2, 6	P2 + PΩ — MHC-I
"2;2,3"	2, 5, 6	P2 plus the last two C-terminal residues
"1,2;3"	1, 2, 6	first two N-terminal residues plus PΩ
";3"	6	C-terminal anchor only
"2;"	2	N-terminal anchor only

# emphasise P2 and the last two residues, and weight anchors 3x
pepcluster -i peptides.fasta -o out --anchors "2;2,3" --anchor-weight 3

Anchor positions are indexed relative to --n-front / --n-back, so the two work together — e.g. a 2+2 anchor with anchors at the 2nd of each side:

pepcluster -i peptides.fasta -o out --n-front 2 --n-back 2 --anchors "2;2"

Up to 8 anchor positions are supported (they form the blocking key).

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Tighter clusters: upper-bound-guided block search

For speed, the greedy pass only compares anchors that share a coarse-alphabet block at the anchor positions. This can over-split: two anchors that are genuinely similar overall but whose anchor residues fall in different coarse groups never get compared, so they land in separate clusters.

--obg-block-search addresses this. For each anchor it also searches neighbouring blocks, but only those whose upper bound on achievable similarity reaches the threshold — computed from the best possible residue match per block, so a block that cannot contain a match is provably skipped (a real match is never dropped). Eligible blocks are searched highest-bound-first.

pepcluster -i peptides.fasta -o out -t 0.5 --obg-block-search
# cap the work (search at most 4 blocks per anchor):
pepcluster -i peptides.fasta -o out -t 0.5 --obg-block-search --obg-max-probes 4

It yields fewer, tighter clusters (e.g. ~7% fewer at t=0.5 on random data) at extra cost; --obg-max-probes and --obg-min-block-upper-bound bound that cost. Off by default, so standard runs are unchanged.

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Central-region profiling

Anchor clustering ignores the peptide's middle. Two clusters can have similar anchors but very different central regions (or vice-versa). With --central-region-profiling (requires --refinement), each cluster gets a central-region k-mer profile, and the refinement merge step uses a combined score instead of anchor similarity alone:

S_merge = (1 − w)·S_anchor(centroid₁, centroid₂) + w·S_kmer(profile₁, profile₂)

so clusters merge only when their anchors and their middle regions agree. S_kmer is a weighted Jaccard over position-binned k-mer profiles, smoothed across adjacent bins. Everything is deterministic (integer bin assignment, raw integer counts, sorted-feature sums).

pepcluster -i peptides.fasta -o out -t 0.8 --refinement \
    --central-region-profiling --cluster-profile-merge-weight 0.2

Knobs: --crr-kmer-size (2), --crr-bins (3), --crr-adjacent-bin-smoothing (0.5), --cluster-profile-merge-weight (0.2), --cluster-profile-merge-threshold (0.6); --no-cluster-profile-merge keeps the anchor-only merge. Off by default. This step runs in Python and scales with the number of clusters, so on very large, many-cluster data pair it with --merge-cap.

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Output files

out/
├── clusters.tsv            # cluster_id, representative_anchor, representative_peptide, header, sequence, anchor (every peptide)
├── cluster_summary.tsv     # cluster_id, representative_anchor, representative_peptide, 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)

representative_peptide is the cluster's central member — the peptide with the least average distance (highest weighted average similarity) to every other peptide in the cluster. It is computed in linear time and is always a real member sequence, so it's a good label or seed for each cluster. representative_anchor is the anchor that originally seeded the cluster.

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How it works

1. Anchor extraction. Each peptide is reduced to its 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)). The anchor positions (--anchors, default P2 and PΩ) carry --anchor-weight (default 2×); every other position carries 1×. The score is a weighted mean in [−…, 1].
4. Blocking. Unique anchors are bucketed by a reduced 10-letter alphabet at the anchor positions (10ᵏ bins for k anchors — 100 by default), 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 — both compute in f64 with identical, deterministic orderings — and produce bit-identical cluster assignments; the test suite asserts this parity across anchor lengths, positions and weights.

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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