mhcseqs 1.1.0

MHC sequence curation and binding groove extraction

mhcseqs

Self-contained pipeline for downloading, curating, and extracting binding grooves from MHC (Major Histocompatibility Complex) protein sequences.

Install

pip install mhcseqs

For development:

git clone https://github.com/openvax/mhcseqs.git
cd mhcseqs
./develop.sh          # uv pip install -e ".[dev]"
./test.sh             # pytest
./lint.sh             # ruff

Quick start

CLI

# Build all output CSVs (writes to ~/.cache/mhcseqs/)
mhcseqs build

# Build to a specific directory instead
mhcseqs build --output-dir output/

# Look up a specific allele
mhcseqs lookup "HLA-A*02:01"

# Check version
mhcseqs --version

Python API

import mhcseqs

# Build the database (downloads to ~/.cache/mhcseqs/, only needed once)
paths = mhcseqs.build()  # BuildPaths dataclass

# Look up any allele → AlleleRecord with everything
r = mhcseqs.lookup("HLA-A*02:01")
r.sequence          # full protein (with signal peptide)
r.mature_sequence   # signal peptide removed (computed property)
r.mature_start      # signal peptide length (24 for HLA-A*02:01)
r.groove1           # α1 domain
r.groove2           # α2 domain
r.ig_domain         # α3 Ig-fold
r.tail              # TM + cytoplasmic
r.species_category  # "human"

# Apply mutations (IEDB-style, e.g. "K66A")
m = mhcseqs.lookup("HLA-A*02:01", mutations=["K66A", "D77S"])

Load as a DataFrame

import mhcseqs

# As a DataFrame (full sequence + groove decomposition + metadata)
df = mhcseqs.load_sequences_dataframe()

# Or as a list of dicts (no pandas dependency)
rows = mhcseqs.load_sequences_dict()

Data directory

By default, mhcseqs build downloads FASTA files and writes output CSVs to ~/.cache/mhcseqs/ (override with $MHCSEQS_DATA or --output-dir).

~/.cache/mhcseqs/
├── fasta/                     # Downloaded FASTA source files
│   ├── hla_prot.fasta         # IMGT/HLA (human)
│   └── ipd_mhc_prot.fasta    # IPD-MHC (non-human)
├── mhc-seqs-raw.csv           # Every protein entry from all sources
├── mhc-full-seqs.csv          # One representative per two-field allele (with grooves)
├── mhc-merge-report.txt       # Deduplication decisions
└── mhc-validation-report.txt  # Sanity checks

Pre-built CSVs are also attached to each GitHub release.

Output files

File	Description
mhc-seqs-raw.csv	Every protein entry from all sources
mhc-full-seqs.csv	One representative per two-field allele: full sequence, groove decomposition, and metadata

Current data summary

All sources (IMGT/HLA, IPD-MHC, UniProt curated references, and 15,860 diverse MHC sequences from UniProt) are merged into a single dataset:

Category	Class I	Class II	Total
human	17,462	7,878	25,340
nhp	4,639	2,486	7,125
murine	59	29	88
ungulate	638	1,128	1,766
carnivore	166	318	484
other_mammal	469	386	855
bird	5,961	3,351	9,312
fish	1,292	3,569	4,861
other_vertebrate	470	666	1,136
total	31,156	19,811	50,967

Covering 466+ species prefixes. Groove parse success rate on IMGT/IPD-MHC entries: 99.6%.

Structural decomposition

Each protein chain is decomposed into four contiguous regions:

Column	Class I alpha	Class II alpha	Class II beta
groove1	α1 domain (~90 aa)	α1 domain (~83 aa)	—
groove2	α2 domain (~93 aa)	—	β1 domain (~93 aa)
ig_domain	α3 Ig-fold (~95 aa)	α2 Ig-fold (~95 aa)	β2 Ig-fold (~95 aa)
tail	TM + cytoplasmic	TM + cytoplasmic	TM + cytoplasmic

For a class I chain: mature_protein = groove1 + groove2 + ig_domain + tail

Key columns

Both CSVs share: gene, mhc_class, chain, species, species_category, species_prefix, source, source_id.

source is one of: imgt, ipd_mhc, uniprot_curated, uniprot_reference, uniprot_diverse.

source_id is the database accession for provenance tracking (e.g., HLA00001 for IMGT, NHP00001 for IPD-MHC, P01901 for UniProt).

species_category is one of: human, nhp, murine, ungulate, carnivore, cetacean, other_mammal, bird, fish, other_vertebrate.

Data sources

Source	source value	Species	Data
IMGT/HLA	imgt	Human	Downloaded at build time
IPD-MHC	ipd_mhc	Non-human	Downloaded at build time
UniProt	uniprot_reference	Multi-species	B2M references (shipped in package)
UniProt	uniprot_curated	Mouse	30 H-2 alleles (shipped in package)
UniProt	uniprot_diverse	614 species	16,208 diverse MHC sequences (shipped in package)

IMGT/HLA and IPD-MHC FASTA files are downloaded on first build and cached. The UniProt curated CSVs (b2m_sequences.csv, mouse_h2_sequences.csv, diverse_mhc_sequences.csv) ship inside the mhcseqs package — no download needed.

To refresh the diverse MHC dataset from UniProt:

python scripts/fetch_diverse_mhc.py    # Download raw data → data/diverse_mhc_raw.csv
python scripts/curate_diverse_mhc.py   # Curate → mhcseqs/diverse_mhc_sequences.csv

Species prefixes

Species	Latin name	MHC prefix
human	Homo sapiens	HLA
macaque	Macaca mulatta	Mamu
chimpanzee	Pan troglodytes	Patr
gorilla	Gorilla gorilla	Gogo
mouse	Mus musculus	H2
rat	Rattus norvegicus	RT1
cattle	Bos taurus	BoLA
pig	Sus scrofa	SLA
horse	Equus caballus	ELA
sheep	Ovis aries	OLA
dog	Canis lupus familiaris	DLA
cat	Felis catus	FLA
chicken	Gallus gallus	Gaga
salmon	Salmo salar	Sasa
zebrafish	Danio rerio	Dare

Groove extraction algorithm

The groove parser is alignment-free — it uses conserved Cys-Cys disulfide pairs in Ig-fold domains as structural landmarks to slice domain boundaries without multiple sequence alignment.

See groove.py module docstring for detailed algorithm documentation with ASCII structural diagrams.

Dependencies

• Python 3.10+
• mhcgnomes >= 3.1.0 — allele name parsing

No alignment tools, BLAST, or structure databases are required.

Repository structure

mhcseqs/
├── mhcseqs/
│   ├── __init__.py        # Public API
│   ├── __main__.py        # CLI entry point
│   ├── version.py         # Package version
│   ├── download.py        # FASTA source downloading
│   ├── species.py         # Species taxonomy (29-class → 10-class)
│   ├── alleles.py         # Allele name parsing (mhcgnomes wrapper)
│   ├── groove.py          # Binding groove extraction + mutation support
│   ├── imgt.py            # IMGT G-DOMAIN position numbering
│   ├── pipeline.py        # Two-step build pipeline
│   ├── validate.py        # Post-build validation
│   ├── b2m_sequences.csv                # Reference B2M sequences (UniProt)
│   ├── mouse_h2_sequences.csv          # Mouse H-2 sequences (UniProt)
│   └── diverse_mhc_sequences.csv      # 16k diverse MHC sequences (UniProt)
├── tests/                 # pytest test suite
├── scripts/
│   ├── fetch_diverse_mhc.py      # Download diverse MHC from UniProt
│   ├── curate_diverse_mhc.py     # Curate into shipped CSV
│   └── validate_signal_peptides.py
├── data/                          # Intermediate data (not shipped)
├── build.py               # Convenience shim
├── pyproject.toml         # Package metadata
├── develop.sh             # Install in dev mode
├── lint.sh                # Run ruff
├── test.sh                # Run pytest
└── deploy.sh              # Build + publish to PyPI

License

Apache 2.0