vaxrank 2.0.2

Mutant peptide ranking for personalized cancer vaccines

vaxrank

Selection of mutated protein fragments for therapeutic personalized cancer vaccines.

Usage

vaxrank \
    --vcf tests/data/b16.f10/b16.vcf \
    --bam tests/data/b16.f10/b16.combined.bam \
    --vaccine-peptide-length 25 \
    --mhc-predictor netmhc \
    --mhc-alleles H2-Kb,H2-Db \
    --padding-around-mutation 5 \
    --output-ascii-report vaccine-peptides.txt \
    --output-pdf-report vaccine-peptides.pdf \
    --output-html-report vaccine-peptides.html

Using a YAML Configuration File

You can specify common parameters in a YAML configuration file to avoid repeating them on every run:

vaxrank --config my_config.yaml --vcf variants.vcf --bam tumor.bam

Example my_config.yaml:

epitopes:
  min_score: 0.00001                        # drop epitopes below this score
  scoring_mode: affinity                    # "affinity" or "percentile_rank"
  logistic_midpoint: 350.0                  # IC50 (nM) at which score = 0.5
  logistic_width: 150.0                     # steepness of logistic curve
  affinity_cutoff: 5000.0                   # IC50 >= this → score 0
  percentile_rank_cutoff: 10.0              # rank >= this → score 0 (percentile mode)
  top_epitopes_per_candidate: 1000          # 0 = keep all

vaccine_peptides:
  preferred_length: 25                      # target amino acids per vaccine peptide
  min_length: 25                            # minimum vaccine peptide length
  max_length: 25                            # maximum vaccine peptide length
  padding_around_mutation: 5                # off-centre windows to consider
  per_mutation: 1                           # peptides to keep per variant
  max_epitopes_per_candidate: 1000          # 0 = keep all
  score_fraction_of_best: 0.99              # drop candidates scoring < 99% of best
  manufacturability:                        # GRAVY = mean hydropathy
    max_c_terminal_hydropathy: 1.5          # max GRAVY of C-terminal 7-mer
    min_kmer_hydropathy: 0.0                # min max-7mer GRAVY (floor)
    max_kmer_hydropathy_low_priority: 1.5   # low-priority max-7mer GRAVY cap
    max_kmer_hydropathy_high_priority: 2.5  # high-priority max-7mer GRAVY cap

CLI arguments override values from the config file. You can also use --config-value to override any config value without editing the file:

vaxrank --config my_config.yaml \
  --config-value vaccine_peptides.score_fraction_of_best=0.95 \
  --config-value epitopes.percentile_rank_cutoff=5.0

Use --config-text when the right-hand side should be kept as a raw string instead of being YAML-parsed.

Installation

Vaxrank can be installed using pip:

pip install vaxrank

Requirements: Python 3.9+

Note: to generate PDF reports, you first need to install wkhtmltopdf, which you can do (on macOS) like so:

brew install --cask wkhtmltopdf

Vaxrank uses PyEnsembl for accessing information about the reference genome. You must install an Ensembl release corresponding to the reference genome associated with the mutations provided to Vaxrank.

Example for GRCh38 (adjust release to match your reference):

pyensembl install --release 113 --species human

Example for GRCh37 (legacy):

pyensembl install --release 75 --species human

If your variants were called from alignments against hg19 then you can still use GRCh37 but should ignore mitochondrial variants.

Features

Reference Proteome Filtering

Vaxrank filters out peptides that exist in the reference proteome to focus on truly novel mutant sequences. This uses a set-based kmer index for O(1) membership testing. The index is built once and cached locally for subsequent runs.

Cancer Hotspot Annotation

Vaxrank annotates variants that occur at known cancer mutation hotspots using bundled data from cancerhotspots.org (Chang et al. 2016, 2017). This helps identify clinically relevant mutations. The hotspot data includes ~2,700 recurrently mutated positions across cancer types.

MHC Binding Prediction

Vaxrank integrates with MHC binding predictors via mhctools. Use --mhc-predictor <name> to select one:

--mhc-predictor	Tool	MHC Class	Notes
mhcflurry	MHCflurry	I	Open-source neural network; installed with mhctools
bigmhc	BigMHC	I	Auto-detects EL or IM model
bigmhc-el	BigMHC EL	I	Presentation (eluted ligand) model
bigmhc-im	BigMHC IM	I	Immunogenicity model
pepsickle	Pepsickle	I	Proteasomal cleavage predictor
netmhc	NetMHC	I	Auto-detects NetMHC3 or NetMHC4
netmhc3	NetMHC 3.x	I	Requires local install
netmhc4	NetMHC 4.0	I	Requires local install
netmhcpan	NetMHCpan	I	Auto-detects installed version
netmhcpan28	NetMHCpan 2.8	I	Requires local install
netmhcpan3	NetMHCpan 3.x	I	Requires local install
netmhcpan4	NetMHCpan 4.0	I	Default mode (EL + BA)
netmhcpan4-ba	NetMHCpan 4.0	I	Binding affinity mode only
netmhcpan4-el	NetMHCpan 4.0	I	Eluted ligand mode only
netmhcpan41	NetMHCpan 4.1	I	Default mode (EL + BA)
netmhcpan41-ba	NetMHCpan 4.1	I	Binding affinity mode only
netmhcpan41-el	NetMHCpan 4.1	I	Eluted ligand mode only
netmhcpan42	NetMHCpan 4.2	I	Default mode (EL + BA)
netmhcpan42-ba	NetMHCpan 4.2	I	Binding affinity mode only
netmhcpan42-el	NetMHCpan 4.2	I	Eluted ligand mode only
netmhccons	NetMHCcons	I	Requires local install
netmhcstabpan	NetMHCstabpan	I	Stability predictor; requires local install
netchop	NetChop	--	Proteasomal cleavage predictor
netmhciipan	NetMHCIIpan	II	Auto-detects installed version
netmhciipan3	NetMHCIIpan 3.x	II	Requires local install
netmhciipan4	NetMHCIIpan 4.0	II	Default mode (EL + BA)
netmhciipan4-ba	NetMHCIIpan 4.0	II	Binding affinity mode only
netmhciipan4-el	NetMHCIIpan 4.0	II	Eluted ligand mode only
netmhciipan43	NetMHCIIpan 4.3	II	Default mode (EL + BA)
netmhciipan43-ba	NetMHCIIpan 4.3	II	Binding affinity mode only
netmhciipan43-el	NetMHCIIpan 4.3	II	Eluted ligand mode only
mixmhcpred	MixMHCpred	I	Requires local install
netmhcpan-iedb	NetMHCpan via IEDB	I	Uses IEDB web API
netmhccons-iedb	NetMHCcons via IEDB	I	Uses IEDB web API
netmhciipan-iedb	NetMHCIIpan via IEDB	II	Uses IEDB web API
smm-iedb	SMM via IEDB	I	Uses IEDB web API
smm-pmbec-iedb	SMM-PMBEC via IEDB	I	Uses IEDB web API
random	Random	--	Returns random scores; for testing only

Paper & Citation

The original Vaxrank paper describes an earlier version of the software. The current codebase has been substantially rewritten since publication (updated configuration system, reference proteome filtering, cancer hotspot annotation, expanded predictor support, etc.), but the core algorithm for selecting neoantigen vaccine peptides remains the same.

Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines can be cited as:

@article {Rubinsteyn142919,
    author = {Rubinsteyn, Alex and Hodes, Isaac and Kodysh, Julia and Hammerbacher, Jeffrey},
    title = {Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines},
    year = {2017},
    doi = {10.1101/142919},
    publisher = {Cold Spring Harbor Laboratory},
    abstract = {Therapeutic vaccines targeting mutant tumor antigens ({\textquotedblleft}neoantigens{\textquotedblright}) are an increasingly popular form of personalized cancer immunotherapy. Vaxrank is a computational tool for selecting neoantigen vaccine peptides from tumor mutations, tumor RNA data, and patient HLA type. Vaxrank is freely available at www.github.com/hammerlab/vaxrank under the Apache 2.0 open source license and can also be installed from the Python Package Index.},
    URL = {https://www.biorxiv.org/content/early/2017/05/27/142919},
    eprint = {https://www.biorxiv.org/content/early/2017/05/27/142919.full.pdf},
    journal = {bioRxiv}
}

Development

To install Vaxrank for local development:

git clone [e-mail]:openvax/vaxrank.git
cd vaxrank
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -e .
# Examples; adjust release to match your reference
pyensembl install --release 113 --species human
pyensembl install --release 113 --species mouse

Run linting and tests:

./lint.sh && ./test.sh

The first run of the tests may take a while to build the reference proteome kmer index, but subsequent runs will use the cached index.

Architecture

Configuration

Vaxrank uses msgspec frozen Struct objects for configuration, with all defaults centralised in vaxrank/config/defaults.py. Config values are resolved in order:

1. Compiled-in defaults
2. YAML config file (--config)
3. --config-value / --config-text CLI overrides
4. Dedicated CLI flags (e.g. --vaccine-peptide-length)

EpitopeConfig — epitope scoring and filtering

Field	Default	Description
logistic_epitope_score_midpoint	350.0	IC50 (nM) at which epitope score = 0.5
logistic_epitope_score_width	150.0	Steepness of logistic scoring curve
min_epitope_score	0.00001	Epitopes scoring below this are dropped
binding_affinity_cutoff	5000.0	IC50 >= this → score 0
scoring_mode	"affinity"	"affinity" (IC50-based) or "percentile_rank"
percentile_rank_cutoff	10.0	Rank >= this → score 0 (percentile mode)

VaccineConfig — peptide assembly and manufacturability

Field	Default	Description
preferred_peptide_length	25	Preferred amino acids per vaccine peptide
min_peptide_length	25	Minimum vaccine peptide length
max_peptide_length	25	Maximum vaccine peptide length
padding_around_mutation	5	Off-centre window positions to consider
max_vaccine_peptides_per_variant	1	Peptides to keep per variant
num_mutant_epitopes_to_keep	1000	Max epitope predictions per peptide (0 = all)
score_fraction_of_best	0.99	Drop candidates scoring below this fraction of the best
max_c_terminal_hydropathy	1.5	Max GRAVY score of the C-terminal 7-mer
min_kmer_hydropathy	0.0	Minimum max-7mer GRAVY (floor)
max_kmer_hydropathy_low_priority	1.5	Low-priority max-7mer GRAVY cap
max_kmer_hydropathy_high_priority	2.5	High-priority max-7mer GRAVY cap

The four *_hydropathy* fields control the manufacturability tie-breaking in vaccine peptide ranking. See VaccinePeptide.peptide_synthesis_difficulty_score_tuple for details on how each threshold is applied.

Key Modules

• reference_proteome.py: Set-based kmer index for checking if peptides exist in the reference proteome
• cancer_hotspots.py: Lookup for known cancer mutation hotspots
• epitope_logic.py: Epitope scoring and filtering logic
• core_logic.py: Main vaccine peptide selection algorithm
• report.py: Report generation (ASCII, HTML, PDF, XLSX)

Dependencies

Key dependencies:

• pyensembl: Reference genome annotation
• varcode: Variant effect prediction
• isovar: RNA-based variant calling
• mhctools: MHC binding prediction
• msgspec: Configuration serialization (YAML/JSON)
• pandas, numpy: Data processing
• jinja2, pdfkit: Report generation

Scripts

Helper scripts included in the repo:

• develop.sh: installs the package in editable mode and sets PYTHONPATH to the repo root.
• lint.sh: runs ruff on vaxrank and tests.
• test.sh: runs pytest with coverage.
• deploy.sh: runs lint/tests, builds a distribution with build, uploads via twine, and tags the release (vX.Y.Z). Deploy is restricted to the main/master branch.