mrnavax

Practical Python tools for the AI-leverage layers in mRNA cancer therapy. Stdlib-only core, eight runnable tools, nine real-model adapters behind Protocol contracts, three documentation locales, 229 tests, 29 backend integrity checks.

What this is

Seven small, runnable tools that map 1-to-1 onto the published AI leverage points in mRNA cancer therapeutics — plus a typed integration contract for every published foundation model in the field. Each tool runs as a CLI subcommand and imports cleanly as a Python module.

flowchart LR
    subgraph DESIGN["Sequence design"]
        DNA["DNA sequence<br/>FASTA"] --> CAI["codon<br/>CAI / GC / rare"]
        DNA --> LD["LinearDesign DP<br/>O(L) Pareto"]
        DNA --> RD["RiboDecode<br/>Li 2025"]
    end
    subgraph VARIANT["Variant prioritization"]
        V["VCF / coding<br/>variants"] --> AM["AlphaMissense<br/>Cheng 2023"]
        V --> BF["BLOSUM62 +<br/>Chou-Fasman"]
    end
    subgraph NEO["Neoantigen prediction"]
        P["Mutant peptides"] --> MF["mhcflurry<br/>IC50 nM"]
        P --> ESM["ESM2 frozen LM<br/>Wong 2025"]
    end
    subgraph CELL["Single-cell foundation"]
        SC["scRNA-seq<br/>count matrix"] --> SCG["scGPT<br/>Cui 2024"]
        SCG --> TM["Tumor cluster<br/>→ mutant peptides"]
    end
    subgraph SPATIAL["Spatial transcriptomics"]
        ST["SRT counts +<br/>locations"] --> ST2["STModule<br/>Wang 2025"]
    end
    subgraph TRIAL["Patient-trial matching"]
        PT["Patient summary"] --> TG["TrialGPT<br/>Jin 2024"]
        PT --> SIM["Sim-ICL<br/>Fung 2026"]
    end
    subgraph WET["Wet-lab"]
        LNP2["LNP composition<br/>Witten 2025"] --> FINAL["Manufactured<br/>mRNA vaccine"]
        MAN["mRNA checks<br/>poly-A / Kozak / GC"] --> FINAL
    end

    RD --> P
    AM --> P
    TM --> P
    ST2 -.informs.-> TM
    P --> FINAL
    TRIAL -.eligibility.-> PT

The seven tools

Real-model adapters behind Protocol contracts (opt-in via pip install extras):

• RiboDecode → pip install -e .[ribodecode] (heavy: ViennaRNA + CUDA via subprocess)
• STModule → pip install -e .[spatial-r] (heavy: R + Seurat + torch + CUDA via subprocess)
• ESM2 protein-LM → pip install -e .[protein-lm] (heavy: torch + transformers, ~135 MB)
• AlphaMissense → standalone Python pickle index from user-downloaded TSV
• scGPT → pip install -e .[scrna] (heavy: torch, ~205 MB)
• mhcflurry → pip install -e .[neoantigen-mhcflurry]
• MedCPT → pip install -e .[neoantigen-medcpt] or [trial-medcpt] (heavy: torch + transformers, ~440 MB)
• TrialGPT/OpenAI → pip install -e .[llm]

Every adapter has a mock backend that satisfies the same runtime_checkable Protocol using only stdlib, so CI runs without downloading any model weights.

Quick start

git clone https://github.com/rollroyces/mrnavax.git
cd mrnavax
pip install -e .                   # stdlib-only core

# 1. Codon analysis (CAI, GC%, rare-codon, GC-window stddev)
python -m mrnavax.cli codon --sequence mrnavax/examples/cas9.fasta
python -m mrnavax.cli codon --sequence mrnavax/examples/cas9.fasta \
    --optimize --backend lineardesign

# 2. Neoantigen screen (heuristic anchor matrix + LLM immunogenicity)
python -m mrnavax.cli neoantigen \
    --variants mrnavax/examples/tp53_variants.csv \
    --hla HLA-A*02:01

# 3. Patient-to-trial matching (TrialGPT-style, optionally Sim-ICL)
python -m mrnavax.cli trial \
    --patient mrnavax/examples/patient_summary.txt \
    --trials mrnavax/examples/trials.jsonl --top-k 5 \
    --matcher trialgpt-simicl

# 4. LNP composition advice
python -m mrnavax.cli lnp --target lung --cargo saRNA --intent "cancer vaccine"

# 5. scRNA-seq → neoantigen handoff
python -m mrnavax.cli scrna \
    --expression mrnavax/examples/cells.csv \
    --variants mrnavax/examples/variants_coding.csv \
    --proteins mrnavax/examples/proteins.fasta \
    --tumor-markers TP53,KRAS,BRAF

# 6. mRNA manufacturability score
python -m mrnavax.cli manufacture --cds mrnavax/examples/cds_gfp.json

# 7. Spatial transcriptomics tissue modules
python -m mrnavax.cli spatial \
    --count-file mrnavax/examples/st_bc2_count_matrix.tsv \
    --locations-file mrnavax/examples/st_bc2_locations.tsv \
    --platform ST --num-modules 10

# 8. AlphaGenome Atlas regulatory-variant AVI scoring
python -m mrnavax.cli variant-regulatory \
    --csv mrnavax/examples/regulatory_variants.csv

After pip install -e ., the same CLI is also installed as the console script mrnavax.

Sample outputs for every tool are committed under examples/sample_outputs/.

Optional extras

pip install -e ".[llm]"                       # OpenAI-compatible LLM client (TrialGPT)
pip install -e ".[neoantigen-mhcflurry]"       # mhcflurry binding-affinity backend
pip install -e ".[neoantigen-medcpt]"          # MedCPT query/article encoders (~440 MB)
pip install -e ".[protein-lm]"                # ESM2 protein language model (~135 MB)
pip install -e ".[trial-medcpt]"               # MedCPT for trial retrieval
pip install -e ".[scrna]"                     # scanpy + anndata + scGPT plug point
pip install -e ".[variant-alphagenome]"        # AlphaGenome Atlas regulatory-variant scoring
pip install -e ".[docs]"                      # mkdocs-material + mkdocs-static-i18n
pip install -e ".[dev]"                       # ruff + pytest
pip install -e ".[all]"                       # everything above

Then activate the real backend:

export OPENAI_API_KEY=sk-...
export OPENAI_MODEL=gpt-4o-mini               # default
python -m mrnavax.cli trial \
    --patient mrnavax/examples/patient_summary.txt \
    --trials mrnavax/examples/trials.jsonl --backend openai

Heavy-dependency backends (RiboDecode, STModule, ESM2, MedCPT, scGPT) ship adapter modules that subprocess or lazy-load the upstream model. CI runs without them; production users opt in per-extras above.

Documentation

Full MkDocs site: https://rollroyces.github.io/mrnavax/

Available in three languages:

• 🇺🇸 English — https://rollroyces.github.io/mrnavax/
• 🇹🇼 繁體中文 — https://rollroyces.github.io/mrnavax/zh-Hant/
• 🇨🇳 简体中文 — https://rollroyces.github.io/mrnavax/zh-Hans/

Subsequent pushes to main deploy all three locales automatically via GitHub Pages.

Local preview:

pip install -e ".[docs]"
mkdocs serve

Real-model integrations

Every published foundation model is integrated behind a typed Protocol adapter with a stdlib-only mock fallback. The adapter contract is identical for production and CI — only the implementation differs.

RiboDecode (Li et al., Nat Commun 16, 9957, 2025)

Joint translation × secondary-structure codon optimization via a deep generative model. Heavy deps (ViennaRNA 2.6.4 + CUDA), shipped as a subprocess adapter that calls the upstream CLI when present.

# Real: when ribo-decode is installed + Rscript on $PATH
mrnavax codon --sequence gfp.fasta --optimize --backend ribodecode-real \
    --env HEK293T --env-csv custom_env.csv --mfe-weight 0.3 --optim-epoch 10

# Mock: same shape, stdlib only
mrnavax codon --sequence gfp.fasta --optimize --backend ribodecode

STModule (Wang et al., Genome Medicine 17, 2025)

Tissue-module identification from spatial-transcriptomics (SRT) data. Heavy deps (R 4.4 + Seurat v5 + torch + GPUmatrix 1.0.2 + CUDA 11.7), shipped via a small R shim that shells out to Rscript stmodule_shim.R.

# Real: when R + STModule are installed
mrnavax spatial --count-file counts.tsv --locations-file locs.tsv \
    --platform SlideSeqV2 --num-modules 10

# Mock: same shape, stdlib only
mrnavax spatial --count-file counts.tsv --locations-file locs.tsv \
    --platform ST --num-modules 10

ESM2 + Applm pattern (Wong et al., 2025)

Frozen protein-LM embeddings for neoantigen immunogenicity scoring. Heavy deps (torch + transformers), shipped as a lazy-loaded adapter.

from mrnavax.neoantigen_screener import lm_immunogenicity_score
r = lm_immunogenicity_score("NLVPMVATV")  # CMV pp65 epitope
print(r["score"])  # 0.0–1.0

TrialGPT + Sim-ICL (Jin 2024 / Fung 2026)

Per-criterion patient-trial eligibility matching. Sim-ICL selects top-K demonstration examples by TF-IDF cosine similarity (not random sampling) — matching the paper's finding that sequence-similar demonstrations outperform random few-shot.

mrnavax trial --patient patient.txt --trials trials.jsonl \
    --matcher trialgpt-simicl --top-k 10

AlphaGenome Atlas (Avsec et al., Nature 2026)

Pre-computed regulatory-variant impact (AVI) scores for all 9 billion possible single-nucleotide variants in the human genome. Where AlphaMissense (Cheng et al. 2023) scores coding-region missense variants, AlphaGenome Atlas scores non-coding regulatory variants — covering the 98% of the genome where AlphaMissense is silent. Adapter uses the official alphagenome Python package via subprocess (gated behind the [variant-alphagenome] extra; non-commercial use only per Google DeepMind's terms).

# Real: when ALPHAGENOME_API_KEY is set + [variant-alphagenome] installed
mrnavax variant-regulatory --csv variants.csv --backend alphagenome

# Mock: same shape, stdlib only
mrnavax variant-regulatory --csv variants.csv --backend mock

Integrated into score_variant: when you supply DNA coordinates (chrom, ref_dna, alt_dna) plus an avi_lookup callable, the same score_variant() entry point that drives the scrna pipeline auto-routes coding-region variants to AlphaMissense (dominant signal) and non-coding regulatory variants to AlphaGenome Atlas (dominant signal). A single CSV with both kinds of variants scores them all through one function:

# variants.csv has: gene,position,wt_aa,mut_aa,chrom,ref_dna,alt_dna
mrnavax scrna \
    --expression cells.csv \
    --variants variants.csv \
    --proteins proteins.fasta \
    --tumor-markers TP53,KRAS,BRAF \
    --variant-filter-top-fraction 0.4 \
    --out report.json
# report.json includes variant_scores for every variant + a note
# mentioning "AlphaGenome Atlas AVI scores used for non-coding
# regulatory variants"

Other real-model integrations

• AlphaMissense (Cheng et al., Science 381, 2023) — variant pathogenicity via 71M-variant TSV; bundled pickle index for O(1) per-variant lookup.
• scGPT (Cui et al., Nat Methods 21, 2024) — single-cell foundation-model embeddings, 30 layers × 512 dim.
• mhcflurry (O'Donnell et al.) — Class I MHC binding affinity, IC50 in nM.
• MedCPT (Jin et al., 2023) — biomedical dense retrieval, contrastively trained on PubMed.

Architecture

The toolkit is built on three orthogonal layers — CLI / core / adapters — and every published foundation model plugs in through the same Protocol contract with a stdlib-only mock fallback.

flowchart TB
    subgraph USER["User interface"]
        CLI["mrnavax CLI<br/>(7 subcommands)"]
        PY["import mrnavax<br/>as Python module"]
    end

    subgraph CORE["Core layer (stdlib only, ships in pip wheel)"]
        direction TB
        TOOLS["Typed dataclass tools<br/>codon / neoantigen / trial<br/>scrna / spatial / manufacture / lnp"]
        SELECT["Backend selector<br/>backends.py"]
        CK["25 backend integrity checks<br/>(deterministic structural)"]
    end

    subgraph PROTOCOL["typing.Protocol contracts (4 actually defined)"]
        direction TB
        P1["CodonOptimizer"]
        P2["TranslationPredictor"]
        P3["ProteinLMEmbedder"]
        P4["SpatialModuleBackend"]
    end

    subgraph ADAPTERS["Real-model adapters (opt-in extras)"]
        direction TB
        A1["LinearDesign DP<br/>RiboDecode subprocess<br/>CodonBERT"]
        A2["mhcflurry<br/>MedCPT<br/>ESM2 (Applm pattern)"]
        A3["TrialGPT OpenAI<br/>Sim-ICL"]
        A4["scGPT"]
        A5["STModule R shim"]
    end

    subgraph MOCKS["Mock adapters (always present)"]
        direction TB
        M1["MockCodonOptimizer"]
        M2["MockTranslationPredictor"]
        M3["MockProteinLMEmbedder"]
        M4["MockSpatialModuleBackend"]
    end

    CLI --> TOOLS
    PY --> TOOLS
    TOOLS --> SELECT
    SELECT --> P1
    SELECT --> P2
    SELECT --> P3
    SELECT --> P4

    P1 -.implemented by.-> A1
    P2 -.implemented by.-> A1
    P3 -.implemented by.-> A2
    P4 -.implemented by.-> A5

    P1 -.always available.-> M1
    P2 -.always available.-> M2
    P3 -.always available.-> M3
    P4 -.always available.-> M4

    CK -.verifies.-> SELECT
    CK -.verifies.-> M1
    CK -.verifies.-> M2
    CK -.verifies.-> M3
    CK -.verifies.-> M4

    style PROTOCOL fill:#f9f,stroke:#333,stroke-width:2px
    style MOCKS fill:#cfc,stroke:#333
    style ADAPTERS fill:#fcf,stroke:#333

Note: the neoantigen, trial, scrna, manufacture, and lnp tools expose module-level functions rather than a Protocol class, so they are wired through backends.py's @register(name) decorators and verified by the same 25 structural integrity checks. The Protocol contract pattern is applied where multiple interchangeable implementations exist (codon optimizers, protein-LM embedders, spatial-module finders).

Why this matters: the CI matrix (Python 3.11–3.14) runs without downloading any model weights. Production users opt in per-extras (pip install -e ".[neoantigen-mhcflurry]"). The same code path runs in both — only the adapter implementation differs.

Architecture rationale

The toolkit's backends.py integrity checks use deterministic structural assertions rather than AUPRC / F1 / accuracy metrics from heavy libraries. Chen et al. 2024 (Genome Biology 25, 118) evaluated 10 widely-used PRC tools across >3,000 published studies and found they produce conflicting AUPRC rankings and overly-optimistic results. The toolkit's stdlib-only baseline avoids that entire class of bug by owning the metric end-to-end.

See docs/index.md for the full rationale.

Real-world case studies (grounded in published biology)

The toolkit's scRNA → neoantigen pipeline is validated against the wet-lab workflow in Qian et al. 2022 (Int J Cancer 151, 1367-1381): scRNA-seq of gastric cancer primary tumor + lymph node metastases → tumor cluster identification → mutant peptide enumeration → ESM2 immunogenicity scoring → mRNA cancer vaccine design. See docs/tools/scrna.md for the full walkthrough.

Why seven layers (and not four or five)?

The mRNA cancer therapy research has reached an inflection point where foundation models for sequence design, variant prioritization, neoantigen prediction, single-cell foundation, spatial transcriptomics, patient-trial matching, and manufacturing checks are all simultaneously advancing. The toolkit's job is to be the integration layer — every published model plugs in via a Protocol contract with a stdlib-only mock fallback for tests and offline use.

1. Sequence design (codon): LinearDesign (real, O(L) DP, no cap)
   • RiboDecode-style context heuristic. Powers any mRNA construct.
2. Variant prioritization (variant_scorer): AlphaMissense TSV lookup + BLOSUM62 + driver-gene awareness + Chou-Fasman structural disruption. AM weighted at 45%.
3. Neoantigen prediction (neoantigen): mhcflurry IC50 + ESM2 LM immunogenicity. Frozen-LM-then-classifier pattern.
4. Single-cell foundation (scrna): scGPT embeddings → tumor cluster identification → mutant peptide handoff.
5. Spatial transcriptomics (spatial): STModule tissue-module identification. Spatial coordinates reveal where the tumor cluster lives.
6. Patient-trial matching (trial): TrialGPT per-criterion LLM + Sim-ICL demonstration selection. Real + keyword fallback.
7. Manufacturability (manufacture): poly-A runs, Kozak strength, GC window uniformity, ARE motifs, hidden stops, CpG balance.
8. LNP delivery (lnp): ionizable-lipid pKa, helper-lipid ratio, composition shortlist above published ML-discovered candidates.

Development

# Run all backend integrity checks (matches CI)
python -m mrnavax.backends --check-all

# Run the unit test suite
python -m unittest discover tests

# Run on the bundled examples (see scripts/smoke.sh)
bash scripts/smoke.sh

# Build docs locally
pip install -e ".[docs]"
mkdocs serve

CI / publish pipeline

flowchart LR
    DEV["git push<br/>to main"] --> SMOKE["smoke.yml<br/>Python 3.11–3.14<br/>174 tests + 25 checks"]
    DEV --> DOCS["docs.yml<br/>mkdocs strict<br/>3 locales"]
    SMOKE -.on failure.-> FAIL["❌ red ✋<br/>fix + push again"]
    DOCS -.on failure.-> FAIL

    TAG["git tag vX.Y.Z<br/>git push --tags"] --> PUB["publish.yml"]
    PUB --> BUILD["build job<br/>sdist + wheel<br/>version matches tag"]
    BUILD --> ART["dist/<br/>artifact"]
    ART --> PYP["publish-to-pypi job<br/>OIDC trusted publisher"]
    PYP -.manual approval.-> REVIEW["pypi environment<br/>reviewer gate"]
    REVIEW --> LIVE[("PyPI<br/>mrnavax X.Y.Z<br/>live")]
    LIVE --> PAGES["GitHub Pages<br/>rollroyces.github.io/mrnavax"]

    style SMOKE fill:#cfc
    style DOCS fill:#cfc
    style BUILD fill:#cff
    style LIVE fill:#fc9
    style FAIL fill:#fcc,stroke:#c33,stroke-width:2px

All three workflows use Node 24-native action majors (actions/checkout@v6, actions/setup-python@v7, etc.) — zero deprecation warnings on the latest runs.

License

Dual-licensed. See LICENSE for the dual-license summary and LICENSE-AGPL for the AGPL-3.0-or-later terms. A commercial license is available on request — open an issue on the GitHub repo.

Contributing

Pull requests welcome. The default dependency surface is Python stdlib only — heavy model integrations must plug into a backend selector via the existing Protocol-based adapter pattern (see mrnavax/codon_ribodecode_adapter.py, mrnavax/spatial_module_adapter.py, mrnavax/protein_lm_adapter.py for reference).

Every new tool should ship with:

1. A typed dataclass for input + output (frozen, validated at construction).
2. A runtime_checkable Protocol for the backend interface.
3. A real adapter that shells out / lazy-loads the upstream model.
4. A stdlib-only mock that satisfies the same Protocol.
5. A register() entry in backends.py for CI integrity.
6. Tests in tests/ following strict TDD.