FridrichMethod Germinal Antibody and Nanobody Design
Germinal is an open pipeline for epitope-targeted de novo antibody and nanobody design. It hallucinates CDRs on a fixed framework, designs sequences with AbMPNN, and cofolds with a structure predictor (it downloads AlphaFold-Multimer params). Runnable through biomodals.
The biomodals author notes Germinal is finicky and suggests BoltzGen for general binder design; treat Germinal as the antibody-format option, not a default.
Prerequisites
| Requirement | Value |
|---|---|
| Runner | Modal (biomodals) |
| GPU | H100 (default; GPU env var) |
| Setup | See Getting started |
How to run
git clone https://github.com/hgbrian/biomodals && cd biomodals
uv run --with modal --with PyYAML modal run modal_germinal.py \
--target-yaml target_example.yaml \
--max-trajectories 1 \
--max-passing-designs 1
Key parameters
| Parameter | Default | Description |
|---|---|---|
--target-yaml |
required | Target config (target_name, target_pdb_path, target_chain, binder_chain, target_hotspots, length) |
--run-type |
vhh |
vhh (nanobody) or scfv |
--max-trajectories |
100 | Trajectories to run |
--max-passing-designs |
10 | Stop after this many passing designs |
--out-dir |
./out/germinal |
Output directory |
Target YAML
target_name: PDL1
target_pdb_path: target.pdb
target_chain: A
binder_chain: B
target_hotspots: "45,67,89"
length: 120
Decision tree
Antibody-format binder?
│
├─ Nanobody / VHH → germinal (run-type vhh) or mber
├─ scFv → germinal (run-type scfv)
└─ Miniprotein (not antibody) → binder-design (boltzgen, bindcraft, mosaic)
For VHH nanobodies, biomodals also has modal_mber.py (mBER) and modal_iggm.py
(IgGM) as alternatives.
Cost
Adaptyv's own tests of these models showed Germinal costing about $1.60 per accepted design, averaged across 7 targets.
Troubleshooting
| Issue | Cause | Fix |
|---|---|---|
| Pipeline fails early | Missing PyYAML | Add --with PyYAML to the invocation |
| No passing designs | Hard epitope or low budget | Raise --max-trajectories |
| OOM | Large target | Use the default H100 or trim the target |
Next: Validate with boltz or chai, rank with ipsae, filter with protein-qc.