Germinal

Skill FridrichMethod/awesome-skills/skills/germinal

"De novo antibody and nanobody (VHH) design with Germinal. Use this skill when: (1) Designing epitope-targeted nanobodies or scFvs, (2) Needing CDR design on a fixed framework, (3) Working on antibody-format binders rather than miniproteins.From its SKILL.md

Install

npx -y skills add FridrichMethod/awesome-skills --skill germinal

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
13 stars13 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.9 KB, 695 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.