Execute qualquer Skill no Manus
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Execute qualquer Skill no Manus com um clique

germinal

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Atualizado11 de junho de 2026 às 14:45

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. For miniprotein binders, use binder-design (BoltzGen, BindCraft, RFdiffusion, Mosaic). For structure validation, use boltz or chai.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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Fonte

adaptyvbio

adaptyvbio/protein-design-skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Cientistas biológicos, todos os outrosCiências da Vida, Físicas e Sociais·SOC 19-1029

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name	germinal
description	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. For miniprotein binders, use binder-design (BoltzGen, BindCraft, RFdiffusion, Mosaic). For structure validation, use boltz or chai.
license	MIT
category	design-tools
tags	["antibody","nanobody","vhh","scfv","binder"]
biomodals_script	modal_germinal.py

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.