菜单
Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen or BindCraft, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types.
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基于 SOC 职业分类
Computational protein design toolkit. Use this skill group when: (1) Designing protein binders from scratch, (2) Predicting protein structures (Chai, Boltz, Protenix), (3) Sequence design with ProteinMPNN/LigandMPNN/SolubleMPNN, (4) Quality control and filtering of protein designs, (5) Planning and managing binder design campaigns, (6) Experimental characterization (SPR, BLI, cell-free expression), (7) Searching protein databases (PDB, UniProt, AFDB), (8) Antibody/nanobody design (IgGM, mBER). This is the top-level index. Sub-skills handle specific tools.
Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools.
Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
| name | binder-design |
| description | Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen or BindCraft, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. |
| license | MIT |
| category | orchestration |
| tags | ["guidance","tool-selection","workflow"] |
| source | https://github.com/adaptyvbio/protein-design-skills |
De novo binder design?
│
├─ Standard target → BoltzGen ✓ (recommended)
│ All-atom output, single-step: backbone + sequence + side chains
│
├─ Need diversity/exploration → BoltzGen (multiple runs)
│ or BindCraft with varied hotspots
│
├─ Integrated end-to-end → BindCraft
│ Built-in AF2 validation loop
│
├─ Ligand binding → BoltzGen ✓
│ All-atom diffusion handles ligand context natively
│
└─ Antibody/Nanobody → IgGM or mBER
| Tool | Strengths | Weaknesses | Best For |
|---|---|---|---|
| BoltzGen | All-atom, single-step, ligand-aware | Higher GPU requirement (L40S) | Standard (recommended) |
| BindCraft | End-to-end, built-in validation | Less diverse outputs | Production campaigns |
| IgGM | Antibody/nanobody CDR design | Specialized format | Ab/VHH design |
| mBER | VHH nanobody, mask-based design | VHH-specific | VHH optimization |
Target → BoltzGen → Chai → QC filter
(pdb) (all-atom) (val) (rank)
# binder.yaml
entities:
- protein:
id: B
sequence: 70..100
- file:
path: target.cif
include:
- chain:
id: A
binding_types:
- chain:
id: A
binding: 45,67,89
modal run modal_boltzgen.py \
--input-yaml binder.yaml \
--protocol protein-anything \
--num-designs 50
For end-to-end design with integrated validation:
modal run modal_bindcraft.py \
--input-pdb target.pdb \
--hotspots "A45,A67,A89" \
--number-of-final-designs 50
modal run modal_chai1.py --input-faa sequences.fasta --out-dir predictions/
Filter: pLDDT > 0.80, ipTM > 0.50, PAE_interface < 10, scRMSD < 2.0Å
| Stage | Count |
|---|---|
| BoltzGen designs | 50-200 |
| After Chai validation | all |
| After QC filtering | 50-100 |
| Experimental testing | 10-50 |