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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).
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SOC 職業分類に基づく
| name | cell-free-expression |
| description | 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). |
| license | MIT |
| category | experimental |
| tags | ["expression","cfps","validation"] |
| source | https://github.com/adaptyvbio/protein-design-skills |
| System | Best For | Yield | Disulfides | Cost |
|---|---|---|---|---|
| E. coli extract | Rapid prototyping | 100-400 μg/mL | Poor | Low |
| E. coli PURE | Defined, unnatural AAs | 50-150 μg/mL | Controllable | High |
| Wheat germ | Eukaryotic proteins | 100-500 μg/mL | Moderate | Medium |
| Rabbit reticulocyte | Mammalian proteins | 10-50 μg/mL | Poor | High |
| HeLa/CHO | Native mammalian | 10-50 μg/mL | Good | Very High |
| Problem | Design Fix | Reagent Fix |
|---|---|---|
| No expression | Codon optimize first 30 codons | Use BL21-CodonPlus extract |
| Low yield | Optimize 5' UTR (ΔG > -5 kcal/mol) | Increase Mg²⁺ (10-18 mM) |
| Aggregation | Add solubility tags (MBP, SUMO) | Add 0.1% Tween-20, chaperones |
| Inactive protein | Slow translation (use rare codons!) | Add GroEL/ES, DnaK/J |
| Truncation | Remove AGG/AGA/CUA clusters | Supplement rare tRNAs |
| Codon | AA | Issue |
|---|---|---|
| AGG | Arg | Very rare, stalling |
| AGA | Arg | Very rare, stalling |
| CUA | Leu | Low abundance |
| Feature | Good | Marginal | Bad |
|---|---|---|---|
| Rare codon content | <3% | 3-8% | >10% |
| First 30 codons rare | 0 | 1-2 | >2 |
| GC content | 45-55% | 35-45% | <30% or >70% |
| 5' UTR ΔG | > -3 kcal/mol | -3 to -8 | < -10 kcal/mol |
| Cysteine count | Even | Mixed | Odd |
| Tag | Size | Enhancement | Notes |
|---|---|---|---|
| MBP | 40 kDa | Excellent | Best overall |
| SUMO | 11 kDa | Very Good | Native N-terminus after cleavage |
| NusA | 55 kDa | Excellent | Large size |
| Trx | 12 kDa | Good | For disulfide proteins |
| Temperature | Use Case |
|---|---|
| 37°C | Fast expression, stable proteins |
| 30°C | Balanced (default) |
| 25°C | Disulfide proteins, complex folds |
| 18-20°C | Aggregation-prone proteins |
1. Deplete DTT (dialysis or IAM 5 mM)
2. Add 4 mM GSSG + 1 mM GSH (4:1 ratio)
3. Add 10 μM PDI
4. Optional: Add 5 μM DsbC
5. Express at 25°C for 4-6 hours
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 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.
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.