By name: protein-qc description: > Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. license: MIT category: evaluation tags: [qc, filtering, metrics, thresholds] source: https://github.com/adaptyvbio/protein-design-skills
Protein Design Quality Control
Critical Limitation
Individual metrics have weak predictive power for binding:
- Individual metric ROC AUC: 0.64-0.66 (slightly better than random)
- Metrics are pre-screening filters, not affinity predictors
- Composite scoring is essential for meaningful ranking
Quick Reference: All Thresholds
| Category | Metric | Standard | Stringent |
|---|---|---|---|
| Structural | pLDDT | > 0.85 | > 0.90 |
| pTM | > 0.70 | > 0.80 | |
| scRMSD | < 2.0 Å | < 1.5 Å | |
| Binding | ipTM | > 0.50 | > 0.60 |
| PAE_interaction | < 12 Å | < 10 Å | |
| interface_dG | < -10 | < -15 | |
| Expression | Instability | < 40 | < 30 |
| GRAVY | < 0.4 | < 0.2 | |
| ESM2 PLL | > 0.0 | > 0.2 |
Design-Level Checks
| Pattern | Risk | Action |
|---|---|---|
| Odd cysteine count | Unpaired disulfides | Redesign |
| NG/NS/NT motifs | Deamidation | Flag/avoid |
| K/R ≥ 3 consecutive | Proteolysis | Flag |
| ≥ 6 hydrophobic run | Aggregation | Redesign |
Sequential Filtering Pipeline
import pandas as pd
designs = pd.read_csv('designs.csv')
# Stage 1: Structural confidence
designs = designs[designs['pLDDT'] > 0.85]
# Stage 2: Self-consistency
designs = designs[designs['scRMSD'] < 2.0]
# Stage 3: Binding quality
designs = designs[(designs['ipTM'] > 0.5) & (designs['PAE_interaction'] < 10)]
# Stage 4: Sequence plausibility
designs = designs[designs['esm2_pll_normalized'] > 0.0]
# Stage 5: Expression checks
designs = designs[designs['cysteine_count'] % 2 == 0]
designs = designs[designs['instability_index'] < 40]
Composite Scoring
def composite_score(row):
return (
0.30 * row['pLDDT'] +
0.20 * row['ipTM'] +
0.20 * (1 - row['PAE_interaction'] / 20) +
0.15 * row['shape_complementarity'] +
0.15 * row['esm2_pll_normalized']
)
designs['score'] = designs.apply(composite_score, axis=1)
top_designs = designs.nlargest(100, 'score')
Metric AUC Comparison
| Metric | AUC |
|---|---|
| ipTM | ~0.64 |
| PAE | ~0.65 |
| ESM2 PLL | ~0.72 |
| Composite | ~0.75+ |
Campaign Health
| Pass Rate | Status |
|---|---|
| > 15% | Excellent |
| 10-15% | Good |
| 5-10% | Marginal |
| < 5% | Poor — diagnose |
Failure Recovery
Low pLDDT (< 5% passing):
- Regenerate backbones with
noise_scale=0.5-0.8 - Use SolubleMPNN instead of ProteinMPNN
- Try BindCraft (integrated design)
Low ipTM (< 5% passing):
- Review hotspot selection (surface-exposed, conserved)
- Increase binder length (more contact area)
- Try BoltzGen for all-atom precision
High scRMSD (> 50%):
- Lower ProteinMPNN temperature to 0.1
- Increase sequences per backbone to 32
- Regenerate backbones with lower noise_scale