# Protein Qc

> >

- **Type:** Skill
- **Install:** `agentstack add skill-adaptyvbio-protein-design-skills-protein-qc`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [adaptyvbio](https://agentstack.voostack.com/s/adaptyvbio)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [adaptyvbio](https://github.com/adaptyvbio)
- **Source:** https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/protein-qc

## Install

```sh
agentstack add skill-adaptyvbio-protein-design-skills-protein-qc
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Protein Design Quality Control

## Critical Limitation

**Individual metrics have weak predictive power for binding**. Research shows:
- 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

These thresholds filter out poor designs but do NOT predict binding affinity.

## QC Organization

QC is organized by **purpose** and **level**:

| Purpose | What it assesses | Key metrics |
|---------|------------------|-------------|
| **Binding** | Interface quality, binding geometry | ipTM, PAE, SC, dG, dSASA |
| **Expression** | Manufacturability, solubility | Instability, GRAVY, pI, cysteines |
| **Structural** | Fold confidence, consistency | pLDDT, pTM, scRMSD |

Each category has two levels:
- **Metric-level**: Calculated values with thresholds (pLDDT > 0.85)
- **Design-level**: Pattern/motif detection (odd cysteines, NG sites)

---

## Quick Reference: All Thresholds

| Category | Metric | Standard | Stringent | Source |
|----------|--------|----------|-----------|--------|
| **Structural** | pLDDT | > 0.85 | > 0.90 | AF2/Chai/Boltz |
| | pTM | > 0.70 | > 0.80 | AF2/Chai/Boltz |
| | scRMSD |  0.61 | > 0.70 | AF3/Boltz (see ipsae) |
| | ipTM | > 0.50 | > 0.60 | AF2/Chai/Boltz |
| | PAE_interaction |  0.50 | > 0.62 | PyRosetta |
| | interface_dG |  0.0 | > 0.2 | ESM2 |
| | Folding ΔG | = 3 consecutive | Proteolysis | Flag |
| >= 6 hydrophobic run | Aggregation | Redesign |

See: references/binding-qc.md, references/expression-qc.md, references/structural-qc.md

---

## Interface metrics (PyRosetta)

Beyond shape complementarity and interface_dG, two interface metrics catch common
de novo failure modes:

- **Buried unsatisfied H-bonds (BUNS)**: buried polar atoms making no hydrogen bond.
  This is a dominant energetic failure mode and is orthogonal to dG and dSASA. Keep
  interface BUNS at or below 4 (standard) or 2 (stringent).
- **ContactMolecularSurface**: shape-complementarity-weighted contact area that, unlike
  dSASA, is not fooled by gappy or holey interfaces. Higher is better.

Both are in the Cao 2022, AlphaProteo, and BindCraft filter sets.

For structure-quality ranking, biomodals also provides `modal_af2rank.py` (AF2Rank),
which scores how well a design re-predicts from its own structure as a template.

## Binder ranking (benchmark-backed)

A meta-analysis of 3,766 experimentally tested binders across 15 targets (Overath et
al., bioRxiv 2025, doi:10.1101/2025.08.14.670059) found that AF3 `ipSAE_min` is the
single best in-silico predictor of binding, and that a simple linear model of three
features generalizes best across targets. Complexity did not help: gradient-boosted
and many-feature models did not beat the linear one.

Recommended filtering strategies from that work:

1. Threshold on one of: `AF3 ipSAE_min > 0.61`, or `ipSAE_min × interface_ΔG/ΔSASA  0.42`.
2. Pre-filter on `shape_complementarity > 0.62` and `RMSD_binder  0.85]

# Stage 2: Self-consistency
designs = designs[designs['scRMSD']  0.5) & (designs['PAE_interaction']  0.0]

# Stage 5: Expression checks (design-level)
designs = designs[designs['cysteine_count'] % 2 == 0]  # Even cysteines
designs = designs[designs['instability_index']  15% | Excellent | Above average, proceed |
| 10-15% | Good | Normal, proceed |
| 5-10% | Marginal | Below average, review issues |
|  0.85)

```
Low pLDDT across campaign
├── Check scRMSD distribution
│   ├── High scRMSD (>2.5Å): Backbone issue
│   │   └── Fix: Regenerate backbones with lower noise_scale (0.5-0.8)
│   └── Low scRMSD but low pLDDT: Disordered regions
│       └── Fix: Check design length, simplify topology
├── Try more sequences per backbone
│   └── modal run modal_ligandmpnn.py --input-pdb bb.pdb --params-str "--number_of_batches 32 --temperature 0.1"
├── Use SolubleMPNN instead of ProteinMPNN
│   └── Better for expression-optimized sequences
└── Consider different design tool
    └── BindCraft (integrated design) may work better
```

### Too Few Pass ipTM Filter ( 0.5)

```
Low ipTM across campaign
├── Review hotspot selection
│   ├── Are hotspots surface-exposed? (SASA > 20Ų)
│   ├── Are hotspots conserved? (check MSA)
│   └── Try 3-6 different hotspot combinations
├── Increase binder length (more contact area)
│   └── Try 80-100 AA instead of 60-80 AA
├── Check interface geometry
│   ├── Is target flat? → Try helical binders
│   └── Is target concave? → Try smaller binders
└── Try all-atom design tool
    └── BoltzGen (all-atom, better packing)
```

### High scRMSD (> 50% with scRMSD > 2.0Å)

```
Sequences don't specify intended structure
├── ProteinMPNN issue
│   ├── Lower temperature: --sampling_temp "0.1"
│   ├── Increase sequences: --num_seq_per_target 32
│   └── Check fixed_positions aren't over-constraining
├── Backbone geometry issue
│   ├── Backbones may be unusual/strained
│   ├── Regenerate with lower noise_scale (0.5-0.8)
│   └── Reduce diffuser.T to 30-40
└── Try different sequence design
    └── ColabDesign (AF2 gradient-based) may work better
```

### Everything Passes But No Experimental Hits

```
In silico metrics don't predict affinity
├── Generate MORE designs (10x current)
│   └── Computational metrics have high false positive rate
├── Increase diversity
│   ├── Higher ProteinMPNN temperature (0.2-0.3)
│   ├── Different backbone topologies
│   └── Different hotspot combinations
├── Try different design approach
│   ├── BindCraft (different algorithm)
│   ├── ColabDesign (AF2 hallucination)
│   └── BoltzGen (all-atom diffusion)
└── Check if target is druggable
    └── Some targets are inherently difficult
```

### Too Many Designs Pass (> 50%)

```
Suspiciously high pass rate
├── Check if thresholds are too lenient
│   └── Use stringent thresholds: pLDDT > 0.90, ipTM > 0.60
├── Verify prediction quality
│   ├── Are predictions actually running? Check output files
│   └── Are complexes being predicted, not just monomers?
├── Check for data issues
│   ├── Same sequence being predicted multiple times?
│   └── Wrong FASTA format (missing chain separator)?
└── Apply diversity filter
    └── Cluster at 70% identity, take top per cluster
```

---

## Diagnostic Commands

### Quick Campaign Assessment

```python
import pandas as pd

df = pd.read_csv('designs.csv')

# Pass rates at each stage
print(f"Total designs: {len(df)}")
print(f"pLDDT > 0.85: {(df['pLDDT'] > 0.85).mean():.1%}")
print(f"ipTM > 0.50: {(df['ipTM'] > 0.50).mean():.1%}")
print(f"scRMSD  0.85) & (df['ipTM'] > 0.5) & (df['scRMSD']  0.85).mean()  0.50).mean() < 0.1:
    print("ISSUE: Low ipTM - check hotspots or interface geometry")
elif (df['scRMSD'] < 2.0).mean() < 0.5:
    print("ISSUE: High scRMSD - sequences don't specify backbone")
```

---

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [adaptyvbio](https://github.com/adaptyvbio)
- **Source:** [adaptyvbio/protein-design-skills](https://github.com/adaptyvbio/protein-design-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-adaptyvbio-protein-design-skills-protein-qc
- Seller: https://agentstack.voostack.com/s/adaptyvbio
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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