bio-protac-degraders

What this skill does

## Version Compatibility

Reference examples tested with: PRosettaC (web service), DeepTernary 1.0+, AlphaFold3 (constraints-enabled), Boltz-1 / Boltz-2, RDKit 2024.09+, OpenMM 8.1+ (for ternary MD).

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# PROTAC and Bivalent Degrader Design

Design bifunctional molecules (PROTACs) that recruit an E3 ubiquitin ligase to a target protein, inducing target ubiquitination and proteasomal degradation. PROTACs differ from traditional drugs: a stable **ternary complex** (target + PROTAC + E3) is required, not just target binding. The PROTAC field exploded post-2020 with clinical successes (ARV-471 estrogen-receptor degrader, ARV-110 androgen-receptor degrader). Postdoc-grade PROTAC design balances **target ligand binding**, **E3 ligand binding**, **linker geometry** (length, rigidity, chemistry), **cooperativity** (positive = ternary stable; negative = hook effect), and **cell permeability** (PROTACs are 800-1500 Da, often Lipinski-violating).

For target ligand design, see `chemoinformatics/virtual-screening` and `chemoinformatics/admet-prediction`. For linker-only enumeration, see `chemoinformatics/reaction-enumeration`. For generative linker design, see `chemoinformatics/generative-design`.

## E3 Ligase Choice

| E3 ligase | Ligand series | Best at | Limitations |
|-----------|---------------|---------|-------------|
| VHL | VL-269 (Salami 2018) | Surface-exposed targets | Tissue-restricted expression |
| CRBN (cereblon) | thalidomide, pomalidomide | Broad tissue expression | Off-target neosubstrates (IKZF1, SALL4) |
| IAP (XIAP, cIAP1) | SMAC mimetics (LCL161, etc.) | Apoptotic / IAP targets | Limited target scope |
| MDM2 | nutlin / idasanutlin | TP53 pathway | Limited target diversity |
| KEAP1 | DDB1-DCAF15-Keap1 | NRF2 pathway | Specialized use |
| RNF114 | EN450 | Newer; under exploration | Limited tooling |
| RNF4 | EN450-RNF4 | Newer; growing | Limited tooling |
| Cyclin E - SKP2 | New ligands emerging | Targeted cancers | Discovery-phase |

**Decision:** For first-generation PROTAC, **CRBN (cereblon)** is the most-developed (thalidomide-derived; broad tissue distribution). **VHL** is second-most-developed (more selective; tissue-restricted). For specialty targets, consider IAP / MDM2.

## Linker Design Principles

Linkers tune ternary complex geometry and stability:

| Property | Range | Effect |
|----------|-------|--------|
| Linker length | 8-30 atoms | Critical; geometry-dependent |
| Linker rigidity | Flexible (PEG) vs rigid (piperazine, pyridine) | Higher rigidity often reduces entropy penalty |
| Linker chemistry | PEG, alkyl, piperazine, triazole, ether, amide | PEG common; rigid for tighter binding |
| Click chemistry compatibility | Triazole compatible | Easy synthesis |
| MW range | PROTAC 800-1500 Da | Lipinski-violating but accepted |
| Polar atoms | 1-5 per linker | Permeability vs solubility balance |

**Critical:** The "Goldilocks linker length" is target-specific. Too short = ternary clash; too long = ternary entropy too high. Typically 12-20 atoms for surface-exposed targets.

## Decision Tree by Scenario

| Goal | E3 / linker | Tools |
|------|-------------|-------|
| First-generation PROTAC, surface-exposed target | CRBN + PEG linker (10-15 atoms) | PRosettaC for ternary prediction |
| Selective degrader (avoid off-target) | VHL + rigid linker | PRosettaC + cellular validation |
| BTK / IAP family targets | IAP-based PROTAC | Standard pipelines |
| Targeted protein degradation in cancer | CRBN or VHL | Standard clinical track |
| Novel target, no cryptic | Multiple E3 / linker variants | Combinatorial design + PRosettaC |
| Molecular glue (non-PROTAC) | CRBN-based | Distinct mechanism; pomalidomide-class |
| Optimize cooperativity (alpha > 1) | PRosettaC iterative; experimental | Ternary MD + ITC |
| Cell-active candidate | Standard development | PK + degradation cellular assays |

## Ternary Complex Prediction Tools

| Tool | Approach | Strength | Fails when |
|------|----------|----------|------------|
| PRosettaC | Rosetta + REMD on PROTAC | Mature; reliable | License Rosetta |
| DeepTernary | Equivariant deep learning | Fast; SE(3) | OOD chemistry |
| AlphaFold3 + constraints | Foundation model + restraints | High accuracy | Public access limited |
| Boltz-1 / Boltz-2 + constraints | Foundation model | Fast; flexible | Limited PROTAC training |
| HADDOCK | Restraint-based MD | Veteran | Manual restraint specification |
| Schrodinger Phase + Glide | Commercial | Production-ready | License cost |

**Decision:** For first-pass ternary modeling, **PRosettaC** is the standard. For prospective generative + ranking, **AlphaFold3** with constraints (if access) or **Boltz-1/2** (open). **DeepTernary** is a fast alternative when validated against benchmark.

## Cooperativity (Alpha)

Cooperativity quantifies how the ternary complex stabilizes (or destabilizes) the binary binding:

```
alpha = (Kd_binary,target) / (Kd_ternary,target)
```

- alpha > 1: positive cooperativity (ternary stronger than binary)
- alpha = 1: no cooperativity (independent binding)
- alpha < 1: negative cooperativity (mutual destabilization)

**Positive cooperativity** (alpha > 2-3) is the gold standard for PROTAC design. It means the ternary complex is stabilized by protein-protein interactions induced by the linker geometry.

Measure with ITC (isothermal titration calorimetry) or SPR/BLI titrations of binary vs ternary.

## DC50 / Dmax Characterization

In cellular assays:
- **DC50**: PROTAC concentration for 50% degradation (analogous to IC50)
- **Dmax**: maximum fraction degraded at any concentration

| Property | Good value | Notes |
|----------|------------|-------|
| DC50 | < 100 nM | Clinically meaningful |
| Dmax | > 80% | Sufficient depletion for phenotype |
| Hook effect | < 10x range | Linear dose-response over 100x |
| Hook concentration | typically > 10x DC50 | Above which binary complexes dominate |

**Hook effect**: at high PROTAC concentrations, binary complexes (PROTAC-target alone, PROTAC-E3 alone) dominate, and ternary complex formation drops. Dose-response curves are bell-shaped.

## Ternary Complex Modeling Workflow

**Goal:** Predict 3D structure of target-PROTAC-E3 ternary complex.

**Approach:**
1. Start with binary co-crystals: target + target-ligand pose; E3 + E3-ligand pose
2. Connect via linker enumeration (combinatorial)
3. Score by geometric feasibility (linker length, no clashes)
4. Refine with energy minimization

```python
# Pseudo-code workflow
def predict_ternary(target_pdb, target_ligand_sdf,
                    e3_pdb, e3_ligand_sdf, linker_smiles):
    # 1. Place binary complexes in same coordinate frame
    # 2. Enumerate linker connectivity from target-ligand exit vector to e3-ligand entry vector
    # 3. Score by total linker length, RMSD to expected geometry
    # 4. Minimize with restraint MD (5-20 ns)
    return ternary_poses
```

For production workflow, use PRosettaC web service or AlphaFold3 with chain-chain constraint inputs.

## Linker Length Calculation

```python
from rdkit import Chem
from rdkit.Chem import AllChem

def linker_distance(target_pose, e3_pose, target_ligand_smi, e3_ligand_smi, n_conf=20):
    """
    Compute distance between target-ligand exit vector and e3-ligand entry vector.
    Linker must span this distance with flexibility.
    """
    # Place target + e3 in same frame (from PRosettaC or HADDOCK)
    # Find target-ligand attachment atom and e3-ligand attachment atom
    # Compute distance
    pass

def required_linker_atoms(distance_A, rigidity='flexible'):
    """
    Estimate atoms needed in linker to span distance.
    Flexible (CC bond ~1.5 A) -> distance / 1.5
    R