FridrichMethod TPD Ternary Complex Agent
The TPD Ternary Complex Agent specializes in predicting and modeling ternary complex formation for targeted protein degradation (TPD). It uses AlphaFold-Multimer, molecular dynamics, and deep learning to model Protein of Interest (POI)-degrader-E3 ligase assemblies, enabling rational optimization of PROTACs and molecular glues.
When to Use This Skill
- When predicting ternary complex formation for degrader design.
- For understanding POI-E3 interface complementarity.
- To optimize linker geometry based on complex structure.
- When assessing ubiquitination site accessibility.
- For comparing E3 ligase options for a target.
Core Capabilities
Ternary Structure Prediction: Model full POI-degrader-E3 complexes.
Interface Analysis: Assess protein-protein interactions in complex.
Linker Geometry Optimization: Guide linker design from structures.
Ubiquitination Site Analysis: Identify accessible lysines for Ub transfer.
Cooperativity Scoring: Predict binding cooperativity (α factor).
E3 Comparison: Evaluate different E3 ligases for same target.
Supported E3 Ligases
| E3 Ligase | Structure | Complex Quality |
|---|---|---|
| CRBN-DDB1-CUL4A | High resolution | Excellent |
| VHL-ELOB-ELOC-CUL2 | High resolution | Excellent |
| MDM2 | Good | Good |
| IAP (cIAP1/XIAP) | Moderate | Moderate |
| DCAF15-DDB1 | Emerging | Developing |
| KEAP1 | High resolution | Good |
Workflow
Input: POI structure, degrader, E3 ligase specification.
Binary Modeling: Model POI-warhead and E3-ligand complexes.
Ternary Assembly: Predict full ternary complex structure.
MD Refinement: Molecular dynamics for complex stability.
Interface Scoring: Quantify POI-E3 interface quality.
Lysine Analysis: Map ubiquitination sites.
Output: Ternary structure, scores, optimization suggestions.
Example Usage
User: "Model the ternary complex for this BRD4 PROTAC with VHL to understand the protein-protein interface."
Agent Action:
python3 Skills/Drug_Discovery/TPD_Ternary_Complex_Agent/predict_ternary.py \
--poi_structure brd4_bd1.pdb \
--warhead_pose brd4_warhead_docked.sdf \
--e3_ligase VHL \
--e3_ligand vhl_ligand.sdf \
--protac_smiles "PROTAC_SMILES_STRING" \
--linker_conformations 100 \
--md_refinement true \
--output ternary_complex_results/
Ternary Complex Scoring
| Score Component | Weight | Interpretation |
|---|---|---|
| Interface Area | 20% | Larger = more stable |
| Shape Complementarity | 25% | Better fit = stability |
| Electrostatics | 20% | Charge matching |
| Linker Strain | 15% | Lower = better geometry |
| Complex Stability (ΔG) | 20% | Favorable energetics |
Output Components
| Output | Description | Format |
|---|---|---|
| Ternary Structure | POI-PROTAC-E3 model | .pdb |
| Confidence Scores | pLDDT, PAE | .json |
| Interface Map | Contact residues | .csv |
| Lysine Accessibility | Ubiquitination sites | .csv |
| Cooperativity | α factor estimate | .json |
| Optimization Suggestions | Design recommendations | .md |
| MD Trajectory | Stability simulation | .xtc |
Interface Quality Metrics
| Metric | Definition | Good Value |
|---|---|---|
| Buried Surface Area | Contact area | >800 Ų |
| Shape Complementarity | Sc score | >0.65 |
| Gap Volume Index | Interface packing | <2.0 |
| Hydrogen Bonds | Intermolecular H-bonds | >3 |
| Salt Bridges | Charged interactions | >1 |
AI/ML Components
Structure Prediction:
- AlphaFold-Multimer for ternary modeling
- Template-based homology
- Deep learning interface prediction
Conformational Sampling:
- Linker conformer generation
- Ensemble docking
- MD for dynamics
Scoring Functions:
- Physics-based energy
- ML-derived interface scores
- Cooperativity prediction models
Cooperativity Analysis
| α Factor | Interpretation | Mechanism |
|---|---|---|
| α > 1 | Positive cooperativity | E3 binding enhances POI binding |
| α = 1 | No cooperativity | Independent binding |
| α < 1 | Negative cooperativity | E3 binding reduces POI binding |
Ubiquitination Site Requirements
| Requirement | Threshold | Rationale |
|---|---|---|
| Surface Accessibility | >30 Ų | E2 access |
| Distance to E2~Ub | <15 Å | Transfer distance |
| Lysine Environment | Favorable | Not buried |
| Number of Sites | ≥1 | At least one Lys |
E3 Ligase Comparison
| E3 | Advantages | Considerations |
|---|---|---|
| CRBN | Broad applicability, many ligands | Some immune targets |
| VHL | High selectivity, well-validated | Limited tissue in some organs |
| MDM2 | No CRBN competition | Fewer validated targets |
| IAP | Cancer expression, dual mechanism | Complex biology |
Prerequisites
- Python 3.10+
- AlphaFold-Multimer
- GROMACS/OpenMM for MD
- RDKit, BioPython
- GPU compute (recommended)
Related Skills
- PROTAC_Design_Agent - Full PROTAC design
- Molecular_Glue_Discovery_Agent - Glue discovery
- Protein_Protein_Docking_Agent - PPI docking
- Molecular_Dynamics_Agent - MD simulations
Validation Approaches
| Method | Purpose | Confidence |
|---|---|---|
| Crystal Structure | Ground truth | Highest |
| Cryo-EM | Large complexes | High |
| HDX-MS | Interface mapping | Moderate-High |
| Crosslinking MS | Distance constraints | Moderate |
| Mutagenesis | Interface validation | Functional |
Special Considerations
- Conformational Flexibility: Multiple ternary conformations possible
- Linker Dynamics: Flexible linkers sample many geometries
- Induced Fit: Proteins may reorganize upon complex formation
- Crystal Packing: May influence observed geometries
- Kinetic vs Thermodynamic: Ternary stability ≠ degradation efficiency
Design Implications
| Structural Finding | Design Action |
|---|---|
| Poor interface | Change E3 or target site |
| Long distance | Longer linker |
| Steric clash | Shorter linker or different exit vector |
| No accessible Lys | Different binding mode |
| High flexibility | Constrained linker |
Quality Control
| QC Metric | Threshold | Interpretation |
|---|---|---|
| pLDDT (interface) | >70 | Reliable prediction |
| PAE (POI-E3) | <10 Å | Good relative positioning |
| MD RMSD | <3 Å | Stable complex |
| Clash Score | <50 | Good packing |
Author
AI Group - Biomedical AI Platform