tpd-ternary-complex-agent

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name: 'tpd-ternary-complex-agent'

description: 'AI-powered ternary complex prediction for targeted protein degradation, modeling POI-degrader-E3 ligase assemblies to optimize PROTAC and molecular glue efficacy.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

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

1. Ternary Structure Prediction: Model full POI-degrader-E3 complexes.

1. Interface Analysis: Assess protein-protein interactions in complex.

1. Linker Geometry Optimization: Guide linker design from structures.

1. Ubiquitination Site Analysis: Identify accessible lysines for Ub transfer.

1. Cooperativity Scoring: Predict binding cooperativity (α factor).

1. 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

1. Input: POI structure, degrader, E3 ligase specification.

1. Binary Modeling: Model POI-warhead and E3-ligand complexes.

1. Ternary Assembly: Predict full ternary complex structure.

1. MD Refinement: Molecular dynamics for complex stability.

1. Interface Scoring: Quantify POI-E3 interface quality.

1. Lysine Analysis: Map ubiquitination sites.

1. 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

1. Conformational Flexibility: Multiple ternary conformations possible
2. Linker Dynamics: Flexible linkers sample many geometries
3. Induced Fit: Proteins may reorganize upon complex formation
4. Crystal Packing: May influence observed geometries
5. 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

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