---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. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-20" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• tpd-ternary-complex-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

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.

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

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

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

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

6. E3 Comparison: Evaluate different E3 ligases for same target.

Supported E3 Ligases

Workflow

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

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

3. Ternary Assembly: Predict full ternary complex structure.

4. MD Refinement: Molecular dynamics for complex stability.

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

6. Lysine Analysis: Map ubiquitination sites.

7. 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

Output Components

Interface Quality Metrics

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

Ubiquitination Site Requirements

E3 Ligase Comparison

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

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

Quality Control

Author

AI Group - Biomedical AI Platform