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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

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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

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.

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

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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