File contents 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
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 possibleLinker Dynamics : Flexible linkers sample many geometriesInduced Fit : Proteins may reorganize upon complex formationCrystal Packing : May influence observed geometriesKinetic 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
1 --- 2 name: tpd-ternary-complex-agent 3 description: <!-- 4 --- 5 <!-- 6 # COPYRIGHT NOTICE 7 # This file is part of the "Universal Biomedical Skills" project. 8 # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> 9 # All Rights Reserved. 10 # 11 # This code is proprietary and confidential. 12 # Unauthorized copying of this file, via any medium is strictly prohibited. 13 # 14 # Provenance: Authenticated by MD BABU MIA 15 16 --> 17 18 --- 19 name: 'tpd-ternary-complex-agent' 20 description: 'AI-powered ternary complex prediction for targeted protein degradation, modeling POI-degrader-E3 ligase assemblies to optimize PROTAC and molecular glue efficacy.' 21 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. 22 allowed-tools: 23 - read_file 24 - run_shell_command 25 --- 26 27 28 # TPD Ternary Complex Agent 29 30 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. 31 32 ## When to Use This Skill 33 34 * When predicting ternary complex formation for degrader design. 35 * For understanding POI-E3 interface complementarity. 36 * To optimize linker geometry based on complex structure. 37 * When assessing ubiquitination site accessibility. 38 * For comparing E3 ligase options for a target. 39 40 ## Core Capabilities 41 42 1. **Ternary Structure Prediction**: Model full POI-degrader-E3 complexes. 43 44 2. **Interface Analysis**: Assess protein-protein interactions in complex. 45 46 3. **Linker Geometry Optimization**: Guide linker design from structures. 47 48 4. **Ubiquitination Site Analysis**: Identify accessible lysines for Ub transfer. 49 50 5. **Cooperativity Scoring**: Predict binding cooperativity (α factor). 51 52 6. **E3 Comparison**: Evaluate different E3 ligases for same target. 53 54 ## Supported E3 Ligases 55 56 | E3 Ligase | Structure | Complex Quality | 57 |-----------|-----------|-----------------| 58 | CRBN-DDB1-CUL4A | High resolution | Excellent | 59 | VHL-ELOB-ELOC-CUL2 | High resolution | Excellent | 60 | MDM2 | Good | Good | 61 | IAP (cIAP1/XIAP) | Moderate | Moderate | 62 | DCAF15-DDB1 | Emerging | Developing | 63 | KEAP1 | High resolution | Good | 64 65 ## Workflow 66 67 1. **Input**: POI structure, degrader, E3 ligase specification. 68 69 2. **Binary Modeling**: Model POI-warhead and E3-ligand complexes. 70 71 3. **Ternary Assembly**: Predict full ternary complex structure. 72 73 4. **MD Refinement**: Molecular dynamics for complex stability. 74 75 5. **Interface Scoring**: Quantify POI-E3 interface quality. 76 77 6. **Lysine Analysis**: Map ubiquitination sites. 78 79 7. **Output**: Ternary structure, scores, optimization suggestions. 80 81 ## Example Usage 82 83 **User**: "Model the ternary complex for this BRD4 PROTAC with VHL to understand the protein-protein interface." 84 85 **Agent Action**: 86 ```bash 87 python3 Skills/Drug_Discovery/TPD_Ternary_Complex_Agent/predict_ternary.py \ 88 --poi_structure brd4_bd1.pdb \ 89 --warhead_pose brd4_warhead_docked.sdf \ 90 --e3_ligase VHL \ 91 --e3_ligand vhl_ligand.sdf \ 92 --protac_smiles "PROTAC_SMILES_STRING" \ 93 --linker_conformations 100 \ 94 --md_refinement true \ 95 --output ternary_complex_results/ 96 ``` 97 98 ## Ternary Complex Scoring 99 100 | Score Component | Weight | Interpretation | 101 |-----------------|--------|----------------| 102 | Interface Area | 20% | Larger = more stable | 103 | Shape Complementarity | 25% | Better fit = stability | 104 | Electrostatics | 20% | Charge matching | 105 | Linker Strain | 15% | Lower = better geometry | 106 | Complex Stability (ΔG) | 20% | Favorable energetics | 107 108 ## Output Components 109 110 | Output | Description | Format | 111 |--------|-------------|--------| 112 | Ternary Structure | POI-PROTAC-E3 model | .pdb | 113 | Confidence Scores | pLDDT, PAE | .json | 114 | Interface Map | Contact residues | .csv | 115 | Lysine Accessibility | Ubiquitination sites | .csv | 116 | Cooperativity | α factor estimate | .json | 117 | Optimization Suggestions | Design recommendations | .md | 118 | MD Trajectory | Stability simulation | .xtc | 119 120 ## Interface Quality Metrics 121 122 | Metric | Definition | Good Value | 123 |--------|------------|------------| 124 | Buried Surface Area | Contact area | >800 Ų | 125 | Shape Complementarity | Sc score | >0.65 | 126 | Gap Volume Index | Interface packing | <2.0 | 127 | Hydrogen Bonds | Intermolecular H-bonds | >3 | 128 | Salt Bridges | Charged interactions | >1 | 129 130 ## AI/ML Components 131 132 **Structure Prediction**: 133 - AlphaFold-Multimer for ternary modeling 134 - Template-based homology 135 - Deep learning interface prediction 136 137 **Conformational Sampling**: 138 - Linker conformer generation 139 - Ensemble docking 140 - MD for dynamics 141 142 **Scoring Functions**: 143 - Physics-based energy 144 - ML-derived interface scores 145 - Cooperativity prediction models 146 147 ## Cooperativity Analysis 148 149 | α Factor | Interpretation | Mechanism | 150 |----------|----------------|-----------| 151 | α > 1 | Positive cooperativity | E3 binding enhances POI binding | 152 | α = 1 | No cooperativity | Independent binding | 153 | α < 1 | Negative cooperativity | E3 binding reduces POI binding | 154 155 ## Ubiquitination Site Requirements 156 157 | Requirement | Threshold | Rationale | 158 |-------------|-----------|-----------| 159 | Surface Accessibility | >30 Ų | E2 access | 160 | Distance to E2~Ub | <15 Å | Transfer distance | 161 | Lysine Environment | Favorable | Not buried | 162 | Number of Sites | ≥1 | At least one Lys | 163 164 ## E3 Ligase Comparison 165 166 | E3 | Advantages | Considerations | 167 |----|------------|----------------| 168 | CRBN | Broad applicability, many ligands | Some immune targets | 169 | VHL | High selectivity, well-validated | Limited tissue in some organs | 170 | MDM2 | No CRBN competition | Fewer validated targets | 171 | IAP | Cancer expression, dual mechanism | Complex biology | 172 173 ## Prerequisites 174 175 * Python 3.10+ 176 * AlphaFold-Multimer 177 * GROMACS/OpenMM for MD 178 * RDKit, BioPython 179 * GPU compute (recommended) 180 181 ## Related Skills 182 183 * PROTAC_Design_Agent - Full PROTAC design 184 * Molecular_Glue_Discovery_Agent - Glue discovery 185 * Protein_Protein_Docking_Agent - PPI docking 186 * Molecular_Dynamics_Agent - MD simulations 187 188 ## Validation Approaches 189 190 | Method | Purpose | Confidence | 191 |--------|---------|------------| 192 | Crystal Structure | Ground truth | Highest | 193 | Cryo-EM | Large complexes | High | 194 | HDX-MS | Interface mapping | Moderate-High | 195 | Crosslinking MS | Distance constraints | Moderate | 196 | Mutagenesis | Interface validation | Functional | 197 198 ## Special Considerations 199 200 1. **Conformational Flexibility**: Multiple ternary conformations possible 201 2. **Linker Dynamics**: Flexible linkers sample many geometries 202 3. **Induced Fit**: Proteins may reorganize upon complex formation 203 4. **Crystal Packing**: May influence observed geometries 204 5. **Kinetic vs Thermodynamic**: Ternary stability ≠ degradation efficiency 205 206 ## Design Implications 207 208 | Structural Finding | Design Action | 209 |--------------------|---------------| 210 | Poor interface | Change E3 or target site | 211 | Long distance | Longer linker | 212 | Steric clash | Shorter linker or different exit vector | 213 | No accessible Lys | Different binding mode | 214 | High flexibility | Constrained linker | 215 216 ## Quality Control 217 218 | QC Metric | Threshold | Interpretation | 219 |-----------|-----------|----------------| 220 | pLDDT (interface) | >70 | Reliable prediction | 221 | PAE (POI-E3) | <10 Å | Good relative positioning | 222 | MD RMSD | <3 Å | Stable complex | 223 | Clash Score | <50 | Good packing | 224 225 ## Author 226 227 AI Group - Biomedical AI Platform 228 229 230 <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
commit bdba1d2a43
Frequently asked questions How do I install the Tpd Ternary Complex Agent skill? Run npx skillmds@latest add biotender-max/tpd-ternary-complex-agent in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.
What does the Tpd Ternary Complex Agent skill do? <!-- It is listed under AI & ML on SkillMD.
Is Tpd Ternary Complex Agent safe to use? This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.
Which AI agents work with Tpd Ternary Complex Agent? This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.
Is Tpd Ternary Complex Agent free to use? Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.
Who published Tpd Ternary Complex Agent? BioTender-max (@biotender-max) published this skill. Their other Agent Skills are listed on their SkillMD profile.
BioTender-max