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---
name: 'time-resolved-cryoem-agent'
description: 'AI-powered time-resolved cryo-EM analysis for capturing protein dynamics, drug-binding kinetics, and conformational transitions for dynamics-based drug discovery.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# Time-Resolved Cryo-EM Agent
The **Time-Resolved Cryo-EM Agent** leverages time-resolved cryo-electron microscopy to capture protein dynamics, drug-binding kinetics, and conformational transitions. It integrates AI-powered analysis with experimental time-resolved data to enable dynamics-based drug discovery, moving beyond static structures to understand drug mechanisms in motion.
## When to Use This Skill
* When studying drug-binding kinetics structurally.
* For capturing protein conformational transitions.
* To understand allosteric mechanisms and dynamics.
* When designing drugs targeting specific conformational states.
* For characterizing enzyme catalytic cycles.
## Core Capabilities
1. **Kinetics Extraction**: Extract binding kinetics from time-resolved data.
2. **Conformational Sorting**: Classify particles by conformational state.
3. **Trajectory Reconstruction**: Build conformational trajectories.
4. **Intermediate Identification**: Detect rare intermediate states.
5. **MD Integration**: Combine with molecular dynamics simulations.
6. **Dynamics-Based Design**: Design drugs targeting specific states.
## Time-Resolved Methods
| Method | Timescale | Resolution | Application |
|--------|-----------|------------|-------------|
| Rapid Mixing | ms-s | 3-4 Å | Ligand binding |
| Temperature Jump | μs-ms | 3-5 Å | Transitions |
| Photocaging | μs-ms | 3-5 Å | Triggered reactions |
| Flow-Mixing | 10ms-s | 3-4 Å | Enzyme kinetics |
## Workflow
1. **Input**: Time-resolved cryo-EM datasets, protein sequence.
2. **Particle Processing**: 3D classification across timepoints.
3. **State Assignment**: AI-powered conformational sorting.
4. **Kinetics Fitting**: Extract rate constants.
5. **Intermediate Mapping**: Identify transient states.
6. **Drug Design**: Target state-specific pockets.
7. **Output**: Kinetic models, conformational movie, design targets.
## Example Usage
**User**: "Analyze time-resolved cryo-EM data of this kinase to understand drug binding kinetics and identify targetable intermediate states."
**Agent Action**:
```bash
python3 Skills/Structural_Biology/Time_Resolved_CryoEM_Agent/analyze_dynamics.py \
--timepoints "0ms,10ms,50ms,100ms,500ms,1s" \
--particle_stacks timepoint_particles/ \
--protein_sequence kinase.fasta \
--ligand drug_compound.sdf \
--kinetics_model two_state \
--extract_intermediates true \
--output kinase_dynamics/
```
## Input Requirements
| Input | Format | Purpose |
|-------|--------|---------|
| Particle Stacks | MRC per timepoint | Time-resolved data |
| Timepoint Labels | CSV | Time assignments |
| Protein Sequence | FASTA | Structure reference |
| Ligand Structure | SDF | Binding analysis |
| Initial Model | Optional PDB | 3D classification |
## Output Components
| Output | Description | Format |
|--------|-------------|--------|
| Conformational States | Per-timepoint structures | .pdb |
| Kinetics Parameters | kon, koff, Kd | .json |
| State Populations | Fraction vs time | .csv |
| Conformational Movie | Trajectory animation | .mp4 |
| Intermediate Structures | Transient states | .pdb |
| Energy Landscape | Free energy surface | .png |
| Drug Design Targets | State-specific pockets | .json |
## Kinetics Analysis
| Parameter | Definition | Drug Design Relevance |
|-----------|------------|----------------------|
| kon | Association rate | Target engagement speed |
| koff | Dissociation rate | Residence time |
| Kd | Equilibrium constant | Affinity |
| t1/2 | Half-life | Duration of action |
| Conformational Rate | State transition speed | Mechanism insight |
## AI/ML Components
**Conformational Sorting**:
- 3D variational autoencoders
- Heterogeneous reconstruction
- Continuous conformational analysis (cryoDRGN)
**Kinetics Modeling**:
- Hidden Markov models
- Bayesian kinetics fitting
- Deep learning rate estimation
**Intermediate Detection**:
- Rare event identification
- Manifold learning
- Transition path sampling
## Drug Discovery Applications
| Application | Dynamic Insight | Design Strategy |
|-------------|-----------------|-----------------|
| Slow Binding | Long residence time | Optimize koff |
| Allosteric Drugs | State stabilization | Target intermediate |
| Covalent Inhibitors | Binding trajectory | Optimize approach |
| Conformational Selection | State preference | Pre-organize ligand |
| Induced Fit | Protein reorganization | Accommodate flexibility |
## Prerequisites
* Python 3.10+
* cryoSPARC, RELION
* cryoDRGN
* GROMACS/OpenMM
* PyTorch
## Related Skills
* CryoEM_AI_Drug_Design_Agent - Static structure design
* Molecular_Dynamics_Agent - MD simulations
* AlphaFold3_Agent - Structure prediction
* PROTAC_Design_Agent - Degrader design
## Conformational Analysis Methods
| Method | Software | Best For |
|--------|----------|----------|
| 3DVA | cryoSPARC | Principal motions |
| Multi-body | RELION | Domain movements |
| cryoDRGN | cryoDRGN | Continuous heterogeneity |
| 3D Classification | Various | Discrete states |
## Time Resolution Capabilities
| Mixing Method | Dead Time | Applications |
|---------------|-----------|--------------|
| Rapid On-Grid | ~10 ms | Fast binding |
| Blot-Free | ~1 ms | Very fast kinetics |
| Microfluidic | ~50 ms | Enzyme catalysis |
| Spray-Mixing | ~10 ms | Protein-protein |
## Special Considerations
1. **Sample Consumption**: Time-resolved requires more sample
2. **Synchronization**: Initiation must be well-controlled
3. **Resolution Trade-off**: Fewer particles per timepoint
4. **Intermediate Lifetime**: Must match experimental timescale
5. **Data Quality**: Requires high-quality data collection
## Kinetic Mechanisms
| Mechanism | Model | Parameters |
|-----------|-------|------------|
| Two-State | A ⇌ B | kon, koff |
| Induced Fit | A + L ⇌ AL ⇌ AL* | Multiple rates |
| Conformational Selection | A ⇌ A* + L ⇌ A*L | Pre-equilibrium |
| Sequential | A → B → C | Multiple intermediates |
## Validation Approaches
| Method | Purpose | Complementarity |
|--------|---------|-----------------|
| SPR | Binding kinetics | Validate rates |
| ITC | Thermodynamics | Validate ΔG |
| NMR | Dynamics | Solution behavior |
| MD Simulation | Mechanism | Molecular detail |
## Applications in Drug Discovery
| Target | Dynamic Insight | Design Implication |
|--------|-----------------|-------------------|
| Kinases | DFG-in/out transition | State-selective inhibitors |
| GPCRs | Activation pathway | Biased agonists |
| Transporters | Alternating access | Mechanism-based design |
| ATPases | Catalytic cycle | Allosteric inhibitors |
## Author
AI Group - Biomedical AI Platform
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
