Time Resolved CryoEM Agent

---
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.'
---


# 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