time-resolved-cryoem-agent

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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.

1. Conformational Sorting: Classify particles by conformational state.

1. Trajectory Reconstruction: Build conformational trajectories.

1. Intermediate Identification: Detect rare intermediate states.

1. MD Integration: Combine with molecular dynamics simulations.

1. 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.

1. Particle Processing: 3D classification across timepoints.

1. State Assignment: AI-powered conformational sorting.

1. Kinetics Fitting: Extract rate constants.

1. Intermediate Mapping: Identify transient states.

1. Drug Design: Target state-specific pockets.

1. 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:

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 ⇌ AL | 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

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