File contents 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
Kinetics Extraction : Extract binding kinetics from time-resolved data.
Conformational Sorting : Classify particles by conformational state.
Trajectory Reconstruction : Build conformational trajectories.
Intermediate Identification : Detect rare intermediate states.
MD Integration : Combine with molecular dynamics simulations.
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
Input : Time-resolved cryo-EM datasets, protein sequence.
Particle Processing : 3D classification across timepoints.
State Assignment : AI-powered conformational sorting.
Kinetics Fitting : Extract rate constants.
Intermediate Mapping : Identify transient states.
Drug Design : Target state-specific pockets.
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
Sample Consumption : Time-resolved requires more sampleSynchronization : Initiation must be well-controlledResolution Trade-off : Fewer particles per timepointIntermediate Lifetime : Must match experimental timescaleData 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
1 --- 2 name: time-resolved-cryoem-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: 'time-resolved-cryoem-agent' 20 description: 'AI-powered time-resolved cryo-EM analysis for capturing protein dynamics, drug-binding kinetics, and conformational transitions for dynamics-based drug discovery.' 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 # Time-Resolved Cryo-EM Agent 29 30 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. 31 32 ## When to Use This Skill 33 34 * When studying drug-binding kinetics structurally. 35 * For capturing protein conformational transitions. 36 * To understand allosteric mechanisms and dynamics. 37 * When designing drugs targeting specific conformational states. 38 * For characterizing enzyme catalytic cycles. 39 40 ## Core Capabilities 41 42 1. **Kinetics Extraction**: Extract binding kinetics from time-resolved data. 43 44 2. **Conformational Sorting**: Classify particles by conformational state. 45 46 3. **Trajectory Reconstruction**: Build conformational trajectories. 47 48 4. **Intermediate Identification**: Detect rare intermediate states. 49 50 5. **MD Integration**: Combine with molecular dynamics simulations. 51 52 6. **Dynamics-Based Design**: Design drugs targeting specific states. 53 54 ## Time-Resolved Methods 55 56 | Method | Timescale | Resolution | Application | 57 |--------|-----------|------------|-------------| 58 | Rapid Mixing | ms-s | 3-4 Å | Ligand binding | 59 | Temperature Jump | μs-ms | 3-5 Å | Transitions | 60 | Photocaging | μs-ms | 3-5 Å | Triggered reactions | 61 | Flow-Mixing | 10ms-s | 3-4 Å | Enzyme kinetics | 62 63 ## Workflow 64 65 1. **Input**: Time-resolved cryo-EM datasets, protein sequence. 66 67 2. **Particle Processing**: 3D classification across timepoints. 68 69 3. **State Assignment**: AI-powered conformational sorting. 70 71 4. **Kinetics Fitting**: Extract rate constants. 72 73 5. **Intermediate Mapping**: Identify transient states. 74 75 6. **Drug Design**: Target state-specific pockets. 76 77 7. **Output**: Kinetic models, conformational movie, design targets. 78 79 ## Example Usage 80 81 **User**: "Analyze time-resolved cryo-EM data of this kinase to understand drug binding kinetics and identify targetable intermediate states." 82 83 **Agent Action**: 84 ```bash 85 python3 Skills/Structural_Biology/Time_Resolved_CryoEM_Agent/analyze_dynamics.py \ 86 --timepoints "0ms,10ms,50ms,100ms,500ms,1s" \ 87 --particle_stacks timepoint_particles/ \ 88 --protein_sequence kinase.fasta \ 89 --ligand drug_compound.sdf \ 90 --kinetics_model two_state \ 91 --extract_intermediates true \ 92 --output kinase_dynamics/ 93 ``` 94 95 ## Input Requirements 96 97 | Input | Format | Purpose | 98 |-------|--------|---------| 99 | Particle Stacks | MRC per timepoint | Time-resolved data | 100 | Timepoint Labels | CSV | Time assignments | 101 | Protein Sequence | FASTA | Structure reference | 102 | Ligand Structure | SDF | Binding analysis | 103 | Initial Model | Optional PDB | 3D classification | 104 105 ## Output Components 106 107 | Output | Description | Format | 108 |--------|-------------|--------| 109 | Conformational States | Per-timepoint structures | .pdb | 110 | Kinetics Parameters | kon, koff, Kd | .json | 111 | State Populations | Fraction vs time | .csv | 112 | Conformational Movie | Trajectory animation | .mp4 | 113 | Intermediate Structures | Transient states | .pdb | 114 | Energy Landscape | Free energy surface | .png | 115 | Drug Design Targets | State-specific pockets | .json | 116 117 ## Kinetics Analysis 118 119 | Parameter | Definition | Drug Design Relevance | 120 |-----------|------------|----------------------| 121 | kon | Association rate | Target engagement speed | 122 | koff | Dissociation rate | Residence time | 123 | Kd | Equilibrium constant | Affinity | 124 | t1/2 | Half-life | Duration of action | 125 | Conformational Rate | State transition speed | Mechanism insight | 126 127 ## AI/ML Components 128 129 **Conformational Sorting**: 130 - 3D variational autoencoders 131 - Heterogeneous reconstruction 132 - Continuous conformational analysis (cryoDRGN) 133 134 **Kinetics Modeling**: 135 - Hidden Markov models 136 - Bayesian kinetics fitting 137 - Deep learning rate estimation 138 139 **Intermediate Detection**: 140 - Rare event identification 141 - Manifold learning 142 - Transition path sampling 143 144 ## Drug Discovery Applications 145 146 | Application | Dynamic Insight | Design Strategy | 147 |-------------|-----------------|-----------------| 148 | Slow Binding | Long residence time | Optimize koff | 149 | Allosteric Drugs | State stabilization | Target intermediate | 150 | Covalent Inhibitors | Binding trajectory | Optimize approach | 151 | Conformational Selection | State preference | Pre-organize ligand | 152 | Induced Fit | Protein reorganization | Accommodate flexibility | 153 154 ## Prerequisites 155 156 * Python 3.10+ 157 * cryoSPARC, RELION 158 * cryoDRGN 159 * GROMACS/OpenMM 160 * PyTorch 161 162 ## Related Skills 163 164 * CryoEM_AI_Drug_Design_Agent - Static structure design 165 * Molecular_Dynamics_Agent - MD simulations 166 * AlphaFold3_Agent - Structure prediction 167 * PROTAC_Design_Agent - Degrader design 168 169 ## Conformational Analysis Methods 170 171 | Method | Software | Best For | 172 |--------|----------|----------| 173 | 3DVA | cryoSPARC | Principal motions | 174 | Multi-body | RELION | Domain movements | 175 | cryoDRGN | cryoDRGN | Continuous heterogeneity | 176 | 3D Classification | Various | Discrete states | 177 178 ## Time Resolution Capabilities 179 180 | Mixing Method | Dead Time | Applications | 181 |---------------|-----------|--------------| 182 | Rapid On-Grid | ~10 ms | Fast binding | 183 | Blot-Free | ~1 ms | Very fast kinetics | 184 | Microfluidic | ~50 ms | Enzyme catalysis | 185 | Spray-Mixing | ~10 ms | Protein-protein | 186 187 ## Special Considerations 188 189 1. **Sample Consumption**: Time-resolved requires more sample 190 2. **Synchronization**: Initiation must be well-controlled 191 3. **Resolution Trade-off**: Fewer particles per timepoint 192 4. **Intermediate Lifetime**: Must match experimental timescale 193 5. **Data Quality**: Requires high-quality data collection 194 195 ## Kinetic Mechanisms 196 197 | Mechanism | Model | Parameters | 198 |-----------|-------|------------| 199 | Two-State | A ⇌ B | kon, koff | 200 | Induced Fit | A + L ⇌ AL ⇌ AL* | Multiple rates | 201 | Conformational Selection | A ⇌ A* + L ⇌ A*L | Pre-equilibrium | 202 | Sequential | A → B → C | Multiple intermediates | 203 204 ## Validation Approaches 205 206 | Method | Purpose | Complementarity | 207 |--------|---------|-----------------| 208 | SPR | Binding kinetics | Validate rates | 209 | ITC | Thermodynamics | Validate ΔG | 210 | NMR | Dynamics | Solution behavior | 211 | MD Simulation | Mechanism | Molecular detail | 212 213 ## Applications in Drug Discovery 214 215 | Target | Dynamic Insight | Design Implication | 216 |--------|-----------------|-------------------| 217 | Kinases | DFG-in/out transition | State-selective inhibitors | 218 | GPCRs | Activation pathway | Biased agonists | 219 | Transporters | Alternating access | Mechanism-based design | 220 | ATPases | Catalytic cycle | Allosteric inhibitors | 221 222 ## Author 223 224 AI Group - Biomedical AI Platform 225 226 227 <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
commit 56c5013159
Frequently asked questions How do I install the Time Resolved Cryoem Agent skill? Run npx skillmds@latest add biotender-max/time-resolved-cryoem-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 Time Resolved Cryoem Agent skill do? <!-- It is listed under AI & ML on SkillMD.
Is Time Resolved Cryoem 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 Time Resolved Cryoem 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 Time Resolved Cryoem Agent free to use? Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.
Who published Time Resolved Cryoem Agent? BioTender-max (@biotender-max) published this skill. Their other Agent Skills are listed on their SkillMD profile.
BioTender-max