Coarse-Grained Molecular Dynamics
Purpose
Run µs–ms scale MD simulations using coarse-grained force fields.
Primary use cases: membrane self-assembly, protein-membrane interactions,
lipid nanoparticles, large conformational changes, crowding effects.
When to Use This Skill
- Simulating lipid bilayers, vesicles, or membrane proteins
- Accessing timescales (µs–ms) beyond all-atom MD reach
- Screening protein-membrane binding or insertion
- Studying large-scale conformational changes (IDPs, domain motion)
- Building membrane systems for subsequent AA MD (backmapping)
- Coarse-grained small molecule parameterization (MARTINI)
Reference Files
| File |
Content |
references/cg-theory.md |
CG resolution levels, mapping schemes, Boltzmann inversion, force matching, MARTINI 3 philosophy, bead types, scaling factors |
references/martini-proteins.md |
martinize2, elastic network (ElNeDyn), Go-MARTINI, OpenMM/GROMACS protein CG setup, common pitfalls |
references/martini-membranes.md |
Lipid library, insane.py membrane builder, CHARMM-GUI CG, protein-membrane embedding, lipid mixing |
references/cgmd-workflows.md |
GROMACS CG workflow (mdp parameters, timestep, thermostat), OpenMM CG, backmapping (backward.py), equilibration protocol |
references/cg-analysis.md |
MDAnalysis CG trajectories, membrane thickness/APL/order parameters, lateral diffusion, protein CG RMSD/RMSF, density profiles |
Quick Routing
"Set up a lipid bilayer simulation" → martini-membranes.md
"Convert my protein to MARTINI CG" → martini-proteins.md
"Run a CG simulation in GROMACS" → cgmd-workflows.md
"Backmap CG structure to all-atom" → cgmd-workflows.md (backward.py section)
"Analyze membrane properties from CG trajectory" → cg-analysis.md
"What resolution should I use?" → cg-theory.md
Key Numbers (MARTINI 3)
| Property |
Value |
| Mapping ratio |
~4 heavy atoms per bead |
| Timestep (default) |
20 fs (safe: 10–30 fs) |
| Time scaling factor |
×4 (CG time ≈ 4× real time) |
| vdW cutoff |
1.1 nm |
| Electrostatics cutoff |
1.1 nm |
| Recommended thermostat |
v-rescale (τ=1 ps) |
| Recommended barostat |
Parrinello-Rahman (τ=12 ps) |
| Effective timestep |
80 fs (20 fs × 4 scaling) |
| Accessible timescale |
µs per day (GPU) |
Integration with ALKYL Skills
- AA structure for CG input:
homology-modeling or force-fields skill
- Post-backmapping refinement:
force-fields skill (OpenMM minimization)
- Trajectory analysis:
mdanalysis skill (most tools work on CG trajectories)
- Membrane-protein docking: informed by CG binding mode
1---2name: coarse-grained3description: Use when running coarse-grained (CG) molecular dynamics with MARTINI 3. Covers protein CG with martinize2, membrane assembly with insane.py (POPC/POPE/CHOL bilayers), GROMACS CG workflows, backmapping CG to all-atom, and CG membrane analysis.4---56# Coarse-Grained Molecular Dynamics78## Purpose9Run µs–ms scale MD simulations using coarse-grained force fields.10Primary use cases: membrane self-assembly, protein-membrane interactions,11lipid nanoparticles, large conformational changes, crowding effects.1213## When to Use This Skill14- Simulating lipid bilayers, vesicles, or membrane proteins15- Accessing timescales (µs–ms) beyond all-atom MD reach16- Screening protein-membrane binding or insertion17- Studying large-scale conformational changes (IDPs, domain motion)18- Building membrane systems for subsequent AA MD (backmapping)19- Coarse-grained small molecule parameterization (MARTINI)2021## Reference Files2223| File | Content |24|------|---------|25| `references/cg-theory.md` | CG resolution levels, mapping schemes, Boltzmann inversion, force matching, MARTINI 3 philosophy, bead types, scaling factors |26| `references/martini-proteins.md` | martinize2, elastic network (ElNeDyn), Go-MARTINI, OpenMM/GROMACS protein CG setup, common pitfalls |27| `references/martini-membranes.md` | Lipid library, insane.py membrane builder, CHARMM-GUI CG, protein-membrane embedding, lipid mixing |28| `references/cgmd-workflows.md` | GROMACS CG workflow (mdp parameters, timestep, thermostat), OpenMM CG, backmapping (backward.py), equilibration protocol |29| `references/cg-analysis.md` | MDAnalysis CG trajectories, membrane thickness/APL/order parameters, lateral diffusion, protein CG RMSD/RMSF, density profiles |3031## Quick Routing3233**"Set up a lipid bilayer simulation"** → `martini-membranes.md`3435**"Convert my protein to MARTINI CG"** → `martini-proteins.md`3637**"Run a CG simulation in GROMACS"** → `cgmd-workflows.md`3839**"Backmap CG structure to all-atom"** → `cgmd-workflows.md` (backward.py section)4041**"Analyze membrane properties from CG trajectory"** → `cg-analysis.md`4243**"What resolution should I use?"** → `cg-theory.md`4445## Key Numbers (MARTINI 3)4647| Property | Value |48|----------|-------|49| Mapping ratio | ~4 heavy atoms per bead |50| Timestep (default) | 20 fs (safe: 10–30 fs) |51| Time scaling factor | ×4 (CG time ≈ 4× real time) |52| vdW cutoff | 1.1 nm |53| Electrostatics cutoff | 1.1 nm |54| Recommended thermostat | v-rescale (τ=1 ps) |55| Recommended barostat | Parrinello-Rahman (τ=12 ps) |56| Effective timestep | 80 fs (20 fs × 4 scaling) |57| Accessible timescale | µs per day (GPU) |5859## Integration with ALKYL Skills60- AA structure for CG input: `homology-modeling` or `force-fields` skill61- Post-backmapping refinement: `force-fields` skill (OpenMM minimization)62- Trajectory analysis: `mdanalysis` skill (most tools work on CG trajectories)63- Membrane-protein docking: informed by CG binding mode
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