Coarse grained

Skill Kdevos12/ALKYL/skills/coarse-grained

Claude Plugin for CompChem , Drug Discovery & Organic Chemistry reasoning

Install

npx -y skills add Kdevos12/ALKYL --skill coarse-grained

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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.

SKILL.md

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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