openmm

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• SKILL.md
• openmm_md.cpython-313.pyc · scripts/__pycache__/
• openmm_md.py · scripts/

SKILL.md (first lines)

# OpenMM Molecular Dynamics Engine

## Overview

OpenMM is a toolkit for molecular simulation, particularly suited for biomolecular systems (proteins, ligands, membranes). This skill provides computational investigation capabilities for protein dynamics, ligand binding exploration, conformational sampling, and free energy calculations. OpenMM supports GPU acceleration for high-performance simulations and multiple force fields (AMBER, CHARMM, OPLS).

## Core Capabilities

### 1. Protein Dynamics Simulation

**Basic MD Run:**

Simulate protein motion in explicit solvent:

```python
from openmm import *
from openmm.app import *
from openmm.unit import *

# Load structure
pdb = PDBFile('protein.pdb')

# Create force field and system
forcefield = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
system = forcefield.createSystem(pdb.topology, nonbondedMethod=PME)

# Create integrator (NVT ensemble)
integrator = LangevinIntegrator(300*kelvin, 1/picosecond, 2*femtoseconds)

# Run simulation
simulation = Simulation(pdb.topology, system, integrator)
simulation.context.setPositions(pdb.positions)
simulation.minimizeEnergy()
simulation.reporters.append(PDBReporter('trajectory.pdb', 1000))
simulation.step(100000)  # 200 ps
```

**Key Ensembles:**
- NVE: Constant volume, constant energy (microcanonical)
- NVT: Constant volume, constant temperature (Langevin/Nose-Hoover)
- NPT: Constant pressure, constant temperature (isothermal-isobaric)

### 2. Ligand Binding Exploration

**Protein-Ligand Complex Dynamics:**

Simulate ligand movement in protein binding pocket:

```python
# Load complex (protein + ligand)
pdb = PDBFile('complex.pdb')

# Create system with AMBER FF
forcefield = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
system = forcefield.createSystem(
    pdb.topology,
    nonbondedMethod=PME,
    constraints=HBonds
)

# Run with restraints on protein (ligand free)
# Can use positional restraints to keep protein stable
```

**Binding Free Energy (Alchemical):**

Calculate free energy of ligand binding:

```python
# Thermodynamic Integration (TI) or
# Free Energy Perturbation (FEP)
# Alchemically transform ligand from bound → unbound state
```

### 3. Conformational Sampling

**Enhanced Sampling Techniques:**

Explore conformational landscape:

```python
# Replica Exchange Molecular Dynamics (REMD)
# Multiple replicas at different temperatures
# Exchanges improve sampling efficiency

# Or: Simulated annealing
# Gradual temperature reduction
```

### 4. Energy Minimization

**Structure Optimization:**

Relax structures before MD:

```python
simulation.minimizeEnergy(maxIterations=1000)
```

Finds local energy minimum without dynamics.

### 5. Analysis

**Extract Properties:**

Compute from trajectories:

```python
- Root-mean-square deviation (RMSD)
- Radius of gyration (Rg)
- Hydrogen bond occupancy
- Dihedral angles (phi/psi for proteins)
- Free energy differences
- Binding free energies (MM-PBSA)
```

## Available Force Fields

### Biomolecules

From lamm-mit/scienceclaw, MIT.

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