molecular-dynamics

Molecular dynamics (MD) simulation computationally models the time evolution of molecular systems by integrating Newton's equations of motion. This skill covers two complementary tools:

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Install skill "molecular-dynamics" with this command: npx skills add k-dense-ai/claude-scientific-skills/k-dense-ai-claude-scientific-skills-molecular-dynamics

Molecular Dynamics

Overview

Molecular dynamics (MD) simulation computationally models the time evolution of molecular systems by integrating Newton's equations of motion. This skill covers two complementary tools:

  • OpenMM (https://openmm.org/): High-performance MD simulation engine with GPU support, Python API, and flexible force field support

  • MDAnalysis (https://mdanalysis.org/): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages

Installation:

conda install -c conda-forge openmm mdanalysis nglview

or

pip install openmm mdanalysis

When to Use This Skill

Use molecular dynamics when:

  • Protein stability analysis: How does a mutation affect protein dynamics?

  • Drug binding simulations: Characterize binding mode and residence time of a ligand

  • Conformational sampling: Explore protein flexibility and conformational changes

  • Protein-protein interaction: Model interface dynamics and binding energetics

  • RMSD/RMSF analysis: Quantify structural fluctuations from a reference structure

  • Free energy estimation: Compute binding free energy or conformational free energy

  • Membrane simulations: Model proteins in lipid bilayers

  • Intrinsically disordered proteins: Study IDR conformational ensembles

Core Workflow: OpenMM Simulation

  1. System Preparation

from openmm.app import * from openmm import * from openmm.unit import * import sys

def prepare_system_from_pdb(pdb_file, forcefield_name="amber14-all.xml", water_model="amber14/tip3pfb.xml"): """ Prepare an OpenMM system from a PDB file.

Args:
    pdb_file: Path to cleaned PDB file (use PDBFixer for raw PDB files)
    forcefield_name: Force field XML file
    water_model: Water model XML file

Returns:
    pdb, forcefield, system, topology
"""
# Load PDB
pdb = PDBFile(pdb_file)

# Load force field
forcefield = ForceField(forcefield_name, water_model)

# Add hydrogens and solvate
modeller = Modeller(pdb.topology, pdb.positions)
modeller.addHydrogens(forcefield)

# Add solvent box (10 Å padding, 150 mM NaCl)
modeller.addSolvent(
    forcefield,
    model='tip3p',
    padding=10*angstroms,
    ionicStrength=0.15*molar
)

print(f"System: {modeller.topology.getNumAtoms()} atoms, "
      f"{modeller.topology.getNumResidues()} residues")

# Create system
system = forcefield.createSystem(
    modeller.topology,
    nonbondedMethod=PME,         # Particle Mesh Ewald for long-range electrostatics
    nonbondedCutoff=1.0*nanometer,
    constraints=HBonds,           # Constrain hydrogen bonds (allows 2 fs timestep)
    rigidWater=True,
    ewaldErrorTolerance=0.0005
)

return modeller, system

2. Energy Minimization

from openmm.app import * from openmm import * from openmm.unit import *

def minimize_energy(modeller, system, output_pdb="minimized.pdb", max_iterations=1000, tolerance=10.0): """ Energy minimize the system to remove steric clashes.

Args:
    modeller: Modeller object with topology and positions
    system: OpenMM System
    output_pdb: Path to save minimized structure
    max_iterations: Maximum minimization steps
    tolerance: Convergence criterion in kJ/mol/nm

Returns:
    simulation object with minimized positions
"""
# Set up integrator (doesn't matter for minimization)
integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds)

# Create simulation
# Use GPU if available (CUDA or OpenCL), fall back to CPU
try:
    platform = Platform.getPlatformByName('CUDA')
    properties = {'DeviceIndex': '0', 'Precision': 'mixed'}
except Exception:
    try:
        platform = Platform.getPlatformByName('OpenCL')
        properties = {}
    except Exception:
        platform = Platform.getPlatformByName('CPU')
        properties = {}

simulation = Simulation(
    modeller.topology, system, integrator,
    platform, properties
)
simulation.context.setPositions(modeller.positions)

# Check initial energy
state = simulation.context.getState(getEnergy=True)
print(f"Initial energy: {state.getPotentialEnergy()}")

# Minimize
simulation.minimizeEnergy(
    tolerance=tolerance*kilojoules_per_mole/nanometer,
    maxIterations=max_iterations
)

state = simulation.context.getState(getEnergy=True, getPositions=True)
print(f"Minimized energy: {state.getPotentialEnergy()}")

# Save minimized structure
with open(output_pdb, 'w') as f:
    PDBFile.writeFile(simulation.topology, state.getPositions(), f)

return simulation

3. NVT Equilibration

from openmm.app import * from openmm import * from openmm.unit import *

def run_nvt_equilibration(simulation, n_steps=50000, temperature=300, report_interval=1000, output_prefix="nvt"): """ NVT equilibration: constant N, V, T. Equilibrate velocities to target temperature.

Args:
    simulation: OpenMM Simulation (after minimization)
    n_steps: Number of MD steps (50000 × 2fs = 100 ps)
    temperature: Temperature in Kelvin
    report_interval: Steps between data reports
    output_prefix: File prefix for trajectory and log
"""
# Add position restraints for backbone during NVT
# (Optional: restraint heavy atoms)

# Set temperature
simulation.context.setVelocitiesToTemperature(temperature*kelvin)

# Add reporters
simulation.reporters = []

# Log file
simulation.reporters.append(
    StateDataReporter(
        f"{output_prefix}_log.txt",
        report_interval,
        step=True,
        potentialEnergy=True,
        kineticEnergy=True,
        temperature=True,
        volume=True,
        speed=True
    )
)

# DCD trajectory (compact binary format)
simulation.reporters.append(
    DCDReporter(f"{output_prefix}_traj.dcd", report_interval)
)

print(f"Running NVT equilibration: {n_steps} steps ({n_steps*2/1000:.1f} ps)")
simulation.step(n_steps)
print("NVT equilibration complete")

return simulation

4. NPT Equilibration and Production

def run_npt_production(simulation, n_steps=500000, temperature=300, pressure=1.0, report_interval=5000, output_prefix="npt"): """ NPT production run: constant N, P, T.

Args:
    n_steps: Production steps (500000 × 2fs = 1 ns)
    temperature: Temperature in Kelvin
    pressure: Pressure in bar
    report_interval: Steps between reports
"""
# Add Monte Carlo barostat for pressure control
system = simulation.context.getSystem()
system.addForce(MonteCarloBarostat(pressure*bar, temperature*kelvin, 25))
simulation.context.reinitialize(preserveState=True)

# Update reporters
simulation.reporters = []
simulation.reporters.append(
    StateDataReporter(
        f"{output_prefix}_log.txt",
        report_interval,
        step=True,
        potentialEnergy=True,
        temperature=True,
        density=True,
        speed=True
    )
)
simulation.reporters.append(
    DCDReporter(f"{output_prefix}_traj.dcd", report_interval)
)

# Save checkpoints
simulation.reporters.append(
    CheckpointReporter(f"{output_prefix}_checkpoint.chk", 50000)
)

print(f"Running NPT production: {n_steps} steps ({n_steps*2/1000000:.2f} ns)")
simulation.step(n_steps)
print("Production MD complete")
return simulation

Trajectory Analysis with MDAnalysis

  1. Load Trajectory

import MDAnalysis as mda from MDAnalysis.analysis import rms, align, contacts import numpy as np import matplotlib.pyplot as plt

def load_trajectory(topology_file, trajectory_file): """ Load an MD trajectory with MDAnalysis.

Args:
    topology_file: PDB, PSF, or other topology file
    trajectory_file: DCD, XTC, TRR, or other trajectory
"""
u = mda.Universe(topology_file, trajectory_file)
print(f"Universe: {u.atoms.n_atoms} atoms, {u.trajectory.n_frames} frames")
print(f"Time range: 0 to {u.trajectory.totaltime:.0f} ps")
return u

2. RMSD Analysis

def compute_rmsd(u, selection="backbone", reference_frame=0): """ Compute RMSD of selected atoms relative to reference frame.

Args:
    u: MDAnalysis Universe
    selection: Atom selection string (MDAnalysis syntax)
    reference_frame: Frame index for reference structure

Returns:
    numpy array of (time, rmsd) values
"""
# Align trajectory to minimize RMSD
aligner = align.AlignTraj(u, u, select=selection, in_memory=True)
aligner.run()

# Compute RMSD
R = rms.RMSD(u, select=selection, ref_frame=reference_frame)
R.run()

rmsd_data = R.results.rmsd  # columns: frame, time, RMSD
return rmsd_data

def plot_rmsd(rmsd_data, title="RMSD over time", output_file="rmsd.png"): """Plot RMSD over simulation time.""" fig, ax = plt.subplots(figsize=(10, 4)) ax.plot(rmsd_data[:, 1] / 1000, rmsd_data[:, 2], 'b-', linewidth=0.5) ax.set_xlabel("Time (ns)") ax.set_ylabel("RMSD (Å)") ax.set_title(title) ax.axhline(rmsd_data[:, 2].mean(), color='r', linestyle='--', label=f'Mean: {rmsd_data[:, 2].mean():.2f} Å') ax.legend() plt.tight_layout() plt.savefig(output_file, dpi=150) return fig

  1. RMSF Analysis (Per-Residue Flexibility)

def compute_rmsf(u, selection="backbone", start_frame=0): """ Compute per-residue RMSF (flexibility).

Returns:
    resids, rmsf_values arrays
"""
# Select atoms
atoms = u.select_atoms(selection)

# Compute RMSF
R = rms.RMSF(atoms)
R.run(start=start_frame)

# Average by residue
resids = []
rmsf_per_res = []
for res in u.select_atoms(selection).residues:
    res_atoms = res.atoms.intersection(atoms)
    if len(res_atoms) > 0:
        resids.append(res.resid)
        rmsf_per_res.append(R.results.rmsf[res_atoms.indices].mean())

return np.array(resids), np.array(rmsf_per_res)

4. Protein-Ligand Contacts

def analyze_contacts(u, protein_sel="protein", ligand_sel="resname LIG", radius=4.5, start_frame=0): """ Track protein-ligand contacts over trajectory.

Args:
    radius: Contact distance cutoff in Angstroms
"""
protein = u.select_atoms(protein_sel)
ligand = u.select_atoms(ligand_sel)

contact_frames = []
for ts in u.trajectory[start_frame:]:
    # Find protein atoms within radius of ligand
    distances = contacts.contact_matrix(
        protein.positions, ligand.positions, radius
    )
    contact_residues = set()
    for i in range(distances.shape[0]):
        if distances[i].any():
            contact_residues.add(protein.atoms[i].resid)
    contact_frames.append(contact_residues)

return contact_frames

Force Field Selection Guide

System Recommended Force Field Water Model

Standard proteins AMBER14 (amber14-all.xml ) TIP3P-FB

Proteins + small molecules AMBER14 + GAFF2 TIP3P-FB

Membrane proteins CHARMM36m TIP3P

Nucleic acids AMBER99-bsc1 or AMBER14 TIP3P

Disordered proteins ff19SB or CHARMM36m TIP3P

System Preparation Tools

PDBFixer (for raw PDB files)

from pdbfixer import PDBFixer from openmm.app import PDBFile

def fix_pdb(input_pdb, output_pdb, ph=7.0): """Fix common PDB issues: missing residues, atoms, add H, standardize.""" fixer = PDBFixer(filename=input_pdb) fixer.findMissingResidues() fixer.findNonstandardResidues() fixer.replaceNonstandardResidues() fixer.removeHeterogens(True) # Remove water/ligands fixer.findMissingAtoms() fixer.addMissingAtoms() fixer.addMissingHydrogens(ph)

with open(output_pdb, 'w') as f:
    PDBFile.writeFile(fixer.topology, fixer.positions, f)

return output_pdb

GAFF2 for Small Molecules (via OpenFF Toolkit)

For ligand parameterization, use OpenFF toolkit or ACPYPE

pip install openff-toolkit

from openff.toolkit import Molecule, ForceField as OFFForceField from openff.interchange import Interchange

def parameterize_ligand(smiles, ff_name="openff-2.0.0.offxml"): """Generate GAFF2/OpenFF parameters for a small molecule.""" mol = Molecule.from_smiles(smiles) mol.generate_conformers(n_conformers=1)

off_ff = OFFForceField(ff_name)
interchange = off_ff.create_interchange(mol.to_topology())
return interchange

Best Practices

  • Always minimize before MD: Raw PDB structures have steric clashes

  • Equilibrate before production: NVT (50–100 ps) → NPT (100–500 ps) → Production

  • Use GPU: Simulations are 10–100× faster on GPU (CUDA/OpenCL)

  • 2 fs timestep with HBonds constraints: Standard; use 4 fs with HMR (hydrogen mass repartitioning)

  • Analyze only equilibrated trajectory: Discard first 20–50% as equilibration

  • Save checkpoints: MD runs can fail; checkpoints allow restart

  • Periodic boundary conditions: Required for solvated systems

  • PME for electrostatics: More accurate than cutoff methods for charged systems

Additional Resources

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