| name | molecular-dynamics |
| description | Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics. |
| license | MIT |
| metadata | version: "1.0" skill-author: Kuan-lin Huang |
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
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
"""
pdb = PDBFile(pdb_file)
forcefield = ForceField(forcefield_name, water_model)
modeller = Modeller(pdb.topology, pdb.positions)
modeller.addHydrogens(forcefield)
modeller.addSolvent(
forcefield,
model='tip3p',
padding=10*angstroms,
ionicStrength=0.15*molar
)
print(f"System: {modeller.topology.getNumAtoms()} atoms, "
f"{modeller.topology.getNumResidues()} residues")
system = forcefield.createSystem(
modeller.topology,
nonbondedMethod=PME,
nonbondedCutoff=1.0*nanometer,
constraints=HBonds,
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
"""
integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds)
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)
state = simulation.context.getState(getEnergy=True)
print(f"Initial energy: {state.getPotentialEnergy()}")
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()}")
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
"""
simulation.context.setVelocitiesToTemperature(temperature*kelvin)
simulation.reporters = []
simulation.reporters.append(
StateDataReporter(
f"{output_prefix}_log.txt",
report_interval,
step=True,
potentialEnergy=True,
kineticEnergy=True,
temperature=True,
volume=True,
speed=True
)
)
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
"""
system = simulation.context.getSystem()
system.addForce(MonteCarloBarostat(pressure*bar, temperature*kelvin, 25))
simulation.context.reinitialize(preserveState=True)
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)
)
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
"""
aligner = align.AlignTraj(u, u, select=selection, in_memory=True)
aligner.run()
R = rms.RMSD(u, select=selection, ref_frame=reference_frame)
R.run()
rmsd_data = R.results.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
3. RMSF Analysis (Per-Residue Flexibility)
def compute_rmsf(u, selection="backbone", start_frame=0):
"""
Compute per-residue RMSF (flexibility).
Returns:
resids, rmsf_values arrays
"""
atoms = u.select_atoms(selection)
R = rms.RMSF(atoms)
R.run(start=start_frame)
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:]:
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)
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)
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
