Molecular Dynamics

Molecular Dynamics Simulation Pipeline

Autonomous molecular dynamics simulation from structure to analysis. Inspired by DynaMate's three-module architecture: Experiment Planner → Simulation Performer → Result Analyzer.

When to Use

• "帮我跑个 MD 模拟" or "molecular dynamics simulation for X"
• "计算 X 和 Y 的结合自由能" or "binding free energy"
• "这个蛋白稳定吗" or "protein stability analysis"
• "蛋白-配体相互作用模拟"
• User provides a PDB ID, UniProt ID, or protein structure file

Prerequisites Check

Before starting any simulation, verify the environment:

bash: python3 << 'PYEOF'
import sys

checks = {"openmm": False, "ambertools": False, "mdtraj": False, "pdbfixer": False, "nglview": False}

try:
    import openmm
    checks["openmm"] = True
    print(f"✅ OpenMM {openmm.__version__}")
except ImportError:
    print("❌ OpenMM not installed")
    print("   Install: conda install -c conda-forge openmm")

try:
    import parmed
    checks["ambertools"] = True
    print("✅ AmberTools (via parmed)")
except ImportError:
    print("⚠️  AmberTools not installed (optional, for ligand parameterization)")
    print("   Install: conda install -c conda-forge ambertools")

try:
    import mdtraj
    checks["mdtraj"] = True
    print(f"✅ MDTraj {mdtraj.__version__}")
except ImportError:
    print("❌ MDTraj not installed (needed for analysis)")
    print("   Install: pip install mdtraj")

try:
    import pdbfixer
    checks["pdbfixer"] = True
    print("✅ PDBFixer")
except ImportError:
    print("❌ PDBFixer not installed (needed for structure preparation)")
    print("   Install: conda install -c conda-forge pdbfixer")

if not checks["openmm"]:
    print("\n⛔ Cannot run MD simulations without OpenMM.")
    print("Recommend: conda create -n md python=3.11 openmm pdbfixer mdtraj parmed -c conda-forge")
    sys.exit(1)

print("\n✅ Environment ready for MD simulations")
PYEOF

If OpenMM is not available, report clearly and do not attempt the simulation.

Pipeline

Step 1: Structure Retrieval

Fetch the protein structure:

# From PDB
curl -s "https://files.rcsb.org/download/PDBID.pdb" -o structure.pdb

# From AlphaFold (if no experimental structure)
curl -s "https://alphafold.ebi.ac.uk/files/AF-UNIPROT_ID-F1-model_v4.pdb" -o structure.pdb

If user provides a gene name, resolve to UniProt ID first:

curl -s "https://rest.uniprot.org/uniprotkb/search?query=gene_exact:GENE+AND+organism_id:9606&format=json&size=1" | \
python3 -c "import sys,json; d=json.load(sys.stdin); print(d['results'][0]['primaryAccession'])"

Step 2: Structure Preparation

Fix common PDB issues and prepare the system:

from pdbfixer import PDBFixer
from openmm.app import PDBFile

fixer = PDBFixer(filename='structure.pdb')
fixer.findMissingResidues()
fixer.findMissingAtoms()
fixer.addMissingAtoms()
fixer.addMissingHydrogens(7.4)  # pH 7.4

# Remove heterogens except ligand of interest (if any)
fixer.removeHeterogens(keepWater=False)

PDBFile.writeFile(fixer.topology, fixer.positions, open('prepared.pdb', 'w'))
print(f"Prepared structure: {fixer.topology.getNumAtoms()} atoms, {fixer.topology.getNumResidues()} residues")

Step 3: System Setup

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

pdb = PDBFile('prepared.pdb')
forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml')

modeller = Modeller(pdb.topology, pdb.positions)
modeller.addSolvent(forcefield, model='tip3p', padding=1.0*nanometers,
                    ionicStrength=0.15*molar)

system = forcefield.createSystem(modeller.topology,
    nonbondedMethod=PME,
    nonbondedCutoff=1.0*nanometers,
    constraints=HBonds)

print(f"System: {modeller.topology.getNumAtoms()} atoms (protein + water + ions)")

Step 4: Energy Minimization

integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds)
simulation = Simulation(modeller.topology, system, integrator)
simulation.context.setPositions(modeller.positions)

# Minimize
print(f"Initial PE: {simulation.context.getState(getEnergy=True).getPotentialEnergy()}")
simulation.minimizeEnergy(maxIterations=1000)
pe = simulation.context.getState(getEnergy=True).getPotentialEnergy()
print(f"Minimized PE: {pe}")

# Self-correction: if PE is positive or very high, something is wrong
if pe._value > 0:
    print("⚠️  Potential energy is positive — possible steric clash")
    print("   Attempting additional minimization with tolerance=100...")
    simulation.minimizeEnergy(maxIterations=5000, tolerance=100*kilojoules_per_mole)
    pe = simulation.context.getState(getEnergy=True).getPotentialEnergy()
    print(f"Re-minimized PE: {pe}")

Step 5: Equilibration

# NVT equilibration (100 ps)
simulation.context.setVelocitiesToTemperature(300*kelvin)
simulation.reporters.append(StateDataReporter('nvt_log.csv', 1000,
    step=True, temperature=True, potentialEnergy=True))
simulation.step(25000)  # 100 ps at 4 fs/step
print("NVT equilibration complete (100 ps)")

# NPT equilibration (100 ps)
system.addForce(MonteCarloBarostat(1*bar, 300*kelvin))
simulation.context.reinitialize(preserveState=True)
simulation.reporters.append(StateDataReporter('npt_log.csv', 1000,
    step=True, density=True, potentialEnergy=True))
simulation.step(25000)
print("NPT equilibration complete (100 ps)")

Step 6: Production Run

simulation.reporters.clear()
simulation.reporters.append(DCDReporter('trajectory.dcd', 5000))  # Save every 20 ps
simulation.reporters.append(StateDataReporter('production_log.csv', 5000,
    step=True, time=True, potentialEnergy=True, temperature=True, density=True))

# Default: 10 ns production (adjust based on system size)
n_steps = 2500000  # 10 ns at 4 fs/step
print(f"Starting production run: 10 ns ({n_steps} steps)")
simulation.step(n_steps)
print("Production run complete")

# Save final state
simulation.saveState('final_state.xml')
PDBFile.writeFile(simulation.topology, simulation.context.getState(getPositions=True).getPositions(),
                  open('final_frame.pdb', 'w'))

Step 7: Trajectory Analysis

import mdtraj as md
import matplotlib.pyplot as plt
import numpy as np

traj = md.load('trajectory.dcd', top='prepared.pdb')
print(f"Trajectory: {traj.n_frames} frames, {traj.n_atoms} atoms, {traj.time[-1]/1000:.1f} ns")

fig_dir = "figures"
os.makedirs(fig_dir, exist_ok=True)

# RMSD
rmsd = md.rmsd(traj, traj, 0, atom_indices=traj.topology.select('protein and name CA'))
plt.figure(figsize=(8,4), dpi=300)
plt.plot(traj.time/1000, rmsd*10, linewidth=0.8)
plt.xlabel('Time (ns)'); plt.ylabel('RMSD (Å)')
plt.title('Backbone RMSD'); plt.tight_layout()
plt.savefig(f'{fig_dir}/rmsd.png', dpi=300)
print(f"Mean RMSD: {np.mean(rmsd)*10:.2f} ± {np.std(rmsd)*10:.2f} Å")

# RMSF
rmsf = md.rmsf(traj, traj, atom_indices=traj.topology.select('protein and name CA'))
plt.figure(figsize=(10,4), dpi=300)
plt.plot(range(len(rmsf)), rmsf*10, linewidth=0.8)
plt.xlabel('Residue Index'); plt.ylabel('RMSF (Å)')
plt.title('Per-Residue RMSF'); plt.tight_layout()
plt.savefig(f'{fig_dir}/rmsf.png', dpi=300)

# Radius of gyration
rg = md.compute_rg(traj)
print(f"Radius of gyration: {np.mean(rg)*10:.2f} ± {np.std(rg)*10:.2f} Å")

# Hydrogen bonds
hbonds = md.baker_hubbard(traj, freq=0.3)
print(f"Persistent hydrogen bonds (>30% occupancy): {len(hbonds)}")

Self-Correction Protocol

After each step, check for errors before proceeding. Maximum 3 retry attempts per step.

Output Report Structure

# Molecular Dynamics Simulation Report: [PROTEIN]

## System Summary
- PDB ID: [ID] / AlphaFold model: [AF-ID]
- Protein: [Name], [N] residues, [N] atoms
- Solvation: TIP3P water, 0.15 M NaCl, [N] total atoms
- Force field: Amber14

## Simulation Parameters
- Temperature: 300 K (Langevin thermostat, γ = 1/ps)
- Pressure: 1 bar (Monte Carlo barostat)
- Timestep: 4 fs
- Cutoff: 10 Å (PME electrostatics)
- Production: [X] ns

## Results
- RMSD: [mean ± std] Å (converged after [X] ns)
- RMSF: [highlight flexible regions]
- Rg: [mean ± std] Å
- Persistent H-bonds: [N] (>30% occupancy)

## Structural Stability Assessment
[Interpretation of RMSD convergence, flexible regions, overall stability]

## Figures
- figures/rmsd.png — Backbone RMSD over time
- figures/rmsf.png — Per-residue flexibility

Limitations

• GPU strongly recommended for production runs (CPU is 10-100x slower)
• Default 10 ns may be insufficient for large conformational changes
• Ligand parameterization requires AmberTools and GAFF force field
• MM/PBSA calculations are approximate and should be validated experimentally
• This skill does not replace expert computational chemistry judgment for publication-grade simulations