molecular-dynamics — Моделирование поведения молекулярных систем во времени

--- 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: 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:** ```bash 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ``` ### 3. RMSF Analysis (Per-Residue Flexibility) ```python 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 ```python 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) ```python 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) ```python # 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 - **OpenMM documentation**: https://openmm.org/documentation.html - **MDAnalysis user guide**: https://docs.mdanalysis.org/ - **GROMACS** (alternative MD engine): https://manual.gromacs.org/ - **NAMD** (alternative): https://www.ks.uiuc.edu/Research/namd/ - **CHARMM-GUI** (web-based system builder): https://charmm-gui.org/ - **AmberTools** (free Amber tools): https://ambermd.org/AmberTools.php - **OpenMM paper**: Eastman P et al. (2017) PLOS Computational Biology. PMID: 28278240 - **MDAnalysis paper**: Michaud-Agrawal N et al. (2011) J Computational Chemistry. PMID: 21500218