molecular-dynamics

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
tags	["scientific-skills","molecular-dynamics","cheminformatics"]
metadata	{"skill-author":"Kuan-lin Huang"}

--|------------------------|-------------| | 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

• 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