openmm

import openmm.app as app import openmm as mm import openmm.unit as unit # This example assumes a 'protein.pdb' file exists in the same directory. # A minimal PDB can be generated or downloaded, e.g., from RCSB PDB (e.g., 1AKI). # For a real simulation, ensure your PDB is properly prepared (e.g., with PDBFixer). try: pdb = app.PDBFile('protein.pdb') except FileNotFoundError: print("Error: 'protein.pdb' not found. Please provide a PDB file for the quickstart.") exit() # Create a force field for the system forcefield = app.ForceField('amber14-all.xml', 'amber14/tip3pfb.xml') # Create a system from the PDB topology and force field system = forcefield.createSystem(pdb.topology, nonbondedMethod=app.PME, nonbondedCutoff=1.0*unit.nanometers, constraints=app.HBonds) # Create an integrator for advancing the simulation integrator = mm.LangevinMiddleIntegrator(300*unit.kelvin, 1/unit.picosecond, 0.004*unit.picoseconds) # Create a simulation object simulation = app.Simulation(pdb.topology, system, integrator) simulation.context.setPositions(pdb.positions) # Minimize energy to relieve bad contacts print('Minimizing energy...') simulation.minimizeEnergy() print(f'Potential energy after minimization: {simulation.context.getState(getEnergy=True).getPotentialEnergy()}') # Add reporters for output simulation.reporters.append(app.PDBReporter('output.pdb', 1000)) simulation.reporters.append(app.StateDataReporter('data.csv', 1000, step=True, potentialEnergy=True, temperature=True, separator=',')) # Run the simulation print('Running simulation...') simulation.step(10000) # Run 10,000 steps print('Simulation complete. Output saved to output.pdb and data.csv')