Protein MD in Jupyter

Thu, 08 Oct 2026 00:00:00 +0000

Description

This is not a new tutorial. It is a re-creation of the classic protein MD tutorial, Lysozyme in Water by Justin A. Lemkul, rewritten as Jupyter notebooks to make it more intuitive and hands-on. Instead of typing commands one by one in a terminal, every step lives in a notebook cell, with comments explaining what it does, so you can run, modify and re-run the workflow and plot the results in the same place.

The same system (hen egg-white lysozyme, PDB 1AKI) is simulated with two platforms:

GROMACS: the original workflow, driven from Python with GromacsWrapper.
OpenMM: the same workflow written in pure Python, which I find has a gentler learning curve if you already know Python.

Find the notebooks on GitHub: tipiorgup/MDtutorials

Workflow

Clean the crystal structure (remove crystallographic waters).
Build the topology and choose a force field.
Define the simulation box and solvate it.
Add ions to neutralize the system.
Minimize the energy.
Equilibrate under NVT, then NPT.
Run the production MD.
Analyze the results, e.g. plot the potential energy.

Folder Structure

GROMACS: GROMACS_playground.ipynb plus the .mdp parameter files in scripts/ (ions, minimization, NVT, NPT, MD).
OPENMM: OPENMM_playground.ipynb (CHARMM36 force field, minimization, NVT/NPT and production with reporters).
structures: The 1AKI input structure, raw and cleaned.

Setting Up OpenMM

conda create -n openmm
conda activate openmm
conda install -c conda-forge openmm numpy matplotlib jupyter

References

J. A. Lemkul. From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package. Living Journal of Computational Molecular Science (2018), 1(1):5068. DOI: 10.33011/livecoms.1.1.5068
M. J. Abraham et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX (2015), 1–2:19–25. DOI: 10.1016/j.softx.2015.06.001
P. Eastman et al. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. J. Phys. Chem. B (2024), 128(1):109–116. DOI: 10.1021/acs.jpcb.3c06662