Hands On Protein–Ligand Molecular Dynamics Simulation, Interaction Analysis and Binding-Energy Interpretation

Aim

Workshop Objectives

Workshop Structure

Day 1: Protein–Ligand System Preparation & Energy Minimisation

Core Objective: Prepare a docked protein–ligand complex and convert it into a simulation-ready molecular system for GROMACS-based MD simulation.

• Inspect and prepare the protein–ligand complex, generate protein topology, and create ligand parameters compatible with the selected force field.
• Define the simulation box, solvate the complex with explicit water, add counter-ions, and neutralise the molecular system.
• Perform energy minimisation and evaluate potential energy and structural quality before proceeding to equilibration.

 Hands-on Lab: Prepare a protein–ligand complex in Google Colab, generate the required topology and ligand parameters, solvate and neutralise the system, and perform energy minimisation to obtain a simulation-ready structure.

 Tools Covered: GROMACS, Google Colab, ACPYPE/AmberTools, PyMOL/Py3Dmol

Output: Simulation-ready, solvated and energy-minimised protein–ligand complex.

Day 2: Equilibration & Production Molecular Dynamics Simulation

Core Objective: Equilibrate the prepared protein–ligand system, initiate production MD simulation, and verify that the system is suitable for downstream trajectory analysis.

• Perform NVT equilibration to stabilise system temperature and NPT equilibration to stabilise pressure and density.
• Evaluate temperature, pressure, density and potential-energy profiles to assess whether the molecular system has achieved suitable equilibration.
• Configure production MD parameters, initiate the simulation, and prepare the resulting trajectory for subsequent structural analysis.

 Hands-on Lab: Run NVT and NPT equilibration of the prepared protein–ligand system, generate temperature, pressure and density QC plots, and initiate a production MD simulation in GROMACS. A validated longer trajectory will be provided for complete downstream analysis.

 Tools Covered: GROMACS, Google Colab, Python, Matplotlib

Output: Equilibrated system, simulation-QC plots, production-MD setup and analysis-ready trajectory.

Day 3: MD Trajectory, Structural Stability & Protein–Ligand Interaction Analysis

Core Objective: Analyse the MD trajectory to determine protein stability, ligand retention, conformational behaviour and persistence of key protein–ligand interactions.

• Process and align the trajectory, then calculate protein RMSD, ligand RMSD, RMSF and radius of gyration to evaluate structural and conformational stability.
• Analyse hydrogen-bond occupancy and protein–ligand contacts to determine whether important binding-site interactions persist during the simulation.
• Generate dynamic interaction fingerprints and identify persistent, transient, lost and newly formed interactions between the ligand and binding-site residues.

 Hands-on Lab: Analyse a protein–ligand MD trajectory to generate RMSD, RMSF and radius-of-gyration plots, quantify hydrogen-bond persistence, and build a dynamic protein–ligand interaction fingerprint for binding-site interpretation.

 Tools Covered: GROMACS, MDAnalysis, ProLIF, Python, Matplotlib, Google Colab

Output: Structural-stability plots, hydrogen-bond profile, residue-contact analysis and dynamic protein–ligand interaction fingerprint.

Day 4: MM/PBSA Binding-Energy Analysis & Ligand Prioritisation

Core Objective: Estimate protein–ligand binding energetics, identify important energetic residues, and integrate molecular dynamics evidence for research-oriented ligand evaluation.

• Select representative equilibrated trajectory frames and calculate MM/PBSA energetic components including van der Waals, electrostatic, polar and non-polar solvation contributions.
• Perform per-residue energy decomposition to identify favourable and unfavourable residues contributing to protein–ligand binding.
• Integrate structural stability, interaction persistence and MM/PBSA results to compare candidate ligands and develop a scientifically supported interpretation.

 Hands-on Lab: Perform MM/PBSA analysis on selected MD trajectory frames, generate binding-energy and residue-contribution plots, identify key binding residues, and combine structural and energetic evidence to prioritise the candidate ligand.

 Tools Covered: GROMACS, gmx_MMPBSA, AmberTools, Python, Pandas, Matplotlib, Google Colab

Output: MM/PBSA results, residue-energy contributions, key binding residues and an evidence-based ligand-prioritisation summary.

Who Should Enrol?

Important Dates

Workshop Outcomes