Molecular Dynamics Analysis Tutorial

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules, i.e. to sample molecular conformations. This is also the route to relate the microscopic movements and positions of the atoms to the macroscopic or thermodynamic quantities that can be measured experimentally.There are two major simulation methods to sample biomolecular systems: molecular dynamics (MD) and Monte Carlo (MC). Molecular dynamics simulations and molecular modeling are today essential research instruments in biomedicine that complement observation, permit rational approaches in the design of experiments, provide access to complex data and models, and yield an atomic level understanding of cellular processes.

MD simulation has been reported for pharmacophore development and drug design.

  • Calculate average positions of critical amino acids involved in ligand binding.
  • Identify compounds that complement the receptor while causing minimal disruption of the conformation and flexibility of the active site.
  • Snapshots of the protein at constant time intervals during the simulation were overlaid to identify conserved binding regions (conserved in at least three out of eleven frames) for pharmacophore development.
  • A workflow of MD simulations, finger prints for ligands and proteins (FLAP) and linear discriminate analysis has been used to identify best ligand– protein conformations to act as pharmacophore templates based on retrospective ROC analysis of the resulting pharmacophores.
  • A combination of MD simulation and ligand-receptor intermolecular contacts analysis has been proposed to discern critical intermolecular contacts (binding interactions) from redundant ones in a single ligand-protein complex. Critical contacts can be converted into pharmacophore models that can be used for virtual screening.

Presently, technology promises another great advance in computing power towards teraflop and even petaflop speeds employing very large parallel machines. On this new generation of computers, simulations can be carried out for systems thousand times larger or over time scales thousand times longer than previously, permitting the study of protein-protein and protein-nucleic acid recognition and assembly, the investigation of integral functional units of cells, e.g., eventually a complete ribosome, and bridging the gap between computationally feasible and functionally relevant time scales, e.g., for protein folding.

Molecular Dynamics Algorithms

  • Screened Coulomb potentials implicit solvent model


  • Symplectic integrator
  • Verlet–Stoermer integration
  • Runge–Kutta integration
  • Beeman's algorithm
  • Constraint algorithms (for constrained systems)

Short-range Interaction Algorithms

  • Cell lists, a data structure in molecular dynamics simulations to find all atom pairs within a given cut-off distance of each other.
  • Verlet list, a data structure in molecular dynamics simulations to efficiently maintain a list of all particles within a given cut-off distance of each other.
  • Bonded interactions

Long-range Interaction Algorithms

  • Ewald summation
  • Particle mesh Ewald summation (PME)
  • Particle–particle-particle–mesh (P3M)
  • Shifted force method

Parallelization Strategies

  • Domain decomposition method (Distribution of system data for parallel computing)

Ab-initio Molecular Dynamics

  • Car–Parrinello molecular dynamics
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