Step-by-Step Tutorial: How to Do a Molecular Dynamics Simulation

Principles of MD Simulation

Molecular Dynamics (MD) simulation is a numerical method used to study the dynamic behavior of molecular systems through computer simulation. It is widely used in physics, chemistry, biology, and materials science. Its core is to predict the motion trajectories of particles in molecular systems by Newton's equation of motion, to study the structure, dynamic properties, and thermodynamic properties of molecules.

MD simulations are based on Newton's equations of motion, which describe the motion of particles:

• F_i is the force acting on particle i
• m_i is the particle's mass
• a_i is the particle's acceleration
• r_i is the particle's position

The velocity and position of particles can be solved step by step using a numerical method to simulate the system's dynamic evolution.

Fundamental Concepts

Molecular dynamics simulation involves several key concepts, and understanding these concepts is the basis for conducting simulation correctly.

Force Field

Force fields are mathematical models used to describe the interactions between particles in molecular dynamics simulations. Force fields generally include two types of interactions: bonding interactions and non-bonding interactions.

Bonded interaction describe interactions within a molecule, including bond length (the length of the covalent bond between atoms), bond Angle (the Angle at which three covalently linked atoms form), and dihedral Angle (the Angle of torsion between four atoms). Non-bonded interactions are used to describe interactions between molecules, including van der Waals forces (which describe the repulsive and gravitational forces between particles) and electrostatic interactions (interactions between charged particles).

Common force fields are:

• AMBER: Widely used in biomolecules (e.g. proteins, nucleic acids).
• CHARMM: Suitable for a variety of biomolecular and material simulations.
• OPLS-AA: For the simulation of small and drug molecules.
• GROMOS: Suitable for protein and liquid systems.

Time step and integration algorithm

In MD simulation, the time step (Δt) is an important parameter that affects the simulation accuracy. The time step must be small enough to guarantee the stability and accuracy of the simulation system (usually 1-2 femtoseconds).

Common integration algorithms:

• Verlet algorithm: It is simple and efficient to calculate the next atomic position through the recursive relation of position.
• Velocity-verlet algorithm: Improves the calculation of Velocity while providing higher accuracy.
• Leapfrog algorithm: Further improves numerical stability by alternating calculation speed and position.

Ensembles

Molecular dynamics simulations can be performed under different thermodynamic conditions, which are defined by ensemble. 

Common ensembles include:

• NVT Ensemble (canonical ensemble): The number of particles N, volume V, and temperature T remain constant.
• NPT ensemble (isothermal and isobaric ensemble): The number of particles N, pressure P, and temperature T remain constant, often used to simulate actual physical conditions.

MD simulates general steps

No matter which software is used, molecular dynamics simulation has a certain flow. You can start a molecular dynamics simulation by referring to the process below and incorporating the details.

Preparation: Define the system

Before you can start the simulation, you first need to define the objects and systems to be simulated.

• Select the simulated object: The user selects the simulated molecular system according to the target of the study, such as protein, nucleic acid, small molecule, solvent, or solid material. You can take the initial structure from experimental data, such as a PDB file, or build the structure through modeling software.
• Select force fields: Force fields are at the heart of molecular dynamics simulations and determine the interactions between molecules and the potential energy function. Choosing the right force field is crucial to the accuracy of the simulation. You can distinguish and choose between different force fields in the section above on the concept of force fields.
• Adding solvents and ions: For most biomolecular simulations, it is usually necessary to add solvents (such as water) and corresponding ions to the simulation to neutralize the charge or simulate the physiological environment.

Energy Minimization

Energy minimization is the first step in the simulation, which aims to eliminate unreasonable initial structures and remove high energy states due to local defects or excessive interatomic distances in the model construction process. The goal of energy minimization is to obtain a stable initial state by optimizing the geometric structure of the molecular system and avoiding large unreasonable fluctuations in the subsequent simulation.

Equilibration

After the energy is minimized, the system needs to be balanced at a certain temperature and pressure. The purpose of the equilibrium stage is to allow the system to reach thermodynamic equilibrium under the desired conditions for subsequent production simulations.

System balance is usually divided into two stages:

NVT balance (constant temperature, constant volume):

The system temperature remains stable at a predetermined value. By introducing temperature control algorithms (such as Berendsen temperature control and Langevin dynamics), the temperature of the system is gradually approached to the target temperature.

NPT balance (constant pressure, room temperature):

On the basis of NVT balance, pressure control is added to ensure the stability of pressure and volume in the simulation process. Common pressure control methods include Berendsen pressure coupling and the Nose-Hoover method.

Production Simulation

Production simulation is the core stage of MD simulation. At this stage, the system has reached thermodynamic equilibrium through the energy minimization and balancing steps, which can then be followed by long-term production simulations. The purpose of production simulation is to obtain dynamical data of the system, such as particle position, velocity, energy, etc.

Choose the appropriate simulation time:

Select the appropriate length of simulation time according to the research objective. For some small molecules or simple systems, simulation times of a few nanoseconds (ns) may be sufficient; For complex biomolecular systems, several microseconds (μs) or even longer simulation times may be required.

Select the right simulation ensemble:

In production simulation, the choice of a suitable simulation ensemble (NVT, NPT) depends on the simulation goal. If you are simulating a biological system with fixed temperature and pressure, you can choose the NPT ensemble.

Results Analysis

View simulated trajectories with molecular visualization tools (e.g. VMD, PyMOL) to visually understand molecular behavior. Visualization also helps identify potential structural and functional changes. At the same time, the trajectory data generated by the system during the simulation process can be analyzed. For example, the structural stability of the system is evaluated by calculating the root mean square deviation during the simulation. The root-mean-square fluctuation is used to measure the fluctuation range of individual atoms in a molecule.

Setting Up Your Simulation Environment

Before installing the software, you need to select the appropriate hardware resources and ensure that the system configuration meets the needs of the simulation.

Basic System Requirements and Hardware Considerations

The molecular dynamics simulation of large-scale systems often requires powerful computational resources, and you need to pay attention to the configuration of your computer and make sure that you can meet these conditions.

CPU (Central Processing Unit)

MD simulation is usually computationally heavy, so the performance of the CPU is critical. Multi-core processors can significantly improve the parallelization efficiency of simulations.

Recommended configuration: At least a 4-core CPU is better, and for large-scale computing, you can choose high-performance servers or cloud computing services to run MD simulations.

GPU (Graphics Processing Unit)

If the simulation software supports GPU acceleration (e.g. GROMACS, LAMMPS), using Gpus with high computing power will greatly improve the computing speed.

Memory (RAM)

The memory requirements for MD simulation are proportional to the size of the simulated system (number of molecules, number of particles). For small systems, 16GB of memory is fine, but for larger systems or high-resolution simulations, at least 32GB or more is required.

Storage

A large number of track files and data files are generated during the simulation, which can be very large and therefore require sufficient storage space.

Installing Required Software

MD simulation engines include GROMACS, LAMMPS, and CHARMM. The installation requirements and procedures for each software tool are roughly the same, and GROMACS is used as an example.

Get source code

Visit the GROMACS website at https://www.gromacs.org/

Installation dependency

Install the required libraries and tools such as CMake (build tools), FFT libraries, MPI (Parallel Computing support), etc.

Compile and install

Create a build directory, configure compilation options, and start compiling.

Environment configuration

Configure environment variables so that GROMACS can be accessed from anywhere.

Preparing Input Files

The input file for the molecular dynamics (MD) simulation defines the structure, simulation conditions, and operating parameters of the system. Typically, input files include molecular structure files, force field files, and simulation parameter files.

Understanding Input Parameters

System parameter

Simulation Time: Defines the total time for the simulation to run, such as nanoseconds (ns) or microseconds (μs).

Time Step: The time step size is usually set to 1-2 femtoseconds (fs).

Temperature: Defines the target temperature of the system, usually maintained using thermodynamic temperature control algorithms (e.g. Berendsen, Nose-Hoover).

Pressure: If a constant pressure ensemble (NPT) is used, a target pressure value (e.g. 1 atm) needs to be defined.

Force field parameter

Force fields are mathematical models that describe the interactions between atoms in simulations. Different systems need to choose the right force field.

Force field selection:

Biomolecules: AMBER, CHARMM;

Small molecules: GAFF (General Force Field);

Material system: OPLS, COMPASS.

Output parameter

Trajectory file: Records changes in the position, velocity, and energy of particles in the system over time, usually stored in. xtc or. trr format.

Energy file: The energy of the output system (such as potential energy, kinetic energy, total energy, etc.).

Snapshots: Snapshots of the structure of molecules in the system (e.g.,.pdb files)

Generating Molecular Structures and Topologies

The molecular structure file provides the initial coordinates of the molecules, while the topology file defines the connection relationships between the molecules and the force field parameters.

A molecular structure file describes the coordinates and types of all atoms in a molecular system and is usually expressed in the format.pdb. gro, or. mol2.

Topology files define the connection relationships between atoms in a molecule, force field parameters, and intermolecular interactions. Topology files are usually automatically generated by simulation software, but depend on the selected force field.

Running a Basic MD Simulation

MD simulation is usually divided into several stages:

• Energy Minimization: To achieve the lowest energy state of the system and eliminate unreasonable atomic contact.
• Equilibration: Equilibration of system temperature and pressure by short-time simulation.
  NVT balance: Keep the temperature constant.
  NPT balance: Keep temperature and pressure constant.
• Production Run: Run long free simulations to obtain trajectory data.

Analyzing Simulation Results

By running a molecular dynamics simulation, the molecular motion data of the system under given conditions can be obtained. Using basic analytical techniques such as RMSD, RMSF, and energy analysis, it is possible to assess the stability, flexibility, and dynamic behavior of molecular structures. This paper summarizes some commonly used analysis angles, more detailed content can refer to the article How to Analyze Results from Molecular Dynamics Simulations.

• Energy analysis:
  The energy of the system is analyzed over time to ensure that the system reaches equilibrium.
• Trajectory analysis
  Atomic spacing (RMSD): Calculate the root-mean-square deviation of the architecture from the reference structure to evaluate the stability of the structure.
  Root means square fluctuation (RMSF): The fluctuation amplitude of each residue is analyzed to reveal structural flexibility.
  Radial distribution function (RDF): evaluates the distribution between molecules.

There are many software programs available to help you analyze MD simulation results, such as CHAPERONg, which provides a comprehensive analysis of the MD simulation trajectory and simplifies and automates the established pipeline to more efficiently meet the user's usage needs.

Figure 1: CHAPERONg provides and automates an overview of workflows and functions. (Yekeen, Abeeb Abiodun, et al.,2023)