A Step-by-Step Guide to Molecular Dynamics (MD) Simulations Using Web-Based Tools: Case Study on AWS Cloud Platform

Introduction:

Molecular Dynamics (MD) simulations are essential in computational biology and chemistry, providing insights into the dynamic behavior of molecular systems. Traditionally, MD simulations required powerful computational resources and expert knowledge to set up and manage complex software environments. With the advent of cloud computing, these simulations have become more accessible through web-based tools that leverage cloud infrastructure. This guide presents a practical tutorial for performing MD simulations using a web-based tool on Amazon Web Services (AWS), focusing on deploying GPU-accelerated instances for large molecular systems. A case study using the 4V4L molecular system will demonstrate the workflow, from setting up the environment to running and analyzing the simulation.

Table 1 Summarizing molecular dynamics (MD) simulators and cloud computing-related software:

This table provides a quick overview of various MD simulators and tools, highlighting whether they rely on CPUs, GPUs, or both for running molecular dynamics simulations​

Step-by-Step Tutorial:

Step 1: Accessing the Web-Based MD Tool

1. Open your browser and navigate to the web-based MD simulation tool.
2. Log in or create an account. Ensure you have access to AWS for cloud computing.
3. Obtain your AWS access keys (from AWS IAM) and store them securely for later use.

Step 2: Configuring Your Workspace

1. After logging in, create a workspace to store your molecular simulation files.
2. Link the workspace to a cloud storage bucket (e.g., AWS S3). This bucket will store all simulation data, including input files and results.

Case Study Setup: In our case study, we used AWS S3 to manage molecular system data.

Step 3: Uploading Molecular Data

1. Upload molecular data to the workspace. You can either select a local file (e.g., a PDB file) or provide a direct link from the Protein Data Bank (PDB).
2. For this case study, we selected the 4V4L molecular system, a complex system of 128,000 atoms, which increased to 500,000 atoms after processing.

Note: The system needs around 15 GB of RAM, which can be handled by an AWS EC2 t2.2xlarge instance.

Step 4: Generating the Physical Model

1. Use the tool’s “Generate Physical Model” feature to assign atomic properties (e.g., bonds, charges).
2. Create a Protein Structure File (PSF), which contains essential molecular details such as atom charges and bonds.
3. The tool manages this step using VMD software on a t2 instance. Figure 9 illustrates how users can configure the creation of the PSF file​

Step 5: Solvating the Molecular System

1. After generating the physical model, create a solvation box surrounding the molecule with water molecules.
2. Define the size of the box in the tool’s configuration. The VMD software handles this step.

Case Study Note: The tool automatically manages the EC2 instance, adding water molecules to create a solvation box for the 4V4L system (Figure 10)​

Step 6: Equilibrating the System

1. The molecular system needs to be equilibrated to reach a low-energy state before starting the full simulation.
2. Use NAMD software for equilibration, running on an EC2 GPU-optimized p2 instance.
3. Configure relevant parameters like temperature, time steps, and simulation steps. Figure 11 shows an example of this configuration

GPU Acceleration: For heavy workloads such as equilibration and full MD simulations, AWS EC2 instances optimized with GPUs (e.g., p2.16xlarge) are recommended, enabling the use of up to 16 Nvidia Tesla K80 GPUs

Step 7: Running the Full MD Simulation

1. Once equilibrated, configure and run the full MD simulation using a tool like NAMD or GROMACS.
2. Specify simulation parameters (e.g., time step, output frequency) and choose the number of GPUs to be used.
3. Start the simulation, and the tool will manage cloud resources and handle network communication among instances.

Case Study Note: The simulation of the 4V4L molecular system was carried out using AWS EC2 instances, taking advantage of a multi-GPU setup

Step 8: Analyzing the Results

1. Once the simulation completes, access the results stored in your AWS S3 bucket.
2. Download the data for further analysis using visualization tools like VMD.
3. Review logs and simulation metrics provided by the tool.