Tutorial 1: Residue Contact Analysis of Lysozyme MD Analysis

Prerequisites

Before starting, ensure you have the following installed:

• GROMACS for molecular dynamics simulation.
• GMX_RRCS installed on your system.
• Python (3.7 or later) with necessary dependencies such as NumPy and Matplotlib.

Step 1: Preparing the Simulation Data

This step is optional and requires you to process the trajectory using GROMACS. However, we strongly recommend thoroughly processing the trajectory before computing RRCS, as this significantly impacts the program's performance and efficiency. This tutorial uses a classic GROMACS lysozyme MD case to demonstrate RRCS analysis.

After running the simulation, you will obtain an XTC file containing trajectory data. To extract a protein-only trajectory with a time step of 0.1 ns over 300 ns, run:

echo 2 2 2 | gmx trjconv \
-f md_0_1.xtc \           # Input trajectory
-s md_0_1.tpr \           # Topology file
-fit rot+trans \          # Rotational/translational fitting
-center \                 # Center protein
-dt 100 \                 # 100ps interval (0.1ns)
-b 200000 \               # Start at 200ns
-e 300000 \               # End at 300ns
-o md_0_1_processed.xtc   # Output file

echo 2 | gmx convert-tpr \
-s md_0_1.tpr \
-o md_0_1_processed.tpr            # Output a new TPR file that matche the processed trajectory.

echo 2 | gmx trjconv \
-f md_0_1_processed.xtc \
-s md_0_1_processed.tpr \
-o md_0_1_processed_dump0.gro \    # Output the GRO format coordinate file.
-dump 0                            # Extracts structure information at 0 ps.
            

Step 2: Using the Command Line Interface computing RRCS

After preprocessing the trajectory, we can now compute Residue-Residue Contact Strength (RRCS) using GMX_RRCS. We assume that GMX_RRCS has already been installed, and you can execute the following command to perform the calculation:

gmx_RRCS \
--top_file md_0_1_processed_dump0.gro \      # PDB topology
--traj_file md_0_1_processed.xtc \           # Preprocessed trajectory file
--res_file tutorial_1_residue_pair.txt \     # File specifying the residue pairs for RRCS calculation
--output_dir . \                             # Output directory
--output_file tutorial_1_rrcs_result.txt \   # Output file(s)
--num_processes 100                          # Number of processes for parallel computation
--filter_threshold 0.0                       # Export results above 0.0 to a specified file

Step 3: Visualizing RRCS Data

After completing the RRCS calculation, we will now demonstrate some example analyses. Please note that these are just a few possible use cases of RRCS, and you can tailor the analysis to your specific research objectives.

Example 1: Temporal Evolution of RRCS Between Two Residues

In this example, we analyze how the RRCS between Residue PHE34 and Residue ARG114 changes over time. A line plot is generated to visualize the interaction dynamics between these two residues throughout the simulation. You can generate the RRCS plot using the following command:

plot_rrcs_line tutorial_1_rrcs_result.txt tutorial_1_rrcs_result.png SYSTEM:34PHE-SYSTEM:114ARG -w 15

After executing the command, you will obtain a line plot that depicts the temporal evolution of RRCS (Example output file: tutorial_1_rrcs_result.png).

• X-axis: Represents the frames of the simulation trajectory, indicating different time points in the simulation.
• Y-axis: Represents RRCS (Relative Residue Contact Strength), which quantifies the strength of interaction between the specified residue pair over time.

This plot allows you to observe how the interaction between Residue PHE34 and Residue ARG114 changes over time. You may identify trends such as increasing or decreasing interaction strength and detect stable interaction patterns within the simulation.

Example 2: Correlation between Mindist and RRCS

In this example, we analyze the correlation between Minimum Distance (Mindist) and Relative Residue Contact Strength (RRCS).

First, we create an index file that includes Residues PHE34 and ARG114. After executing the following command, enter 2 & r 25 and 2 & r 124 to select the respective residues:

gmx make_ndx -f md_0_1_processed.tpr -o md_0_1_processed.ndx

Next, we use the gmx mindist command to compute the minimum distance between LEU25 and ILE124:

echo 10 11 | gmx mindist -f md_0_1_processed.xtc -s md_0_1_processed.tpr -n md_0_1_processed.ndx -od md_0_1_processed_mindist.xvg

Next, We plot Mindist (X-axis) against RRCS (Y-axis) to visualize their relationship. It is important to highlight the fundamental methodological differences between RRCS and minimum distance (Mindist):

Due to this methodological distinction, RRCS captures richer multi-atom interaction information, including transient yet significant dynamic contacts, which the minimum distance metric inherently overlooks, especially in cases involving tight, fluctuating interactions.

Example 3: Generating a Bar Chart of the Total RRCS for Specific Residues

In this example, we generate a bar chart displaying the total RRCS of specific residues with all other residues, providing an intuitive visualization of their interaction strength with the surrounding environment. Run the following command:

gmx_rrcs_count -i tutorial_1_rrcs_result.txt -o tutorial_1_rrcs_bar.png -r '26,33,34,121,124'

Additional Resources

• Official Documentation: GitHub Repository
• Simulation Tutorial: GROMACS Lysozyme Tutorial
• Visualization Tools:
  • UCSF ChimeraX
  • VMD
  • PyMOL