ProLIF: Command Line Visualization To HTML/PNG

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ProLIF: Command Line Visualization to HTML/PNG

Hey there, fellow researchers!

So, you're diving into the fascinating world of protein-ligand interactions using ProLIF, and you're working on a remote workstation, right? That's awesome! But, as you've found out, sometimes getting those beautiful visualizations in a remote environment can be a bit of a head-scratcher, especially without a fancy remote desktop setup. No worries, though! I'm here to walk you through how to save those ProLIF visualizations – the residue plots, 2D barcode plots, and 3D complex plots – directly to HTML or PNG files from the command line. This way, you can analyze and admire your protein-ligand interactions without the hassle of a GUI. Let's get started!

Understanding the Challenge: Remote Visualization

Okay, so the main hurdle you're facing is that you don't have a graphical interface, like a remote desktop, set up on your remote workstation. This means you can't just open up a Jupyter notebook or run ProLIF directly and expect to see those visualizations pop up on your screen. Instead, you need a way to generate these plots and save them as files that you can easily access and view later. That's where saving to HTML or PNG comes in handy.

Why HTML and PNG?

  • HTML: HTML files are great because they can be interactive, especially for 3D plots. You can often rotate and zoom in on your complex using your web browser. This gives you a more immersive experience when exploring the interaction details. Plus, HTML files are easy to share and open on almost any device. It's like having a mini-website for your protein-ligand complex!
  • PNG: PNG files are perfect for static images. They are widely supported and provide good image quality. They are also super simple to share and include in reports or publications. If you only need a snapshot of your plot, PNG is your go-to format.

Saving Residue Plots

Residue plots are a fantastic way to visualize which residues in your protein are interacting with the ligand. These plots often show interaction frequencies or strengths. Generating them from the command line and saving them to files will be crucial for you.

Using prolif.plot_residue_network()

The prolif.plot_residue_network() function is your best friend here. This function is designed to generate a network graph that displays the interactions. To save this plot to a file, you need to use the right parameters. Here's a basic example:

import prolif
import matplotlib.pyplot as plt

# Assuming you have your ProLIF analysis object (e.g., `lig_contacts`)
# and the necessary data ready.

# Example data (replace with your actual data)
# For example:
# contacts = prolif.contacts.Contacts(...,...) 
# if you have the contacts data

# Create a residue network plot
fig, ax = plt.subplots(figsize=(10, 8))  # Adjust figure size as needed
prolif.plot_residue_network(lig_contacts, ax=ax)

# Save the plot to a PNG file
plt.savefig("residue_plot.png")

# Or save the plot to an HTML file (interactive, if supported)
# You might need to install a library like "plotly" for this
# if you want the interactive feature in HTML files.
# import plotly.graph_objects as go
# prolif.plot_residue_network(lig_contacts, interactive=True, filename="residue_plot.html")
# plt.show()
  • Explanation:
    • First, make sure you've installed ProLIF and matplotlib (pip install prolif matplotlib).
    • Load your ProLIF analysis data. This example assumes you have a lig_contacts object containing your interaction data.
    • Create a matplotlib figure and axes.
    • Call prolif.plot_residue_network() to generate the plot. Make sure you pass the axes object to draw the plot on.
    • Use plt.savefig() to save the plot as a PNG. Specify the filename. Adjust the file format as needed, depending on the format you want (e.g., PNG, PDF, SVG).
    • For HTML output, you might need to use plotly and its related interactive plot functions instead (this depends on the ProLIF version).

Customization Tips

  • Figure Size: Adjust the figsize parameter in plt.subplots() to control the plot dimensions (width, height) for better readability.
  • Labels and Titles: Add labels and a title using ax.set_xlabel(), ax.set_ylabel(), and ax.set_title() to make your plot informative.
  • Colors and Styles: Customize the plot's appearance using matplotlib's extensive styling options. For example, modify node colors, edge widths, or fonts to enhance the visualization.

Generating 2D Barcode Plots

Barcode plots are a neat way to see the sequence of interactions over time (or across different frames of your simulation). These plots are very helpful to see how interactions change during the simulation. Saving these plots in a usable format is therefore critical.

Using ProLIF's Barcode Plotting

ProLIF provides functions to generate these plots as well. Here is a basic example:

import prolif
import matplotlib.pyplot as plt

# Assuming you have your ProLIF analysis object (e.g., `lig_contacts`)

# Create a barcode plot
fig, ax = plt.subplots(figsize=(12, 6))  # Adjust figure size as needed
prolif.plot_barcode(lig_contacts, ax=ax)

# Save the plot to a PNG file
plt.savefig("barcode_plot.png")
  • Explanation:
    • Install matplotlib (if you haven't already).
    • Load your data. For barcode plots, you'll need data that represents interactions over time (usually generated during your ProLIF analysis). The variable name lig_contacts is used as an example.
    • Use prolif.plot_barcode() function to generate the plot. Make sure to pass the axes object to draw the plot on.
    • Use plt.savefig() to save the plot as a PNG. Specify the filename.

Customization Tips

  • Time Scales: If your simulation data has time information, ensure the x-axis (time) is properly scaled.
  • Interaction Types: You can customize the plot to show specific types of interactions (e.g., hydrogen bonds, hydrophobic contacts). This may involve filtering your data before plotting.
  • Appearance: Use matplotlib options to change the plot's colors, line styles, and labels.

Creating 3D Complex Plots

3D plots are great for visualizing the spatial arrangement of your protein-ligand complex. These plots can offer insights into the binding pose and potential steric clashes. This is very cool and can add a lot of value to your analysis.

Using prolif.plot_complex()

To generate 3D complex plots, you'll generally use the prolif.plot_complex() function. The example below uses matplotlib for the 3D plots.

import prolif
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Assuming you have your ProLIF analysis data (e.g., `lig_contacts`)
# and the necessary coordinates data.

# Example data (replace with your actual data)
# For example, you have contact data in `lig_contacts`

# Create a 3D plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# This function may require the coordinates data
# and can be more complex to set up. Please, check the API documentation.
# prolif.plot_complex(lig_contacts, ax=ax)

# Save the plot to a PNG file
plt.savefig("complex_plot.png")
  • Explanation:
    • Install matplotlib and mpl_toolkits (if needed: pip install matplotlib mpl_toolkits).
    • Import Axes3D from mpl_toolkits.mplot3d for the 3D plot.
    • Load your data. You'll need the 3D coordinates of your protein and ligand. Make sure the structure and the interaction data are loaded properly.
    • Create a figure and a 3D axes object using fig.add_subplot(111, projection='3d').
    • Call prolif.plot_complex(). This function may take your interaction data and coordinates as inputs.
    • Save the plot to a file using plt.savefig(). You can specify a format like PNG.

Tips for 3D Plots

  • Coordinate Data: Ensure you have the correct 3D coordinates for your protein and ligand. This is crucial for the plot.
  • Interactions: Choose which types of interactions to display (e.g., hydrogen bonds, salt bridges) to avoid clutter and highlight the most important features.
  • View Angle: Experiment with different viewing angles using ax.view_init() to get the best perspective on your complex. For example, ax.view_init(elev=20, azim=30).
  • Interactive HTML: Consider using a library like plotly for interactive 3D plots. These can allow you to rotate and zoom in on the complex in your web browser, providing a better understanding of the binding interactions.

Putting it all Together: Command-Line Workflow

Here's a step-by-step workflow you can use from the command line:

  1. Set up your environment: Make sure you have ProLIF and the necessary dependencies (like matplotlib, and possibly plotly) installed on your remote workstation. Use pip install prolif matplotlib plotly.
  2. Run your ProLIF analysis: Use your scripts or notebooks to generate your ProLIF analysis data (contacts, etc.). Ensure your code is set up for command-line execution (e.g., no interactive plots).
  3. Generate plots and save to files: Modify your scripts to generate the plots (residue plots, barcode plots, 3D complex plots) and use the plt.savefig("filename.png") or similar functions to save them as PNG or HTML files.
  4. Transfer the files: Once your script is done running, transfer the generated PNG or HTML files from your remote workstation to your local machine using tools like scp, rsync, or an SFTP client.
  5. View your plots: Open the PNG files directly, or open the HTML files in your web browser to view the interactive plots.

Troubleshooting

  • Missing Dependencies: Double-check that all required libraries (ProLIF, matplotlib, plotly, etc.) are installed on your remote machine.
  • No Display: If you're still facing issues, make sure your scripts aren't trying to open plots in a display that doesn't exist (like on the remote server). Save the plots directly to files without calling display-related functions.
  • File Paths: Verify that the file paths you specify in plt.savefig() are correct and that the script has the necessary permissions to write to those locations.
  • Interactive HTML Issues: If you're having trouble with interactive HTML, check your plotly or similar library installation and ensure your code is set up to generate interactive plots correctly.

Conclusion

There you have it! With these methods, you can successfully generate and save ProLIF visualizations to HTML or PNG files from the command line on your remote workstation. This workflow allows you to analyze and visualize your protein-ligand interactions without the need for a graphical interface. Now you can dive deep into your analysis and make stunning visuals for your reports, publications, and presentations. Happy visualizing, and keep up the great work, guys!