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How to control PyMOL from Jupyter notebooks

written by Eric J. Ma on 2024-05-16 | tags: pymol jupyter notebooks python scripting protein visualization pdb files data science gpt-4 matplotlib plotting bioinformatics automation


For a small request at work, I recently learned how to control PyMOL from Jupyter notebooks, specifically to script it without leaving the Jupyter notebook UI, and I thought it was pretty good! Here's how we do it.

We first need to ensure that we have PyMOL-open-source installed on our environment:

mamba install pymol-open-source

This gives us the pymol package, which we can import:

import pymol
from pymol import cmd

Suppose we have a collection of PDB files, say of nanobody VHH structures:

from pathlib import Path
vhh_pdb_files = list(Path(".").glob("*.pdb"))

Using the PyMOL package alongside the built-in os package, we can now define two helper functions:

import os
from pathlib import Path

def setup_PyMOL():
    # may need to do `sudo apt-get install xvfb` if not available
    os.system("Xvfb :99 &")
    os.environ["DISPLAY"] = ":99"
    PyMOL.finish_launching(['PyMOL', '-c'])

def process_pdb(pdb_path: Path):
    cmd.load(pdb_path, "protein_structure")
    cmd.hide("everything", "protein_structure")
    cmd.show("cartoon", "protein_structure")
    cmd.color("green", "protein_structure")

    # Select and color the CDRH3 region in purple
    # CDRH3 region: residues 105 to 118 (inclusive)
    cmd.select("CDRH3", "protein_structure and resi 105-118")
    cmd.color("purple", "CDRH3")
    cmd.zoom("protein_structure")
    cmd.png(f"{pdb_path.with_suffix('.png')}", width=1080, height=1080, dpi=300, ray=1)
    cmd.delete("all") # Clears the loaded structure for the next one

Finally, we can do a for-loop over PDB files:

from pyprojroot import here
from tqdm.auto import tqdm

setup_PyMOL()
for pdb_path in tqdm(vhh_pdb_files):
    process_pdb(pdb_path)
# Quit PyMOL
cmd.quit()
# Kill Xvfb
os.system("killall Xvfb")

Bonus: I wanted to plot all of the PDB files in a grid.

import matplotlib.pyplot as plt
from sqdiv import n_rows_cols

num_rows, num_cols = n_rows_cols(len(vhh_pdb_files))
base_size = 3

fig, axes = plt.subplots(nrows=num_rows, ncols=num_cols, figsize=(base_size * num_cols, base_size * num_rows))

for i, pdb_name in enumerate(vhh_pdb_files):
    ax = axes.flatten()[i]
    img = plt.imread((Path(pdb_name)).with_suffix(".png"))
    ax.imshow(img)
    ax.set_title(pdb_name)

    # Despine all the spines for each plot
    for spine in ax.spines.values():
        spine.set_visible(False)

    # Hide ticks
    ax.set_xticks([])
    ax.set_yticks([])

plt.tight_layout()

This gives us an image that looks like this:

I picked this up within minutes using GPT4.

The subtext of this post is that GPT-4 is an incredibly useful tool for helping me learn new things.

I haven't had many chances to use PyMOL throughout my career, and even when I did, it was mostly in manual mode to visualize protein structures. Scripting PyMOL wasn't something I tried to do, as this was before I felt proficient with Python, and I wasn't processing more than a handful of structures. However, with modern data science idioms, such as working within a Jupyter notebook for exploratory work, using PyMOL "the old way" would have disrupted my workflow by necessitating a context switch away from notebooks into PyMOL. As such, I wondered if we could control PyMOL through Python code directly.

To discover whether this was possible, my first instincts were that if I could think of this idea, someone smarter than me probably already had thought of it. They probably came up with a solution a few years ahead of me, implying that it should exist within GPT4's training set! My first instinct was to go and ask GPT4 how to do so, which led me to discover the pymol-open-source on the conda-forge channel alongside the code above!

Since scripting PyMOL is a form of programming and since the syntax is usually the hardest to remember, the encoded knowledge within GPT-4's weights is a great way to ramp up my exposure to the syntax quickly. Additionally, since I know that I learn fastest in an applied setting, where I pick up something new en route to doing something I already need to do, prompt-hacking GPT-4 as a personalized tutor is incredibly compatible with how I learn.


Cite this blog post:
@article{
    ericmjl-2024-how-notebooks,
    author = {Eric J. Ma},
    title = {How to control PyMOL from Jupyter notebooks},
    year = {2024},
    month = {05},
    day = {16},
    howpublished = {\url{https://ericmjl.github.io}},
    journal = {Eric J. Ma's Blog},
    url = {https://ericmjl.github.io/blog/2024/5/16/how-to-control-pymol-from-jupyter-notebooks},
}
  

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