Silicon-Backbone Ramachandran-Equivalent Dihedral Analysis for Molecular Dynamics Simulations (Karagol-Ramachandran Plots)

The characterisation of conformational space in silicon-backbone polymers via sliding dihedral windows may extend classical Ramachandran analysis beyond protein systems and improve the interpretation of non-biological polymer dynamics.

This repository contains the source code for computing φ/ψ-equivalent dihedral angles along homogeneous silicon backbones from GROMACS molecular dynamics trajectories. It serves as a direct replacement for gmx rama when carbon atoms are substituted by silicon.

Installation

pip install karagol-rama

Usage

Command Line

The CLI mirrors the gmx rama interface:

karagol-rama -s md.tpr -f md.xtc -o rama_out.xvg

This is the direct equivalent of:

gmx rama -s md.tpr -f md.xtc -o rama_md_0_1.xvg

All CLI options

Examples

Run with default silicon selection:

karagol-rama -s md.tpr -f md.xtc -o rama_out.xvg

Use a different backbone element (e.g. germanium):

karagol-rama -s md.tpr -f md.xtc -o rama_out.xvg --element Ge

Provide a custom atom selection if silicon atoms are not detected automatically:

karagol-rama -s md.tpr -f md.xtc -o rama_out.xvg --selection "resname POL and name Si*"

Run quietly with no terminal output:

karagol-rama -s md.tpr -f md.xtc -o rama_out.xvg -q

Python API

You can also use karagol-rama directly in Python scripts or Colab/Jupyter notebooks.

One-shot run

from karagol_rama import KaragolRama

KaragolRama(
    tpr_file="md.tpr",
    traj_file="md.xtc",
    output_file="rama_out.xvg",
    element="Si",
).run_all()

Step-by-step

from karagol_rama import KaragolRama

analyser = KaragolRama(
    tpr_file="md.tpr",
    traj_file="md.xtc",
    output_file="rama_out.xvg",
    element="Si",
)

analyser.load()       # load topology & trajectory
analyser.run()        # compute phi/psi dihedrals
analyser.save()       # write .xvg file
analyser.summary()    # print statistics

Custom selection

analyser = KaragolRama(
    tpr_file="md.tpr",
    traj_file="md.xtc",
    output_file="rama_out.xvg",
    selection="resname POL and name Si*",
)
analyser.run_all()

Plotting the output

import numpy as np
import matplotlib.pyplot as plt

data = np.loadtxt("rama_out.xvg", comments=["#", "@"])

plt.figure(figsize=(6, 6))
plt.scatter(data[:, 0], data[:, 1], s=1, alpha=0.3, color="steelblue")
plt.xlabel("Phi (degrees)")
plt.ylabel("Psi (degrees)")
plt.title("Karagol-Ramachandran Plot – Silicon Backbone")
plt.xlim(-180, 180)
plt.ylim(-180, 180)
plt.tight_layout()
plt.show()

Requirements

• Python ≥ 3.8
• MDAnalysis ≥ 2.0.0
• NumPy ≥ 1.20.0

pip install MDAnalysis numpy

Citation

If you use this package in your research, please cite the MDAnalysis library:

• Gowers, R. J. et al. MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. Proceedings of the 15th Python in Science Conference (2016).
• Michaud-Agrawal, N. et al. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 32, 2319–2327 (2011).

License

This project is licensed under the MIT License — see the LICENSE file for details.