mdsa-tools 0.1.5

The Weir Labs H-bond Systems Analyses modules!

mdsa-tools: Tools for systems-level analysis of Molecular Dynamics (MD) simulations

Pipeline overview

We start from an MD trajectory and generate per-frame interaction networks (graphs/adjacency matrices). Adjacencies are flattened (row-wise) into vectors; stacking these per-frame vectors yields a feature matrix suitable for clustering (e.g., k-means) and dimensionality reduction (PCA/UMAP). Results can be visualized with graphs, scatter plots, MDCcircos plots (residue H-bonding), or replicate maps of frame-level measurements of interest.

An additional module uses cluster assignments as candidate substates for Markov state model (MSM) analysis.

Install

pip install mdsa-tools
# Optional:
# pip install "mdsa-tools[docs]"   # if you want to build the docs locally
# pip install "mdsa-tools[examples]"  # if you define this extra for demo deps

Systems Problem Area:

At the Weir Lab at Wesleyan University, we perform molecular dynamics (MD) simulations of a ribosomal subsystem to study tuning of protein translation by the CAR interaction surface- a ribosomal interface identified by the lab that interacts with the +1 codon (poised to enter the ribosome A site). Our "computational genetics" research focuses on modifying adjacent codon identities at the A-site and the +1 positions to model how changes at these sites influence the behavior of the CAR surface and corellate with translation rate variations.

Quickstart example (see examples for more use-cases;contour plots, UMAP, MSM, etc):

Google collab viewer: Jupyter notebook env:

from mdsa_tools.Data_gen_hbond import trajectory
from mdsa_tools.Analysis import systems_analysis
import numpy as np

# --- Load trajectories (replace with your own paths) ---
top1 = "/path/to/system1.prmtop"
traj1 = "/path/to/system1.mdcrd"
top2 = "/path/to/system2.prmtop"
traj2 = "/path/to/system2.mdcrd"

sys1 = trajectory(trajectory_path=traj1, topology_path=top1).create_system_representations()
sys2 = trajectory(trajectory_path=traj2, topology_path=top2).create_system_representations()

# Optionally save for reuse
# np.save("example_systems/system_one.npy", sys1)
# np.save("example_systems/system_two.npy", sys2)

# --- Analyze ---
analyzer = systems_analysis([sys1, sys2])

# Clustering
sil_labels, elbow_labels, sil_centers, elbow_centers = analyzer.cluster_system_level(
    outfile_path="out/syskmeans/", max_clusters=25
)
print("Clustering successfully completed.")

# Dimensionality reduction (PCA or UMAP); color by cluster labels
analyzer.reduce_systems_representations(
    outfile_path="out/PCA/test_", 
    method="PCA",
    colormappings=sil_labels
)
print("PCA reduction successful.")