MDavocado: Analysis and Visualization of Protein Motion by Time-Dependent Angular Diagrams

Extracting meaningful information from atomistic molecular
dynamics
(MD) simulations of proteins remains a challenging task due to the
high-dimensionality and complexity of the data. MD simulations yield
trajectories that contain the positions of thousands of atoms in millions
of steps. Gaining a comprehensive understanding of local dynamical
events across the entire trajectory is often difficult. Here, we present
a novel approach to visualize MD trajectories in the form of time-dependent
Ramachandran plots. Specialized data aggregation techniques are employed
to address the challenge of plotting millions of data points on a
single image, thereby ensuring that the analysis is independent of
the molecule size and/or length of the MD simulation. This approach
facilitates quick identification of flexible and dynamic regions,
and its strength is the ability to simultaneously observe the movements
of all amino acids over time. The Python program MDavocado is freely
available at GitHub (https://github.com/zoranstefanic/MDavocado).