BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories

Bayesian
network modeling (BN modeling, or BNM) is an interpretable
machine learning method for constructing probabilistic graphical models
from the data. In recent years, it has been extensively applied to
diverse types of biomedical data sets. Concurrently, our ability to
perform long-time scale molecular dynamics (MD) simulations on proteins
and other materials has increased exponentially. However, the analysis
of MD simulation trajectories has not been data-driven but rather
dependent on the user’s prior knowledge of the systems, thus
limiting the scope and utility of the MD simulations. Recently, we
pioneered using BNM for analyzing the MD trajectories of protein complexes.
The resulting BN models yield novel fully data-driven insights into
the functional importance of the amino acid residues that modulate
proteins’ function. In this report, we describe the BaNDyT
software package that implements the BNM specifically attuned to the
MD simulation trajectories data. We believe that BaNDyT is the first
software package to include specialized and advanced features for
analyzing MD simulation trajectories using a probabilistic graphical
network model. We describe here the software’s uses, the methods
associated with it, and a comprehensive Python interface to the underlying
generalist BNM code. This provides a powerful and versatile mechanism
for users to control the workflow. As an application example, we have
utilized this methodology and associated software to study how membrane
proteins, specifically the G protein-coupled receptors, selectively
couple to G proteins. The software can be used for analyzing MD trajectories
of any protein as well as polymeric materials.