DNET: A Graph-Based Tool and Workflow for Dynamic Hydrogen-Bond Networks and Applications for Visual Rhodopsins

G Protein-Coupled Receptors (GPCRs) mediate signal transduction
across cellular membranes and are major drug targets. Activation of
these receptors upon binding of an extracellular ligand involves propagation
of structural change across the transmembrane domain to the cytoplasmic
G protein partner, a process generally thought to involve dynamic
hydrogen­(H)-bond networks. Here we present DNET, a graph-based tool
and workflow that enables efficient computations of dynamic protein–water
H-bond networks. DNET is a fully portable Python tool that reads simulation
trajectories, computes graphs of the dynamic protein–water
H-bond networks, and generates, for each H-bonding residue, a residue
summary that includes water interactions, H-bond time series, histograms,
potential of mean force estimates, and the number of conformations
of the H-bond. To facilitate estimates of pKa fluctuations within the H-bond network, DNET calls PROPKA
and computes, for each titratable residue that is part of the H-bond
network, time series and analyses of the pKa estimates. To illustrate the usefulness of DNET we apply it to study
the wild-type and two mutations of jumping spider rhodopsin 1, JSR-1,
a visual rhodopsin GPCR activated by the photoisomerization of the
covalently bound retinal chromophore. The UV–vis data we present
here demonstrate that the mutated JSR-1 proteins express, but both
have an altered electrostatic environment of the retinal Schiff base.
The DNET analyses indicate a highly complex dynamics of the retinal
H-bond network, with some H-bonds that have only one conformational
mode, and other H-bonds with multiple conformational modes separated
by small energy barriers, and pKa fluctuations
that associate with the H-bond dynamics. The mutations associate with
an altered H-bond network of the retinal Schiff base.