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 pK_a 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 pK_a 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 pK_a fluctuations that associate with the H-bond dynamics. The mutations associate with an altered H-bond network of the retinal Schiff base.