Protons taken hostage: Dynamic H-bond networks of the pH-sensing GPR68.

Proton-sensing G Protein Coupled Receptors (GPCRs) sense changes in the extracellular pH to effect cell signaling for cellular homeostasis. They tend to be overexpressed in solid tumors associated with acidic extracellular pH, and are of direct interest as drug targets. How proton-sensing GPCRs sense extracellular acidification and activate upon protonation change is important to understand, because it may guide the design of therapeutics. Lack of publicly available experimental structures make it challenging to discriminate between conflicting mechanisms proposed for proton-binding, as main roles have been assigned to either an extracellular histidine cluster or to an internal carboxylic triad. Here we present a protocol to derive and evaluate structural models of the proton-sensing GPR68. This approach integrates state-of-the-art homology modeling with microsecond-timescale atomistic simulations, and with a detailed assessment of the compatibility of the structural models with known structural features of class A GPCRs. To decipher structural elements of potential interest for protonation-coupled conformational changes of GPR68, we used the best-compatible model as a starting point for independent atomistic simulations of GPR68 with different protonation states, and graph computations to characterize the response of GPR68 to changes in protonation. We found that GPR68 hosts an extended hydrogen-bond network that inter-connects the extracellular histidine cluster to the internal carboxylic triad, and which can even reach groups at the cytoplasmic G-protein binding site. Taken together, results suggest that GPR68 relies on dynamic, hydrogen-bond networks to inter-connect extracellular and internal proton-binding sites, and to elicit conformational changes at the cytoplasmic G-protein binding site.

Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors.

We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity.