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Voltage-gated sodium, potassium, and calcium channels consist of four voltage-sensing domains (VSDs) that surround a central pore domain and transition from a down state to an up state in response to membrane depolarization. While many types of drugs bind pore domains, the number of organic molecules known to bind VSDs is limited. The Hv1 voltage-gated proton channel is made of two VSDs and does not contain a pore domain, providing a simplified model for studying how small ligands interact with VSDs. Here, we describe a ligand, named HIF, that interacts with the Hv1 VSD in the up and down states. We find that HIF rapidly inhibits proton conduction in the up state by blocking the open channel, as previously described for 2-guanidinobenzimidazole and its derivatives. HIF, however, interacts with a site slowly accessible in the down state. Functional studies and MD simulations suggest that this interaction traps the compound in a narrow pocket lined with charged residues within the VSD intracellular vestibule, which results in slow recovery from inhibition. Our findings point to a “wrench in gears” mechanism whereby side chains within the binding pocket trap the compound as the teeth of interlocking gears. We propose that the use of screening strategies designed to target binding sites with slow accessibility, similar to the one identified here, could lead to the discovery of new ligands capable of interacting with VSDs of other voltage-gated ion channels in the down state.

 

The human voltage-gated proton channel Hv1 is a drug target for cancer, ischemic stroke, and neuroinflammation. It resides on the plasma membrane and endocytic compartments of a variety of cell types, where it mediates outward proton movement and regulates the activity of NOX enzymes. Its voltage-sensing domain (VSD) contains a gated and proton-selective conduction pathway, which can be blocked by aromatic guanidine derivatives such as 2-guanidinobenzimidazole (2GBI). Mutation of Hv1 residue F150 to alanine (F150A) was previously found to increase 2GBI apparent binding affinity more than two orders of magnitude. Here, we explore the contribution of aromatic interactions between the inhibitor and the channel in the presence and absence of the F150A mutation, using a combination of electrophysiological recordings, classic mutagenesis, and site-specific incorporation of fluorinated phenylalanines via nonsense suppression methodology. Our data suggest that the increase in apparent binding affinity is due to a rearrangement of the binding site allowed by the smaller residue at position 150. We used this information to design new arginine mimics with improved affinity for the nonrearranged binding site of the wild-type channel. The new compounds, named “Hv1 Inhibitor Flexibles” (HIFs), consist of two “prongs,” an aminoimidazole ring, and an aromatic group connected by extended flexible linkers. Some HIF compounds display inhibitory properties that are superior to those of 2GBI, thus providing a promising scaffold for further development of high-affinity Hv1 inhibitors.

 

Predictive modeling is arguably one of the most important tasks actuaries face in their day-to-day work. In practice, actuaries may have a number of reasonable models to consider, all of which will provide different predictions. The most common strategy is first to use some kind of model selection tool to select a ``best model" and then to use that model to make predictions. However, there is reason to be concerned about the use of the classical distribution theory to develop predictions because this theory ignores the selection effect. Since accuracy of predictions is crucial to the insurer’s pricing and solvency, care is needed to develop valid prediction methods. This paper investigates the effects of model selection on the validity of classical prediction tools and makes some recommendations for practitioners.