Polyunsaturated fatty acids (PUFAs), but not saturated fatty acids, modulate ion channels such as the cardiac KCNQ1 channel, although the mechanism is not completely understood. Using both simulations and experiments, we find that PUFAs interact directly with the KCNQ1 channel via two different binding sites: one at the voltage sensor and one at the pore. These two amphiphilic binding pockets stabilize the negatively charged PUFA head group by electrostatic interactions with R218, R221, and K316, while the hydrophobic PUFA tail is selectively stabilized by cassettes of hydrophobic residues. The rigid saturated tail of stearic acid prevents close contacts with KCNQ1. By contrast, the mobile tail of PUFA linoleic acid can be accommodated in the crevice of the hydrophobic cassette, a defining feature of PUFA selectivity in KCNQ1. In addition, we identify Y268 as a critical PUFA anchor point underlying fatty acid selectivity. Combined, this study provides molecular models of direct interactions between PUFAs and KCNQ1 and identifies selectivity mechanisms. Long term, this understanding may open new avenues for drug development based on PUFA mechanisms.

In this work, we introduced an improved linear interaction energy (LIE) method parameterization for computations of protein–ligand binding free energies. The protocol, coined LIE-D, builds on the linear relationship between the empirical coefficient γ in the standard LIE scheme and the D parameter, introduced in our work. The D-parameter encompasses the balance (difference) between electrostatic (polar) and van der Waals (nonpolar) energies in protein–ligand complexes. Leave-one-out cross-validation showed that LIE-D reproduced accurately the absolute binding free energies for our training set of protein–ligand complexes (<|error|> = 0.92 kcal/mol, SDerror = 0.66 kcal/mol, R(2) = 0.90, QLOO(2) = 0.89, and sPRESS(LOO) = 1.28 kcal/mol). We also demonstrated LIE-D robustness by predicting accurately the binding free energies for three different protein–ligand systems outside the training data set, where the electrostatic and van der Waals interaction energies were calculated with different force fields.

The human ether-á-go-go–related gene (hERG1) channel conducts small outward K+ currents that are critical for cardiomyocyte membrane repolarization. The gain-of-function mutation N629D at the outer mouth of the selectivity filter (SF) disrupts inactivation and K+-selective transport in hERG1, leading to arrhythmogenic phenotypes associated with long-QT syndrome. Here, we combined computational electrophysiology with Markov state model analysis to investigate how SF-level gating modalities control selective cation transport in wild-type (WT) and mutant (N629D) hERG1 variants. Starting from the recently reported cryogenic electron microscopy (cryo-EM) open-state channel structure, multiple microseconds-long molecular-dynamics (MD) trajectories were generated using different cation configurations at the filter, voltages, electrolyte concentrations, and force-field parameters. Most of the K+ permeation events observed in hERG1-WT simulations occurred at microsecond timescales, influenced by the spontaneous dehydration/rehydration dynamics at the filter. The SF region displayed conductive, constricted, occluded, and dilated states, in qualitative agreement with the well-documented flickering conductance of hERG1. In line with mutagenesis studies, these gating modalities resulted from dynamic interaction networks involving residues from the SF, outer-mouth vestibule, P-helices, and S5–P segments. We found that N629D mutation significantly stabilizes the SF in a state that is permeable to both K+ and Na+, which is reminiscent of the SF in the nonselective bacterial NaK channel. Increasing the external K+ concentration induced “WT-like” SF dynamics in N629D, in qualitative agreement with the recovery of flickering currents in experiments. Overall, our findings provide an understanding of the molecular mechanisms controlling selective transport in K+ channels with a nonconventional SF sequence.

Psychostimulant drugs, such as cocaine, inhibit dopamine reuptake via blockading the dopamine transporter (DAT), which is the primary mechanism underpinning their abuse. Atypical DAT inhibitors are dissimilar to cocaine and can block cocaine-or methamphetamine-induced behaviors, supporting their development as part of a treatment regimen for psychostimulant use disorders. When developing these atypical DAT inhibitors as medications, it is necessary to avoid off-target binding that can produce unwanted side effects or toxicities. In particular, the blockade of a potassium channel, human ether-a-go-go (hERG), can lead to potentially lethal ventricular tachycardia. In this study, we established a counter screening platform for DAT and against hERG binding by combining machine learning-based quantitative structure−activity relationship (QSAR) modeling, experimental validation, and molecular modeling and simulations. Our results show that the available data are adequate to establish robust QSAR models, as validated by chemical synthesis and pharmacological evaluation of a validation set of DAT inhibitors. Furthermore, the QSAR models based on subsets of the data according to experimental approaches used have predictive power as well, which opens the door to target specific functional states of a protein. Complementarily, our molecular modeling and simulations identified the structural elements responsible for a pair of DAT inhibitors having opposite binding affinity trends at DAT and hERG, which can be leveraged for rational optimization of lead atypical DAT inhibitors with desired pharmacological properties.

Significance Cholesterol is one of the main components found in plasma membranes and is involved in lipid-dependent signaling enabled by integral membrane proteins such as inwardly rectifying potassium (Kir) channels. Similar to other ion channels, most of the Kir channels are down-regulated by cholesterol. One of the very few notable exceptions is Kir3.4, which is up-regulated by this important lipid. Here, we discovered and characterized a molecular switch that controls the impact (up-regulation vs. down-regulation) of cholesterol on Kir3.4. Our results provide a detailed molecular mechanism of tunable cholesterol regulation of a potassium channel.