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Many voltage-gated potassium (Kv) channels display a time-dependent phenomenon called C-type inactivation, whereby prolonged activation by voltage leads to the inhibition of ionic conduction, a process that involves a conformational change at the selectivity filter toward a non-conductive state. Recently, a high-resolution structure of a strongly inactivated triple-mutant channel kv1.2-kv2.1-3m revealed a novel conformation of the selectivity filter that is dilated at its outer end, distinct from the well-characterized conductive state. While the experimental structure was interpreted as the elusive non-conductive state, our molecular dynamics simulations and electrophysiological measurements show that the dilated filter of kv1.2-kv2.1-3m is conductive and, as such, cannot completely account for the inactivation of the channel observed in the structural experiments. The simulation shows that an additional conformational change, implicating isoleucine residues at position 398 along the pore lining segment S6, is required to effectively block ion conduction. The I398 residues from the four subunits act as a state-dependent hydrophobic gate located immediately beneath the selectivity filter. These observations are corroborated by electrophysiological experiments showing that ion permeation can be resumed in the kv1.2-kv2.1-3m channel when I398 is mutated to an asparagine—a mutation that does not abolish C-type inactivation since digitoxin (AgTxII) fails to block the ionic permeation of kv1.2-kv2.1-3m_I398N. As a critical piece of the C-type inactivation machinery, this structural feature is the potential target of a broad class of quaternary ammonium (QA) blockers and negatively charged activators thus opening new research directions toward the development of drugs that specifically modulate gating states of Kv channels.

Abstract Small-molecule modulators of diverse voltage-gated K+ (Kv) channels may help treat a wide range of neurological disorders. However, developing effective modulators requires understanding of their mechanism of action. We apply an orthogonal approach to elucidate the mechanism of action of an imidazolidinedione derivative (AUT5), a highly selective positive allosteric modulator of Kv3.1 and Kv3.2 channels. AUT5 modulation involves positive cooperativity and preferential stabilization of the open state. The cryo-EM structure of the Kv3.1/AUT5 complex at a resolution of 2.5 Å reveals four equivalent AUT5 binding sites at the extracellular inter-subunit interface between the voltage-sensing and pore domains of the channel’s tetrameric assembly. Furthermore, we show that the unique extracellular turret regions of Kv3.1 and Kv3.2 essentially govern the selective positive modulation by AUT5. High-resolution apo and bound structures of Kv3.1 demonstrate how AUT5 binding promotes turret rearrangements and interactions with the voltage-sensing domain to favor the open conformation.

Studies on the interaction sites of peptide toxins and ion channels typically involve site-directed mutations in toxins. However, natural mutant toxins exist among them, offering insights into how the evolutionary process has conserved crucial sequences for activities and molecular target selection. In this study, we present a comparative investigation using electrophysiological approaches and computational analysis between two alpha toxins from evolutionarily close scorpion species of the genus Tityus, namely, Tst3 and Ts3 from T. stigmurus and T. serrulatus, respectively. These toxins exhibit three natural substitutions near the C-terminal region, which is directly involved in the interaction between alpha toxins and Nav channels. Additionally, we characterized the activity of the Tst3 toxin on Nav1.1-Nav1.7 channels. The three natural changes between the toxins did not alter sensitivity to Nav1.4, maintaining similar intensities regarding their ability to alter opening probabilities, delay fast inactivation, and induce persistent currents. Computational analysis demonstrated a preference for the down conformation of VSD4 and a shift in the conformational equilibrium towards this state. This illustrates that the sequence of these toxins retained the necessary information, even with alterations in the interaction site region. Through electrophysiological and computational analyses, screening of the Tst3 toxin on sodium isoform revealed its classification as a classic α-NaTx with a broad spectrum of activity. It effectively delays fast inactivation across all tested isoforms. Structural analysis of molecular energetics at the interface of the VSD4-Tst3 complex further confirmed this effect.

In the category of functional low-affinity interactions, small ligands may interact with multiple protein sites in a highly degenerate manner. Better conceived as a partition phenomenon at the molecular interface of proteins, such low-affinity interactions appear to be hidden to our current experimental resolution making their structural and functional characterization difficult in the low concentration regime of physiological processes. Characterization of the partition phenomenon under higher chemical forces could be a relevant strategy to tackle the problem provided the results can be scaled back to the low concentration range. Far from being trivial, such scaling demands a concentration-dependent understanding of self-interactions of the ligands, structural perturbations of the protein, among other molecular effects. Accordingly, we elaborate a novel and detailed concentration-dependent thermodynamic analysis of the partition process of small ligands aiming at characterizing the stability and structure of the dilute phenomenon from high concentrations. In analogy to an “aggregate” binding constant of a small molecule over multiple sites of a protein receptor, the model defines the stability of the process as a macroscopic equilibrium constant for the partition number of ligands that can be used to analyze biochemical and functional data of two-component systems driven by low-affinity interactions. Acquisition of such modeling-based structural information is expected to be highly welcome by revealing more traceable protein-binding spots for non-specific ligands.

Abstract The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were shown to store the strongest mutual information (MI) signal to discriminate MSA concatenations with the largest fraction of correct pairings. Although that signal might be of practical relevance in the search for an effective heuristic to solve the problem, the number of MSA concatenations with near-native MI is large, imposing severe limitations. Here, a Genetic Algorithm that explores possible MSA concatenations according to a MI maximization criteria is shown to find degenerate solutions with two error sources, arising from mismatches among (i) similar and (ii) non-similar sequences. If mistakes made among similar sequences are disregarded, type-(i) solutions are found to resolve correct pairings at best true positive (TP) rates of 70%—far above the very same estimates in type-(ii) solutions. A machine learning classification algorithm helps to show further that differences between optimized solutions based on TP rates are not artificial and may have biological meaning associated with the three-dimensional distribution of the MI signal. Type-(i) solutions may therefore correspond to reliable results for predictive purposes, found here to be more likely obtained via MI maximization across protein systems having a minimum critical number of amino acid contacts on their interaction surfaces (N > 200).

Here, we investigate the contributions of coevolutive, evolutive and stochastic information in determining protein-protein interactions (PPIs) based on primary sequences of two interacting protein families A and B. Specifically, under the assumption that coevolutive information is imprinted on the interacting amino acids of two proteins in contrast to other (evolutive and stochastic) sources spread over their sequences, we dissect those contributions in terms of compensatory mutations at physically-coupled and uncoupled amino acids of A and B. We find that physically-coupled amino-acids at short range distances store the largest per-contact mutual information content, with a significant fraction of that content resulting from coevolutive sources alone. The information stored in coupled amino acids is shown further to discriminate multi-sequence alignments (MSAs) with the largest expectation fraction of PPI matches – a conclusion that holds against various definitions of intermolecular contacts and binding modes. When compared to the informational content resulting from evolution at long-range interactions, the mutual information in physically-coupled amino-acids is the strongest signal to distinguish PPIs derived from cospeciation and likely, the unique indication in case of molecular coevolution in independent genomes as the evolutive information must vanish for uncorrelated proteins. Keywords: Coevolution, Mutual information, Protein-protein interaction, Protein network, Evolution

Abstract Membrane proteins are primary targets for most therapeutic indications in cancer and neurological diseases, binding over 50% of all known small molecule drugs. Understanding how such ligands impact membrane proteins requires knowledge on the molecular structure of ligand binding, a reasoning that has driven relentless efforts in drug discovery and translational research. Binding of small ligands appears however highly complex involving interaction to multiple transmembrane protein sites featuring single or multiple occupancy states. Within this scenario, looking for new developments in the field, we investigate the concentration-dependent binding of ligands to multiple saturable sites in membrane proteins. The study relying on docking and free-energy perturbation provides us with an extensive description of the probability density of protein-ligand states that allows for computation of thermodynamic properties of interest. It also provides one- and three-dimensional spatial descriptions for the ligand density across the protein-membrane system which can be of interest for structural purposes. Illustration and discussion of the results are shown for binding of the general anesthetic sevoflurane against Kv1.2, a mammalian ion channel for which experimental data are available.

Changes in chromatin structure regulate gene expression and genome maintenance. Molecules that bind to the nucleosome, the complex of DNA and histone proteins, are key modulators of chromatin structure. Previous work indicated that cholesterol, a ubiquitous cellular lipid, may bind to chromatin in vivo, suggesting a potential function for lipids in modulating chromatin architecture. However, the molecular mechanisms of cholesterol's action on chromatin structure have remained unclear. Here, we explored the biophysical impact of cholesterol on nucleosome and chromatin fibers reconstituted in vitro and characterized in silico the cholesterol binding to the nucleosome. Our findings support that cholesterol assists 10 and 30 nm chromatin formation and induces folding of long chromatin fibers as a result of direct interaction of the cholesterol to six nucleosomal binding sites.

The direct-site hypothesis assumes general anesthetics bind ion channels to impact protein equilibrium and function, inducing anesthesia. Despite advancements in the field, a first principle all-atom demonstration of this structure-function premise is still missing. We focus on the clinically used sevoflurane interaction to anesthetic-sensitive Kv1.2 mammalian channel to resolve if sevoflurane binds protein's well-characterized open and closed structures in a conformation-dependent manner to shift channel equilibrium. We employ an innovative approach relying on extensive docking calculations and free-energy perturbation of all potential binding sites revealed by the latter, and find sevoflurane binds open and closed structures at multiple sites under complex saturation and concentration effects. Results point to a non-trivial interplay of site and conformation-dependent modes of action involving distinct binding sites that increase channel open-probability at diluted ligand concentrations. Given the challenge in exploring more complex processes potentially impacting channel-anesthetic interaction, the result is revealing as it demonstrates the process of multiple anesthetic binding events alone may account for open-probability shifts recorded in measurements.

Inhalational general anesthesia results from the poorly understood interactions of haloethers with multiple protein targets, which prominently includes ion channels in the nervous system. Previously, we reported that the commonly used inhaled anesthetic sevoflurane potentiates the activity of voltage-gated K+ (Kv) channels, specifically, several mammalian Kv1 channels and the Drosophila K-Shaw2 channel. Also, previous work suggested that the S4-S5 linker of K-Shaw2 plays a role in the inhibition of this Kv channel by n-alcohols and inhaled anesthetics. Here, we hypothesized that the S4-S5 linker is also a determinant of the potentiation of Kv1.2 and K-Shaw2 by sevoflurane. Following functional expression of these Kv channels in Xenopus oocytes, we found that converse mutations in Kv1.2 (G329T) and K-Shaw2 (T330G) dramatically enhance and inhibit the potentiation of the corresponding conductances by sevoflurane, respectively. Additionally, Kv1.2-G329T impairs voltage-dependent gating, which suggests that Kv1.2 modulation by sevoflurane is tied to gating in a state-dependent manner. Toward creating a minimal Kv1.2 structural model displaying the putative sevoflurane binding sites, we also found that the positive modulations of Kv1.2 and Kv1.2-G329T by sevoflurane and other general anesthetics are T1-independent. In contrast, the positive sevoflurane modulation of K-Shaw2 is T1-dependent. In silico docking and molecular dynamics-based free-energy calculations suggest that sevoflurane occupies distinct sites near the S4-S5 linker, the pore domain and around the external selectivity filter. We conclude that the positive allosteric modulation of the Kv channels by sevoflurane involves separable processes and multiple sites within regions intimately involved in channel gating.

Voltage-gated potassium (Kv) channels are ubiquitous transmembrane proteins involved in electric signaling of excitable tissues. A fundamental property of these channels is the ability to open or close in response to changes in the membrane potential. To date, their structure-based activation mechanism remains unclear, and there is a large controversy on how these gates function at the molecular level, in particular, how movements of the voltage sensor domain are coupled to channel gating. So far, all mechanisms proposed for this coupling are based on the crystal structure of the open voltage-gated Kv1.2 channel and structural models of the closed form based on electrophysiology experiments. Here, we use coarse-grain (CG) molecular dynamics simulations that allow conformational changes from the open to the closed form of the channel (embedded in its membrane environment) to be followed. Despite the low specificity of the CG force field, the obtained closed structure satisfies several experimental constraints. The overall results suggest a gating mechanism in which a lateral displacement the S4−S5 linker leads to a closing of the gate. Only a small up−down movement of the S4 helices is noticed. Additionally, the study suggests a peculiar upward motion of the intracellular tetramerization domain of the channel, hence providing a molecular view on how this domain may further regulate conduction in Kv channels. [ABSTRACT FROM AUTHOR]