A gating lever and molecular logic gate that couple voltage and calcium sensor activation to opening in BK potassium channels

BK Channelopathies and KCNMA1-Linked Disease Models

Novel KCNMA1 variants, encoding the BK K+ channel, are associated with a debilitating dyskinesia and epilepsy syndrome. Neurodevelopmental delay, cognitive disability, and brain and structural malformations are also diagnosed at lower incidence. More than half of affected individuals present with a rare negative episodic motor disorder, paroxysmal nonkinesigenic dyskinesia (PNKD3). The mechanistic relationship of PNKD3 to epilepsy and the broader spectrum of KCNMA1-associated symptomology is unknown. This review summarizes patient-associated KCNMA1 variants within the BK channel structure, functional classifications, genotype-phenotype associations, disease models, and treatment. Patient and transgenic animal data suggest delineation of gain-of-function (GOF) and loss-of-function KCNMA1 neurogenetic disease, validating two heterozygous alleles encoding GOF BK channels (D434G and N999S) as causing seizure and PNKD3. This discovery led to a variant-defined therapeutic approach for PNKD3, providing initial insight into the neurological basis. A comprehensive clinical definition of monogenic KCNMA1-linked disease and the neuronal mechanisms currently remain priorities for continued investigation.

High-resolution structures illuminate key principles underlying voltage and LRRC26 regulation of Slo1 channels

Multi-modal regulation of Slo1 channels by membrane voltage, intracellular calcium, and auxiliary subunits enables its pleiotropic physiological functions. Our understanding of how voltage impacts Slo1 conformational dynamics and the mechanisms by which auxiliary subunits, particularly of the LRRC (Leucine Rich Repeat containing) family of proteins, modulate its voltage gating remain unresolved. Here, we used single particle cryo-electron microscopy to determine structures of human Slo1 mutants which functionally stabilize the closed pore (F315A) or the activated voltage-sensor (R207A). Our structures, obtained under calcium-free conditions, reveal that a key step in voltage-sensing by Slo1 involves a rotameric flip of the voltage-sensing charges (R210 and R213) moving them by ∼6 Å across a hydrophobic gasket. Next we obtained reconstructions of a complex of human Slo1 with the human LRRC26 (γ1) subunit in absence of calcium. Together with extensive biochemical tests, we show that the extracellular domains of γ1 form a ring of interlocked dominos that stabilizes the quaternary assembly of the complex and biases Slo1:γ1 assembly towards high stoichiometric complexes. The transmembrane helix of γ1 is kinked and tightly packed against the Slo1 voltage-sensor. We hypothesize that γ1 subunits exert relatively small effects on early steps in voltage-gating but structurally stabilize non-S4 helices of Slo1 voltage-sensor which energetically facilitate conformational rearrangements that occur late in voltage stimulated transitions.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Fifty years of gating currents and channel gating

We celebrate this year the 50th anniversary of the first electrophysiological recordings of the gating currents from voltage-dependent ion channels done in 1973. This retrospective tries to illustrate the context knowledge on channel gating and the impact gating-current recording had then, and how it continued to clarify concepts, elaborate new ideas, and steer the scientific debate in these 50 years. The notion of gating particles and gating currents was first put forward by Hodgkin and Huxley in 1952 as a necessary assumption for interpreting the voltage dependence of the Na and K conductances of the action potential. 20 years later, gating currents were actually recorded, and over the following decades have represented the most direct means of tracing the movement of the gating charges and gaining insights into the mechanisms of channel gating. Most work in the early years was focused on the gating currents from the Na and K channels as found in the squid giant axon. With channel cloning and expression on heterologous systems, other channels as well as voltage-dependent enzymes were investigated. Other approaches were also introduced (cysteine mutagenesis and labeling, site-directed fluorometry, cryo-EM crystallography, and molecular dynamics [MD] modeling) to provide an integrated and coherent view of voltage-dependent gating in biological macromolecules. The layout of this retrospective reflects the past 50 years of investigations on gating currents, first addressing studies done on Na and K channels and then on other voltage-gated channels and non-channel structures. The review closes with a brief overview of how the gating-charge/voltage-sensor movements are translated into pore opening and the pathologies associated with mutations targeting the structures involved with the gating currents.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ΔV 1/2 , with a RMSE ∼ 32 mV and correlation coefficient of R ∼ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V 1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ΔV 1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Author Summary

Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.

E-cigarette aerosols of propylene glycol impair BK channel activity and parameters of mucociliary function

Propylene glycol (PG) is a common delivery vehicle for nicotine and flavorings in e-cigarette (e-cig) liquids and is largely considered safe for ingestion. However, little is known about its effects as an e-cig aerosol on the airway. Here, we investigated whether pure PG e-cig aerosols in realistic daily amounts impact parameters of mucociliary function and airway inflammation in a large animal model (sheep) in vivo and primary human bronchial epithelial cells (HBECs) in vitro. Five-day exposure of sheep to e-cig aerosols of 100% PG increased mucus concentrations (% mucus solids) of tracheal secretions. PG e-cig aerosols further increased the activity of matrix metalloproteinase-9 (MMP-9) in tracheal secretions. In vitro exposure of HBECs to e-cig aerosols of 100% PG decreased ciliary beating and increased mucus concentrations. PG e-cig aerosols further reduced the activity of large conductance, Ca2+-activated, and voltage-dependent K+ (BK) channels. We show here for the first time that PG can be metabolized to methylglyoxal (MGO) in airway epithelia. PG e-cig aerosols increased levels of MGO and MGO alone reduced BK activity. Patch-clamp experiments suggest that MGO can disrupt the interaction between the major pore-forming BK subunit human Slo1 (hSlo1) and the gamma regulatory subunit LRRC26. PG exposures also caused a significant increase in mRNA expression levels of MMP9 and interleukin 1 beta (IL1B). Taken together, these data show that PG e-cig aerosols cause mucus hyperconcentration in sheep in vivo and HBECs in vitro, likely by disrupting the function of BK channels important for airway hydration.