Jing Li

Voltage Sensor Domains (VSDs) are integral and common regions within voltage-gated ion channels that play a crucial role in cellular signaling and excitability. It has four transmembrane segments (S1-S4) per domain. The transmembrane segment S4 contains positively charged amino acid residues at every third position, which serve as the channel's gating charges. Over the last decade, it has been shown that these gating charges are the target of many pathological mutations. This study investigates three gating charge mutations in Domain I of the NaV1.2 channel (R220G, R223I, R223Q) identified in patients with distinct clinical phenotypes. The three mutations showed differential gating-pore current amplitudes with distinct voltage dependence. To elucidate the molecular basis for the differential Igp observed among these mutants, we performed microsecond-scale molecular dynamics (MD) simulations and a comprehensive analysis of the state-dependent interaction network within VSDI across WT and mutant conformational states. The results showed that cation-π interactions between the S4 gating charges and the hydrophobic residue in S2 are the main forces that prevent VSD from leaking gating-pore current. Hydrophobic interactions represent the second line of defense against VSD leaking, preventing gating-pore formation by maintaining structural integrity across different conformations. Salt-bridge interactions and hydrogen bonds are important stabilizing forces; however, within the voltage-sensing domain, their contribution appears to be less significant than that of cation–π and hydrophobic interactions. Predicting and identifying gating pore currents as a potential shared pathophysiology across diverse clinical presentations may guide the design of targeted therapeutic strategies.

The gating and function of γ-aminobutyric acid type A receptors (GABA_ARs) depend on their hetero-pentameric subunit composition, yet the rules guiding subunit selection remain unclear. We show that a conserved N-linked glycan in α subunits, uniquely positioned within the extracellular pore, acts as a structural constraint that limits the functional viability of α-containing assemblies. A total of 32 µs of molecular dynamics simulations of native and hypothetical non-canonical assemblies show that adding a third pore-facing glycan or placing two glycans on adjacent subunits disrupts key interactions at the β⁺/α^- interface containing the GABA-binding site. These perturbations propagate allosterically, alter extracellular-transmembrane domain coupling, and promote deep gate closure. Systems with three pore-facing glycans consistently adopt deep-closed conformations, whereas native assemblies with two glycans preserve interfacial integrity and pore hydration. These results identify the pore-facing glycan as a structural constraint on functionally viable GABA_AR architecture and gating.

Inflammatory mediators including polyunsaturated fatty acids (PUFAs) potentiate ASIC3 by altering channel gating, yet the molecular basis for their interactions remains poorly defined. Using all-atom MD simulations and electrophysiology, we show that DHA accumulates around ASIC3 through interactions with a membrane-facing electropositive region along the outer leaflet of TM1. In the open state, a more buried binding site for the lipid is exposed that allows the carboxylate head group to bind a critical arginine along with nearby polar residues. Mutation of this site eliminates the DHA-mediated slowing of channel desensitization and also modestly reduces the effect PUFAs have on the proton sensitivity of channel activation. The same mutations do not prevent regulation by other lipid classes like N-acyl amino acids or lysophosphatidyl choline. Our results provide the first detailed description of a PUFA binding site on ASICs, offering new insights into lipid modulation and potential strategies for developing novel treatments for inflammatory pain.

The assembly and gating of γ-aminobutyric acid type A receptors (GABA _A Rs) are tightly regulated by their hetero-pentameric subunit composition, yet the molecular determinants governing the pentameric form remain elusive. Here, we demonstrate that a conserved N -linked glycan on α subunits, uniquely positioned within the central pore of the extracellular domain, acts as a structural gatekeeper limiting α subunit incorporation. Using a total of 28 µs of molecular dynamics simulations across native and putative GABA _A Rs assemblies, we show that introducing a third pore-facing glycan or positioning two glycans on adjacent subunits disrupts key interfacial salt bridges and hydrogen bonds, particularly at the β+/α– interface that hosts the GABA binding site. These disruptions propagate allosterically, reduce internal loop flexibility, and alter extracellular-to-transmembrane domain coupling, ultimately leading to deep closure of the activation and desensitization gates in the transmembrane domain. Systems containing three glycans consistently shift toward dehydrated, non-conductive conformations. In contrast, native form with two pore-facing glycans preserved native interfacial networks and pore radius. Our findings provide a mechanistic insight for the long-observed α-limiting assembly pattern and identify glycan-mediated steric hindrance as a critical factor of receptor gating. These insights bridge evolutionary conservation, N -glycosylation, and dynamic structure-function relationships, highlighting pore-facing glycosylation as a key determinant of GABA _A Rs architecture and function.

Natural Language Processing (NLP) has revolutionized the way computers are used to study and interact with human languages and is increasingly influential in the study of protein and ligand binding, which is critical for drug discovery and development. This review examines how NLP techniques have been adapted to decode the "language" of proteins and small molecule ligands to predict protein-ligand interactions (PLIs). We discuss how methods such as long short-term memory (LSTM) networks, transformers, and attention mechanisms can leverage different protein and ligand data types to identify potential interaction patterns. Significant challenges are highlighted including the scarcity of high-quality negative data, difficulties in interpreting model decisions, and sampling biases in existing data sets. We argue that focusing on improving data quality, enhancing model robustness, and fostering both collaboration and competition could catalyze future advances in machine-learning-based predictions of PLIs.

Voltage-gated sodium (Nav) channels are pivotal for cellular signaling, and mutations in Nav channels can lead to excitability disorders in cardiac, muscular, and neural tissues. A major cluster of pathological mutations localizes in the voltage-sensing domains (VSDs), resulting in either gain-of-function, loss-of-function effects, or both. However, the mechanism behind this functional diversity of mutations at equivalent positions remains elusive. Through hotspot analysis, we identified three gating charges (R1, R2, and R3) as major mutational hotspots in VSDs. The same amino acid substitutions at equivalent gating-charge positions in VSDI and VSDII of the cardiac sodium channel Nav1.5 show differential gating property impacts in electrophysiology measurements. We conducted molecular dynamics (MD) simulations on wild-type channels and six mutants to elucidate the structural basis of their differential impacts. Our 120-µs MD simulations with applied external electric fields captured VSD state transitions and revealed the differential structural dynamics between equivalent R-to-Q mutants. Notably, we observed transient leaky conformations in some mutants during structural transitions, offering a detailed structural explanation for gating-pore currents. Our salt-bridge network analysis uncovered VSD-specific and state-dependent interactions among gating charges, countercharges, and lipids. This detailed analysis revealed how mutations disrupt critical electrostatic interactions, thereby altering VSD permeability and modulating gating properties. By demonstrating the crucial importance of considering the specific structural context of each mutation, our study advances our understanding of structure–function relationships in Nav channels. Our work establishes a robust framework for future investigations into the molecular basis of ion channel–related disorders.

Polyunsaturated fatty acids (PUFAs) and their analogs play a significant role in modulating the activity of diverse ion channels, and recent studies show that these lipids potentiate acid-sensing ion channels (ASICs), leading to increased activity. The potentiation of the channel stems from multiple gating changes, but the exact mechanism of these effects remains uncertain. We posit a mechanistic explanation for one of these changes in channel function, the increase in the maximal current, by applying a combination of electrophysiology and all-atom molecular dynamics simulations on open-state hASIC3. Microsecond-scale simulations were performed on open-state hASIC3 in the absence and presence of a PUFA, docosahexaenoic acid (DHA), and a PUFA analogue, N-arachidonyl glycine (AG). Intriguingly, our simulations in the absence of PUFA or PUFA analogs reveal that a tail from the membrane phospholipid POPC inserts itself into the pore of the channel through lateral fenestrations on the sides of the transmembrane segments, obstructing ion permeation through the channel. The binding of either DHA or AG prevented POPC from accessing the pore in our simulations, which relied on the block of ionic conduction by phospholipids. Finally, we use single-channel recording to show that DHA increases the amplitude of the single-channel currents in ASIC3, which is consistent with our hypothesis that PUFAs relieve the pore block of the channel induced by POPCs. Together, these findings offer a potential mechanistic explanation of how PUFAs modulate the ASIC maximal current, revealing a novel mechanism of action for PUFA-induced modulation of ion channels.

Natural Language Processing (NLP) has revolutionized the way computers are used to study and interact with human languages and is increasingly influential in the study of protein and ligand binding, which is critical for drug discovery and development. This review examines how NLP techniques have been adapted to decode the “language” of proteins and small molecule ligands to predict protein-ligand interactions (PLIs). We discuss how methods such as long short-term memory (LSTM) networks, transformers, and attention mechanisms can leverage different protein and ligand data types to identify potential interaction patterns. Significant challenges are highlighted, including the scarcity of high-quality negative data, difficulties in interpreting model decisions, and sampling biases of existing datasets. We argue that focusing on improving data quality, enhancing model robustness, and fostering both collaboration and competition could catalyze future advances in machine-learning-based predictions of PLIs.

Voltage-gated sodium (Na _v ) channels are pivotal for cellular signaling and mutations in Na _v channels can lead to excitability disorders in cardiac, muscular, and neural tissues. A major cluster of pathological mutations localizes in the voltage-sensing domains (VSDs), resulting in either gain-of-function (GoF), loss-of-function (LoF) effects, or both. However, the mechanism behind this functional divergence of mutations at equivalent positions remains elusive. Through hotspot analysis, we identified three gating charges (R1, R2, and R3) as major mutational hotspots in VSDs. The same amino-acid substitutions at equivalent gating-charge positions in VSD _I and VSD _II of the cardiac sodium channel Na _v 1.5 show differential gating-property impacts in electrophysiology measurements. We conducted 120 µs molecular dynamics (MD) simulations on wild-type and six mutants to elucidate the structural basis of their differential impacts. Our μs-scale MD simulations with applied external electric fields captured VSD state transitions and revealed the differential structural dynamics between equivalent R-to-Q mutants. Notably, we observed transient leaky conformations in some mutants during structural transitions, offering a detailed structural explanation for gating-pore currents. Our salt-bridge network analysis uncovered VSD-specific and state-dependent interactions among gating charges, countercharges, and lipids. This detailed analysis elucidated how mutations disrupt critical electrostatic interactions, thereby altering VSD permeability and modulating gating properties. By demonstrating the crucial importance of considering the specific structural context of each mutation, our study represents a significant leap forward in understanding structure- function relationships in Na _v channels. Our work establishes a robust framework for future investigations into the molecular basis of ion channel-related disorders.

Polyunsaturated fatty acids (PUFAs) and their analogs play a significant role in modulating the activity of diverse ion channels, and recent studies show that these lipids potentiate acid-sensing ion channels (ASICs), leading to increased activity. The potentiation of the channel stems from multiple gating changes, but the exact mechanism of these effects remains uncertain. We posit a mechanistic explanation for one of these changes in channel function, the increase in the maximal current, by applying a combination of electrophysiology and all-atom molecular dynamics simulations on the open-state hASIC3. Microsecond-scale simulations were performed on open-state hASIC3 in the absence and presence of a PUFA, docosahexaenoic acid (DHA), and a PUFA analog, N-arachidonyl glycine (AG). Intriguingly, our simulations in the absence of PUFA or PUFA analogs reveal that a tail from the membrane phospholipid POPC inserts itself into the pore of the channel through lateral fenestrations on the sides of the transmembrane segments, obstructing ion permeation through the channel. The binding of either DHA or AG prevented POPC from accessing the pore in our simulations, relieving the block of ionic conduction by phospholipids. Finally, we use the single-channel recording to show that DHA increases the amplitude of the single-channel currents in ASIC3, which is consistent with our hypothesis that PUFAs relieve the pore block of the channel induced by POPCs. Together, these findings offer a potential mechanistic explanation of how PUFAs modulate ASIC maximal current, revealing a novel mechanism of action for PUFA-induced modulation of ion channels.