GABAA receptors (GABAARs) are the primary inhibitory neurotransmitter receptors throughout the central nervous system. Genetic mutations causing their dysfunction are related to a broad spectrum of human disorders such as epilepsy, neurodevelopment and intellectual disability, autism spectrum disorder, schizophrenia, and depression. GABAARs are also important drug targets for anxiolytics, anticonvulsants, antidepressants, and anesthetics. Despite significant progress in understanding their three-dimensional structure, a critical gap remains in determining the molecular basis for channel gating. We recently identified mutations in the M2-M3 linkers that suggest linker flexibility has asymmetric subunit-specific correlations with channel opening. Here we use non-canonical amino acids (ncAAs) to investigate the role of main-chain H-hydrogen bonds (H-bonds) that may stabilize the M2-M3 linkers. We show that a single main-chain H-bond within the beta 2 subunit M2-M3 linker inhibits pore opening and is required to keep the unliganded channel closed. Furthermore, breaking this H-bond accounts for approximately one third of the energy used to open the channel during activation by GABA. In contrast, the analogous H-bond in the alpha 1 subunit has no effect on gating. Our molecular simulations support the idea that channel opening involves the state-dependent breakage/disruption of a specific main-chain H-bond within the beta 2 subunit M2-M3 linker.

gamma-Aminobutyric acid type A (GABAA) receptors are ligand-gated ion channels in the central nervous system with largely inhibitory function. Despite being a target for drugs including general anesthetics and benzodiazepines, experimental structures have yet to capture an open state of classical synaptic alpha 1 beta 2 gamma 2 GABAA receptors. Here, we use a goal-oriented adaptive sampling strategy in molecular dynamics simulations followed by Markov state modeling to capture an energetically stable putative open state of the receptor. The model conducts chloride ions with comparable conductance as in electrophysiology measurements. Relative to experimental structures, our open model is relatively expanded at both the cytoplasmic (-2 ') and central (9 ') gates, coordinated with distinctive rearrangements at the transmembrane alpha beta subunit interface. Consistent with previous experiments, targeted substitutions disrupting interactions at this interface slowed the open-to-desensitized transition rate. This work demonstrates the capacity of advanced simulation techniques to investigate a computationally and experimentally plausible functionally critical of a complex membrane protein yet to be resolved by experimental methods.

Pentameric ligand-gated ion channels (pLGICs) are responsible for the rapid conversion of chemical to electrical signals. In addition to the canonical extracellular and transmembrane domains, some prokaryotic pLGICs contain an N-terminal domain (NTD) of unclear structure and function. In one such case, the calcium-sensitive channel DeCLIC, the NTD appears to accelerate gating; however, its evident flexibility has posed a challenge to model building, and its role in calcium sensitivity is unclear. Here we report cryo-EM structures of DeCLIC in circularized lipid nanodiscs, achieving the highest resolution reported so far, and enabling definition of calcium-binding sites in both the N-terminal and canonical extracellular domains. In addition to the symmetric state, calcium depletion promoted an asymmetric conformation of the NTD, offering a structural rationale for small-angle scattering results. Behavior of these structures in molecular dynamics simulations demonstrated calcium stabilization of the NTD. These features of DeCLIC offer a model system for ion-channel modulation by a flexible accessory domain, potentially conserved in structurally homologous systems across evolution.

Resolving protein-ligand interactions in atomic detail is key to understanding how small molecules regulate macromolecular function. Although recent breakthroughs in cryogenic electron microscopy (cryo-EM) have enabled high-quality reconstruction of numerous complex biomolecules, the resolution of bound ligands is often relatively poor. Furthermore, methods for building and refining molecular models into cryo-EM maps have largely focused on proteins and may not be optimized for the diverse properties of small-molecule ligands. Here, we present an approach that integrates artificial intelligence (AI) with cryo-EM density-guided simulations to fit ligands into experimental maps. Using three inputs: 1) a protein amino acid sequence, 2) a ligand specification, and 3) an experimental cryo-EM map, we validated our approach on a set of biomedically relevant protein-ligand complexes including kinases, GPCRs, and solute transporters, none of which were present in the AI training data. In cases for which AI was not sufficient to predict experimental poses outright, integration of flexible fitting into molecular dynamics simulations improved ligand model-to-map cross-correlation relative to the deposited structure from 40-71% to 82-95%. This work offers a straightforward pipeline for integrating AI and density-guided simulations to model building in cryo-EM maps of ligand-protein complexes.

The family of rho-type GABA_A receptors includes potential therapeutic targets in several neurological conditions, and features distinctive pharmacology compared to other subtypes. Here we report four cryo-EM structures with previously unresolved ligands, electrophysiology recordings, and molecular dynamics simulations to characterize binding and conformational impact of the drugs THIP (a non-opioid analgesic), CGP36742 (a phosphinic acid) and GABOB (an anticonvulsant) on a human rho 1 GABA_A receptor. A distinctive binding pose of THIP in rho 1 versus alpha 4 beta 3 delta GABA_A receptors offers a rationale for its inverse effects on these subtypes. CGP36742 binding is similar to the canonical rho-type inhibitor TPMPA, supporting a shared mechanism of action among phosphinic acids. Binding of GABOB is similar to GABA, but produces a mixture of partially-locked and desensitized states, likely underlying weaker agonist activity. Together, these results elucidate interactions of a rho-type GABA_A receptor with therapeutic drugs, offering mechanistic insights and a basis for further pharmaceutical development.

A diverse set of modulators, including stimulants and anesthetics, regulates ion channel function in our nervous system. However, structures of ligand-bound complexes can be difficult to capture by experimental methods, particularly when binding is dynamic. Here, we used computational methods and electrophysiology to identify a possible bound state of a modulatory stimulant derivative in a cryptic vestibular pocket of a mammalian serotonin-3 receptor. We first applied a molecular dynamics simulation–based goal-oriented adaptive sampling method to identify possible open-pocket conformations, followed by Boltzmann docking that combines traditional docking with Markov state modeling. Clustering and analysis of stability and accessibility of docked poses supported a preferred binding site; we further validated this site by mutagenesis and electrophysiology, suggesting a mechanism of potentiation by stabilizing intersubunit contacts. Given the pharmaceutical relevance of serotonin-3 receptors in emesis, psychiatric, and gastrointestinal diseases, characterizing relatively unexplored modulatory sites such as these could open valuable avenues to understanding conformational cycling and designing state-dependent drugs.

Integral membrane proteins carry out essential functions in the cell, and their activities are often modulated by specific protein-lipid interactions in the membrane. Here, we elucidate the intricate role of cardiolipin (CDL), a regulatory lipid, as a stabilizer of membrane proteins and their complexes. Using the in silico-designed model protein TMHC4_R (ROCKET) as a scaffold, we employ a combination of molecular dynamics simulations and native mass spectrometry to explore the protein features that facilitate preferential lipid interactions and mediate stabilization. We find that the spatial arrangement of positively charged residues as well as local conformational flexibility are factors that distinguish stabilizing from non-stabilizing CDL interactions. However, we also find that even in this controlled, artificial system, a clear-cut distinction between binding and stabilization is difficult to attain, revealing that overlapping lipid contacts can partially compensate for the effects of binding site mutations. Extending our insights to naturally occurring proteins, we identify a stabilizing CDL site within the E. coli rhomboid intramembrane protease GlpG and uncover its regulatory influence on enzyme substrate preference. In this work, we establish a framework for engineering functional lipid interactions, paving the way for the design of proteins with membrane-specific properties or functions.

Modeling atomic coordinates into a target cryo-electron microscopy map is a crucial step in structure determination. Despite recent advances, proteins with multiple functional states remain a challenge - particularly when suitable molecular templates are unavailable for certain states, and the map resolution is not high enough to build de novo models. This is a common scenario, for example, among pharmacologically relevant membrane-bound receptors and transporters. Here, we introduce a refinement approach in which (i) several initial models are generated by stochastic subsampling of the multiple sequence alignment (MSA) space in AlphaFold2, (ii) the resulting models are subjected to structure-based k-means clustering, iii) density-guided molecular dynamics simulations are performed from the cluster representatives, and (iv) a final model is selected on the basis of both map fit and model quality. This results in improved fitting accuracy compared to single starting point scenarios for three membrane proteins (the calcitonin receptor-like receptor, L-type amino acid transporter and alanine-serine-cysteine transporter) which undergo substantial conformational transitions between functional states. Our results indicate that ensemble construction using generative AI combined with simulation-based refinement facilitates building of alternative states in several families of membrane proteins.

The function of a protein is enabled by its conformational landscape. For non-rigid proteins, a complete characterization of this landscape requires understanding the protein's structure in all functional states, the stability of these states under target conditions, and the transition pathways between them. Several strategies have recently been developed to drive the machine learning algorithm AlphaFold2 (AF) to sample multiple conformations, but it is more challenging to a priori predict what states are stabilized in particular conditions and how the transition occurs. Here, we combine AF sampling with small-angle scattering curves to obtain a weighted conformational ensemble of functional states under target environmental conditions. We apply this to the pentameric ion channel GLIC using small-angle neutron scattering (SANS) curves, and identify apparent closed and open states. By comparing experimental SANS data under resting and activating conditions, we can quantify the subpopulation of closed channels that open upon activation, matching both experiments and extensive simulation sampling using Markov state models. The predicted closed and open states closely resemble crystal structures determined under resting and activating conditions respectively, and project to predicted basins in free energy landscapes calculated from the Markov state models. Further, without using any structural information, the AF sampling also correctly captures intermediate conformations and projects onto the transition pathway resolved in the extensive sampling. This combination of machine learning algorithms and low-dimensional experimental data appears to provide an efficient way to predict not only stable conformations but also accurately sample the transition pathways several orders of magnitude faster than simulation-based sampling.