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.

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.

Glycine receptors are pentameric ligand-gated ion channels that conduct chloride ions across postsynaptic membranes to facilitate fast inhibitory neurotransmission. In addition to gating by the glycine agonist, interactions with lipids and other compounds in the surrounding membrane environment modulate their function, but molecular details of these interactions remain unclear, in particular, for cholesterol. Here, we report coarse-grained simulations in a model neuronal membrane for three zebrafish glycine receptor structures representing apparent resting, open, and desensitized states. We then converted the systems to all-atom models to examine detailed lipid interactions. Cholesterol bound to the receptor at an outer-leaflet intersubunit site, with a preference for the open and desensitized versus resting states, indicating that it can bias receptor function. Finally, we used short atomistic simulations and iterative amino acid perturbations to identify residues that may mediate allosteric gating transitions. Frequent cholesterol contacts in atomistic simulations clustered with residues identified by perturbation analysis and overlapped with mutations influencing channel function and pathology. Cholesterol binding at this site was also observed in a recently reported pig heteromeric glycine receptor. These results indicate state-dependent lipid interactions relevant to allosteric transitions of glycine receptors, including specific amino acid contacts applicable to biophysical modeling and pharmaceutical design.

Loss-of-function mutations of the CFTR gene cause the life-shortening genetic disease cystic fibrosis (CF), whereas overactivity of CFTR may lead to secretory diarrhea and polycystic kidney disease. While effective drugs targeting the CFTR protein have been developed for the treatment of CF, little progress has been made for diseases caused by hyper-activated CFTR. Here, we solve the cryo-EM structure of CFTR in complex with CFTRinh-172 (Inh-172), a CFTR gating inhibitor with promising potency and efficacy. We find that Inh-172 binds inside the pore of CFTR, interacting with amino acid residues from transmembrane segments (TMs) 1, 6, 8, 9, and 12 through mostly hydrophobic interactions and a salt bridge. Substitution of these residues lowers the apparent affinity of Inh-172. The inhibitor-bound structure reveals re-orientations of the extracellular segment of TMs 1, 8, and 12, supporting an allosteric modulation mechanism involving post-binding conformational changes. This allosteric inhibitory mechanism readily explains our observations that pig CFTR, which preserves all the amino acid residues involved in Inh-172 binding, exhibits a much-reduced sensitivity to Inh-172 and that the apparent affinity of Inh-172 is altered by the CF drug ivacaftor (i.e., VX-770) which enhances CFTR’s activity through binding to a site also comprising TM8.

Ligand-gated ion channels transduce electrochemical signals in neurons and other excitable cells. Aside fromcanonical ligands, phospholipids are thought to bind specifically to the transmembrane domain of several ionchannels. However, structural details of such lipid contacts remain elusive, partly due to limited resolution ofthese regions in experimental structures. Here, we discovered multiple lipid interactions in the channel GLICby integrating cryo-electron microscopy and large-scale molecular simulations. We identified 25 bound lipidsin the GLIC closed state, a conformation where none, to our knowledge, were previously known. Three lipidswere associated with each subunit in the inner leaflet, including a buried interaction disrupted in mutantsimulations. In the outer leaflet, two intrasubunit sites were evident in both closed and open states, whilea putative intersubunit site was preferred in open-state simulations. This work offers molecular details ofGLIC-lipid contacts particularly in the ill-characterized closed state, testable hypotheses for state-dependentbinding, and a multidisciplinary strategy for modeling protein-lipid interactions.

ρ-type γ-aminobutyric acid-A (GABAA) receptors are widely distributed in the retina and brain, and are potential drug targets for the treatment of visual, sleep and cognitive disorders. Endogenous neuroactive steroids including β-estradiol and pregnenolone sulfate negatively modulate the function of ρ1 GABAA receptors, but their inhibitory mechanisms are not clear. By combining five cryo-EM structures with electrophysiology and molecular dynamics simulations, we characterize binding sites and negative modulation mechanisms of β-estradiol and pregnenolone sulfate at the human ρ1 GABAA receptor. β-estradiol binds in a pocket at the interface between extracellular and transmembrane domains, apparently specific to the ρ subfamily, and disturbs allosteric conformational transitions linking GABA binding to pore opening. In contrast, pregnenolone sulfate binds inside the pore to block ion permeation, with a preference for activated structures. These results illuminate contrasting mechanisms of ρ1 inhibition by two different neuroactive steroids, with potential implications for subtype-specific gating and pharmacological design.

GROMACS is a widely-used molecular dynamics software package with a focus on performance, portability, and maintainability across a broad range of platforms. Thanks to its early algorithmic redesign and flexible heterogeneous parallelization, GROMACS has successfully harnessed GPU accelerators for more than a decade.With the diversification of accelerator platforms in HPC and no obvious choice for a well-suited multi-vendor programming model, the GROMACS project found itself at a crossroads. The performance and portability requirements, as well as a strong preference for a standards-based programming model, motivated our choice to use SYCL for production on both new HPC GPU platforms: AMD and Intel.Since the GROMACS 2022 release, the SYCL backend has been the primary means to target AMD GPUs in preparation for exascale HPC architectures like LUMI and Frontier.SYCL is a cross-platform, royalty-free, C++17-based standard for programming hardware accelerators, from embedded to HPC.It allows using the same code to target GPUs from all three major vendors with minimal specialization, which offers major portability benefits.While SYCL implementations build on native compilers and runtimes, whether such an approach is performant is not immediately evident.Biomolecular simulations have challenging performance characteristics: latency sensitivity, the need for strong scaling, and typical iteration times as short as hundreds of microseconds. Hence, obtaining good performance across the range of problem sizes and scaling regimes is particularly challenging.Here, we share the results of our work on readying GROMACS for AMD GPU platforms using SYCL,and demonstrate performance on Cray EX235a machines with MI250X accelerators. Our findings illustrate that portability is possible without major performance compromises.We provide a detailed analysis of node-level kernel and runtime performance with the aim of sharing best practices with the HPC community on using SYCL as a performance-portable GPU framework.

We developed fluorescent protein sensors for nicotine with improved sensitivity. For iNicSnFR12 at pH 7.4, the proportionality constant for ∆F/F0 vs [nicotine] (δ-slope, 2.7 μM−1) is 6.1-fold higher than the previously reported iNicSnFR3a. The activated state of iNicSnFR12 has a fluorescence quantum yield of at least 0.6. We measured similar dose-response relations for the nicotine-induced absorbance increase and fluorescence increase, suggesting that the absorbance increase leads to the fluorescence increase via the previously described nicotine-induced conformational change, the ‘candle snuffer’ mechanism. Molecular dynamics (MD) simulations identified a binding pose for nicotine, previously indeterminate from experimental data. MD simulations also showed that Helix 4 of the periplasmic binding protein (PBP) domain appears tilted in iNicSnFR12 relative to iNicSnFR3a, likely altering allosteric network(s) that link the ligand binding site to the fluorophore. In thermal melt experiments, nicotine stabilized the PBP of the tested iNicSnFR variants. iNicSnFR12 resolved nicotine in diluted mouse and human serum at 100 nM, the peak [nicotine] that occurs during smoking or vaping, and possibly at the decreasing levels during intervals between sessions. NicSnFR12 was also partially activated by unidentified endogenous ligand(s) in biofluids. Improved iNicSnFR12 variants could become the molecular sensors in continuous nicotine monitors for animal and human biofluids.

The COVID-19 pandemic demonstrates that a top priority for society, now and in the future, is to be able to respond quickly to diseases with effective treatments. Among the new tools that pharmaceutical industries and researchers have in their hands nowadays, there are the extensive computer simulations capable of evaluating in-silico the interaction between possible drugs and the target proteins. The central goal of the LIGATE project is to create and validate a leading application solution for drug discovery in High-Performance Computing (HPC) systems up to the exascale level. The overall project purpose is the automation of the drug design process, which is currently performed with substantial human effort throughout the different phases of the process: preparation of input parameters, management of data sets with billions of molecules, interaction with HPC queue management systems to handle jobs, and optimization of scoring function parameters and thresholds.

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.