Enhancement of the cyclization of membrane lipids GDGTs (glycerol dialkyl glycerol tetraethers) is a critical strategy for archaea to adapt to various environmental stresses. However, the physiological function of membrane lipid cyclization remains unclear. Here, we reported that the GDGT ring synthases mutant, deficient in GDGT cyclization, inhibited archaellum formation and reduced cell motility in thermoacidophilic crenarchaeon Sulfolobus acidocaldarius. This inhibition was caused by decreased transcription of the archaellum operon, likely due to cleavage of the C-terminal domains in transmembrane proteins ArnRs, the transcription factors that regulate archaellum operon expression. The transcriptomic and proteomic analysis showed deficiency of GDGT cyclization broadly impacted the expression of membrane associate proteins, including respiratory chain proteins, and decreased cellular ATP concentration. Moreover, phylogenetic analysis demonstrated that the correlation between GDGT cyclization and archaellum formation is widespread among (hyper)thermophilic archaea, and this was further verified in the euryarchaeon Thermococcus kodakarensis. Our findings suggested that archaea modify their membrane lipids to profoundly alter cellular appendages and cell physiology to adapt to environmental fluctuations.

Cytokines are crucial regulators of the immune system that orchestrate interactions between cells and, when dysregulated, contribute to the progression of chronic inflammation, cancer, and autoimmunity. Numerous biologic-based clinical agents, mostly monoclonal antibodies, have validated cytokines as important clinical targets and are now part of the standard of care for a number of diseases. These agents, while impactful, still suffer from limitations including a lack of oral bioavailability, high cost of production, and immunogenicity. Small-molecule cytokine inhibitors are attractive alternatives that can address these limitations. Although targeting cytokine-cytokine receptor complexes with small molecules has been a challenging research endeavor, multiple small-molecule inhibitors have now been identified, with a number of them undergoing clinical evaluation. In this review, we highlight the recent advancements in the discovery and development of small-molecule inhibitors targeting soluble cytokines. The strategies for identifying these novel ligands as well as the structural and mechanistic insights into their activity represent important milestones in tackling these challenging and clinically important protein-protein interactions.

Endocrine-related disorders are highly prevalent globally, affecting millions of people. Such diseases are multifactorial in origin and are influenced by the complex interplay of genetics, lifestyle, and environmental factors. Recurring disruptions in the endocrine homeostasis can lead to a cascade of endocrine-related cancers. It is well known that nuclear receptors (NRs), particularly estrogen receptor and androgen receptor malfunctioning promote the oncogenesis of breast cancer and prostate cancer, respectively. However, existing therapeutics against these diseases, including aromatase inhibitors, (anti-) hormonal therapy, etc., often yield limited success, prompting to explore alternative methods of disease management. Additionally, drug resistance is prominent in cancer patients undergoing multidrug therapy. Currently, novel drug design strategies targeting NRs are being implemented for the discovery of a new generation of small molecule modulators, including selective NR modulators (SNuRMs) and degraders (SNuRDs). Moreover, proteolysis-targeting chimeras (PROTACs) as NR degraders, are also being developed primarily to overcome drug resistance, enhance protein selectivity, and mitigate off-target toxicity. This review highlights recent advancements in SNuRMs and SNuRDs for managing NRs-associated endocrine/metabolic disorders. Furthermore, we discuss the therapeutic potential of PROTAC degraders as a stand-alone strategy for receptor-mediated disease intervention, offering new avenues for precision medicine.

The structural knowledge of membrane proteins (MPs) is crucial for the structure-based drug design. The stabilization of MPs during extraction processes is essential for structural and functional maintenance. In this regard, detergents are used to achieve extractions of MPs in their functional form. Based on previous work showing the importance of adjacent dicarboxylate groups for the detergency properties, the synthesis of a new generation of detergents bearing more dicarboxylate groups is reported. The molecular structure of the new generation is characterized by the presence of four chemical entities: a DOTA or NOTA scaffold, three or four pairs of carboxylates, a fatty acid, and PEG chains. The preliminary biochemical evaluation reveals promising features of this novel generation of surfactants for the aqueous extraction of MPs.

Integral membrane proteins (IMPs) are pivotal for cellular functions but challenging to investigate. Here, IC-FPOMP (in-cell fast photochemical oxidation of MPs) is introduced, a method enabling in situ footprinting of IMPs within live cells. IC-FPOMP generates reactive oxygen radicals from various precursors (TiO2 nanoparticles or H2O2) near the membrane. Leveraging a laser and a 96-well plate platform, high-throughput and rapid footprinting of IMPs are achieved. IC-FPOMP of two human IMPs (human glucose transporter-hGLUT1 and human gamma-glutamyl carboxylase-hGGCX) are successful, providing footprinting of both the transmembrane and extramembrane regions. Comparative analysis of hGLUT1 in liposomes versus cells shows that the membrane may impact the transporter's conformation differently. In-cell drug screening targeting hGLUT1 reveals drug-binding behavior in vivo. In summary, IC-FPOMP offers insights into IMP structure-function relationships in cells and facilitates drug discovery.

In drug discovery, various approaches exist to find compounds that alter the different states in living organisms. There are two fundamental discovery strategies regarding the mechanism of action: target-based and phenotypic methods. Both have strengths and weaknesses in assay development, target selection, target validation and structure optimization. While phenotypic screening can identify chemical starting points with the desired phenotype, it is typically difficult to carry out efficient, structure-based optimization without confirming the mechanism of action of such hits. It is therefore critical to uncover the targets behind the phenotype. Target deconvolution is typically carried out by a set of highly selective compounds, where each ligand is associated with a particular target. Hits of such a high-selectivity set can provide valuable information on the phenotype's underlying targets and may also enable novel target-based therapeutic strategies. Consequently, there is a continuously high demand for novel highly-selective tool compounds for target deconvolution. In this work, the ChEMBL database, comprising over 20 million bioactivity data, was mined to identify the most selective novel ligands for a diverse set of targets. A novel method for the automated selection of such high-selectivity ligands is presented. Using these high-selectivity compounds in phenotypic screening campaigns can provide a valuable preliminary direction during target deconvolution. 87 representative compounds were purchased and screened against 60 cancer cell lines. Several compounds were found to possess selective inhibition of cell growth of a few distinct cell lines. The phenotypic assay results, along with the nanomolar activities of individual proteins obtained from the ChEMBL database suggest some novel mechanisms of action for anti-cancer drug discovery.

Nuclear receptors form a family of proteins capable of accommodating a wide variety of small molecules in their ligand binding domain, ranging from therapeutic compounds to endocrine-disrupting chemicals. The rapid identification of these compounds, especially within the latter category, is of paramount importance. Using data extracted from the CompTox Dashboard, an Environmental Protection Agency initiative, we assessed the effectiveness of a combination of molecular docking and pharmacophore models in identifying ligands binding to six nuclear receptors: androgen receptor, estrogen receptor alpha, estrogen receptor beta, glucocorticoid receptor, peroxisome proliferator-activated receptor gamma, and thyroid hormone receptor alpha. For each nuclear receptor, we selected a specifically designed and optimized in silico protocol that, in conjunction with experimental assays, can prioritize compounds for further evaluation to detect any potential toxicological concerns.

Membrane proteins are biologically and pharmaceutically significant, and determining their 3D structures requires a membrane-mimetic system to maintain protein stability. Detergent micelles are widely used as membrane mimetics; however, their dynamic structures often lead to the denaturation and aggregation of encapsulated membrane proteins. To address the limitations of classical detergents in stabilizing membrane proteins, we previously reported a class of glucose-neopentyl glycols (GNGs) and their pendant-bearing versions (P-GNGs), several of which proved more effective than DDM in stabilizing membrane proteins. In this study, we synthesized additional GNG derivatives by varying the lengths of the pendant (P-GNGs), and by introducing hemifluorinated pendants to the GNG scaffold (fluorinated pendant-bearing GNGs or FP-GNGs). The synthetic flexibility of the GNG chemical architecture allowed us to create a diverse range of derivatives, essential for the effective optimization of detergent properties. When tested with two model membrane proteins (a transporter and a G-protein coupled receptor (GPCR)), most of the new (F)P-GNGs demonstrated superior stabilization of these membrane proteins compared to DDM, the original GNG (OGNG)), and a previously developed P-GNG (i.e., GNG-3,14). Notably, several P-GNGs synthesized in this study were as effective as or even better than lauryl maltose neopentyl glycol (LMNG) in stabilizing a human GPCR, beta2 adrenergic receptor (β2AR). Enhanced protein stability was particularly observed for the P-GNGs with a butyl (C4) or pentyl (C5) pendant, indicating that these pendant sizes are optimal for membrane protein stability. The volumes of these pendants appear to minimize the empty spaces in the micelle interiors, thereby enhancing detergent-detergent interactions in micelles complexed with the membrane proteins. Additionally, we identified one FP-GNG that was more efficient at extracting the transporter and more effective at stabilizing the GPCR than DDM. Thus, the current study demonstrates that both chain length and number of fluorine atoms in the pendants of the P-GNGs were crucial determinants for membrane protein stability. We not only developed a few (F)P-GNGs that are significantly more effective than maltoside detergents (LMNG/DDM) for protein extraction and stability but we also provided an effective strategy for detergent design through the utilization of partially fluorinated pendants of varying length.

Membrane proteins, a principal class of drug targets, play indispensable roles in various biological processes and are closely associated with essential life functions. Their study, however, is complicated by their low solubility in aqueous environments and distinctive structural characteristics, necessitating a suitable native-like environment for molecular analysis. Nanodisc technology has revolutionized this field, providing biochemists with a powerful tool to stabilize membrane proteins and significantly enhance their research possibilities. This review outlines the substantial advancements in nanodisc methodologies and applications from 2018 to 2024. We cover the development of various nanodisc models, as well as structural and functional studies of membrane proteins that utilize nanodiscs, highlighting their medical applications.

Membrane proteins play central roles in cell physiology and are the targets of over 50% of FDA-approved drugs. In the present study, we prepared single alkyl-chained triglucosides decorated with multiple pendants, designated multiple pendant-bearing glucosides (MPGs), to enhance membrane protein stability. The new detergents feature two and four pendants of varying size at the hydrophilic-lipophilic interfaces, designated MPG-Ds and MPG-Ts, respectively. When tested with model membrane proteins, including the human adrenergic receptor (β2AR), the tetra-pendant-bearing MPGs (MPG-Ts) demonstrated superior performance compared to the dipendant analogs (MPG-Ds) and the gold standard DDM. All-atom molecular dynamics (MD) simulations results reveal that the four-pendant configuration of this detergent is remarkably effective in excluding water from the hydrophobic micelle interiors compared to the dipendant MPGs and DDM, an unprecedented feature of this new detergent. Our findings provide a novel strategy for designing water-resistant detergents, advancing the field of membrane protein research.

Membrane proteins are crucial to many cellular functions but are notoriously difficult for structural studies due to their instability outside their natural environment and their amphipathic nature with dual hydrophobic and hydrophilic regions. Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a transformative approach, providing near-atomic-resolution structures without the need for crystallization. This review discusses advancements in cryo-EM, emphasizing membrane sample preparation and data processing techniques. It explores innovations in capturing membrane protein structures within native environments, analyzing their dynamics, binding partner interactions, lipid associations, and responses to electrochemical gradients. These developments continue to enhance our understanding of these vital biomolecules, advancing the contributions of structural biology for basic and translational biomedicine.

Current strategies for active targeting in the brain are entirely based on the effective interaction of the ligand with the orthosteric sites of specific receptors on the blood-brain barrier (BBB), which is highly susceptible to various pathophysiological factors and limits the efficacy of drug delivery. Here, we propose an allosteric targeted drug delivery strategy that targets classical BBB transmembrane receptors by designing peptide ligands that specifically bind to their transmembrane domains. This strategy prevents competitive interference from endogenous ligands and antibodies by using the insulin receptor and integrin αv as model targets, respectively, and can effectively overcome pseudotargets or target loss caused by shedding or mutating the extracellular domain of target receptors. Moreover, these ligands can be spontaneously embedded in the phospholipid layer of lipid carriers using a plug-and-play approach without chemical modification, with excellent tunability and immunocompatibility. Overall, this allosteric targeted drug delivery strategy can be applied to multiple receptor targets and drug carriers and offers promising therapeutic benefits in brain diseases.

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data.

Detergents are essential molecular tools for membrane protein (MP) research, yet traditional detergents with static properties often fail to address the diverse and evolving needs of MP studies. To this end, this study introduces "living detergents", an innovative class of detergents equipped with functional tags that enable bioorthogonal modifications with externally introduced structural elements. This approach allows for not only the parallel generation of new detergents, but also in situ tuning of MP samples within freshly formed detergents. The efficacy of this strategy was demonstrated through the rapid identification of optimal detergents for high-quality electron microscopy studies of A2AAR. Overall, this flexible and robust platform enables efficient tailoring of detergents, advancing the exploration of detergent structure-function relationships in MP research and opening pathways for more specialized solutions for diverse experimental demands.

Acral and triggerable pain is a hallmark of diseases involving small nerve fiber impairment, yet the underlying cellular mechanisms remain elusive. A key role is attributed to pain-related proteins located within the neuronal plasma membrane of nociceptive neurons. To explore this, we employed human induced pluripotent stem cell-derived sensory-like neurons and enriched their surface proteins by biotinylation. Samples from three independent cell differentiations were analyzed via liquid chromatography tandem mass spectrometry. Detected proteins were categorized by cellular location and function, followed by generating an interaction network for deregulated surface proteins. Gene expression of selected proteins was quantified using real-time PCR. A comparative analysis was performed between a patient with Fabry disease (FD) and a healthy control, which we used as model system. We successfully extracted surfaceome proteins from human sensory-like neurons, revealing deregulation of 48 surface proteins in FD-derived neurons. Among the candidates with potential involvement in pain pathophysiology were CACNA2D3, GPM6A, EGFR, and ABCA7. Despite the lack of gene expression differences in these candidates, the interaction network indicated compromised neuronal network integrity. Our approach successfully enabled the extraction and comprehensive analysis of the surfaceome from human sensory-like neurons, establishing a novel methodological framework for investigating human sensory-like neuron biology and cellular disease mechanisms.

Nucleic acids have the potential to form not only duplexes, but also various non-canonical secondary structures in living cells. Non-canonical structures play regulatory functions mainly in the central dogma. Therefore, nucleic acid targeting molecules are potential novel therapeutic drugs that can target 'undruggable' proteins in various diseases. One of the concerns of small molecules targeting nucleic acids is selectivity, because nucleic acids have only four different building blocks. Three- and four-stranded non-canonical structures, triplexes and quadruplexes, respectively, are promising targets of small molecules because their three-dimensional structures are significantly different from the canonical duplexes, which are the most abundant in cells. Here, we describe some basic properties of the triplexes and quadruplexes and small molecules targeting the triplexes and tetraplexes.

Drug-resistant Staphylococcus aureus (DRSA) poses a significant global health threat, like bacteremia, endocarditis, skin, soft tissue, bone, and joint infections. Nowadays, the resistance against conventional drugs has been a prompt and focused medical concern. The present study aimed to explore the inhibitory potential of plant-based bioactive compounds (PBBCs) against effective target proteins using a computational approach. We retrieved and verified 22 target proteins associated with DRSA and conducted a screening process that involved testing 87 PBBCs. Molecular docking was performed between screened PBBCs and reference drugs with selected target proteins via AutoDock. Subsequently, we filtered the target proteins and top PBBCs based on their binding affinity scores. Furthermore, molecular dynamic simulation was carried out through GROMACS for a duration of 100 ns, and the binding free energy was calculated using the gmx_MMPBSA. The result showed consistent hydrogen bonding interactions among the amino acid residues Ser 149, Arg 151, Thr 165, Thr 216, Glu 239, Ser 240, Ile 14, as well as Asn 18, Gln 19, Lys 45, Thr 46, Tyr 109, with their respective target proteins of the penicillin-binding protein and dihydrofolate reductase complex. Additionally, we assessed the pharmacokinetic properties of screened PBBCs via SwissADME and AdmetSAR. The findings suggest that β-amyrin, oleanolic acid, kaempferol, quercetin, and friedelin have the potential to inhibit the selected target proteins. In future research, both in vitro and in vivo, experiments will be needed to establish these PBBCs as potent antimicrobial drugs for DRSA.

Magic angle spinning solid-state NMR (MAS ssNMR) is a versatile tool for studying the structure and dynamics of membrane proteins, as well as their interactions with ligands and drugs. Its power lies in the ability to provide atomic-level information on samples under physiological-like conditions. Moreover, it can illuminate dynamics across a wide range of timescales with great relevance to membrane protein function and dysfunction. In this protocol paper, we highlight key aspects of sample preparation, data acquisition, and interpretation, based on our own experience and the broader literature. We discuss key protocol steps along with important considerations for sample preparation and parameters for ssNMR measurements, with reference to the special requirements of membrane-based samples. Such samples display physiologically relevant dynamics across different motional regimes that can be probed by NMR but also can interfere with certain NMR measurements. We guide the reader through the whole process from sample preparation to complex NMR characterization techniques. Throughout the report, we refer back to examples from our own prior work on the interactions between cytochrome c and cardiolipin-containing membranes, with a discussion of the lipid dependence and interactions with a peroxidase-activity inhibitor. We conclude with a short discussion of alternative and new methods that are further boosting the power and versatility of ssNMR as a tool to study membrane-bound proteins and their ligands or drug interactions.

Cell surface proteins (surfaceome) represent key signalling and interaction molecules for therapeutic targeting, biomarker profiling and cellular phenotyping in physiological and pathological states. Here, we employed coronary artery perfusion with membrane-impermeant biotin to label and capture the surface-accessible proteome in the neo-native (intact) heart. Using quantitative proteomics, we identified 701 heart cell surfaceome accessible by the coronary artery, including receptors, cell surface enzymes, adhesion and junctional molecules. This surfaceome comprises to 216 cardiac cell-specific surface proteins, including 29 proteins reported in cardiomyocytes (CXADR, CACNA1C), 12 in cardiac fibroblasts (ITGA8, COL3A1) and 63 in multiple cardiac cell types (ICAM1, SLC3A2, CDH2). Further, this surfaceome comprises to 53 proteins enriched in heart tissue compared to other tissues in humans and implicated in cardiac cell signalling networks involving cardiomyopathy (CDH2, DTNA, PTKP2, SNTA1, CAM, K2D/B), cardiac muscle contraction and development (ENG, SNTA1, SGCG, MYPN), calcium ion binding (SGCA, MASP1, THBS4, FBLN2, GSN) and cell metabolism (SDHA, NUDFS1, GYS1, ACO2, IDH2). This method offers a powerful tool for dissecting the molecular landscape of the coronary artery accessible heart cell surfaceome, its role in maintaining cardiac and vascular function, and potential molecular leads for studying cardiac cell interactions and systemic delivery to the neo-native heart.

Annealing is an ideal approach to synchronizing soluble proteins into their minimum-energy states via tandem heating and cooling treatments. Like soluble proteins, many membrane proteins also suffer intrinsic structural flexibility, the major obstacle to high-resolution structural determination. How to apply annealing onto membrane proteins remains unexplored. Here, we utilized the translocase of the outer mitochondrial membrane (TOM) as the model and investigated the ideal annealing conditions for membrane proteins. After structural determination via cryo-electron microscopy, we indicated that fast cooling the heated TOM complex to 0 °C can significantly improve the local resolution compared with the unannealed one. Structural analyses showed that annealing renders the TOM complex into a new conformation with its Tom7 α1 helix from a reclining position on the membrane surface to a lying orientation, accompanied by the loop between β6 and β7 in Tom40, flipping outward from the Tom40 β-barrel, ideal for preprotein translocation. In all, our results demonstrate the role of annealing in synchronizing membrane proteins and unveil unidentified conformations of the TOM complex.

Mycoviruses are viruses that infect fungi, including plant pathogens. The infection of these mycoviruses is sometimes associated with impaired phenotypes of their fungal hosts, a phenomenon known as hypovirulence. Thus, using mycoviruses as biological control agents has emerged as a promising tool to combat forest diseases. The invasive ascomycete fungus Fusarium circinatum, which causes pine pitch canker (PPC) disease in Pinus tree species and other coniferous trees, is infected by the mycovirus Fusarium circinatum mitovirus 1 (FcMV1), FcMV2-1, and FcMV2-2. However, its impact on pathogen fitness remains unclear. The most accurate method used to identify the effect of a mycovirus on its host is the generation of isogenic lines with and without the mycovirus. The present study aimed to cure F. circinatum isolates infected by FcMV1 using different approaches. For this purpose, three replicates of each isolate were exposed to thermal treatment (38 °C) and antibiotic treatment (ribavirin, cycloheximide, kanamycin, and rifampicin mixed with cAMP)(cyclic adenosine monophosphate) for five successive passages. The viral titer of FcMV1 was then assessed using qPCR (quantitative polymerase chain reaction) after the first week and after the fifth week of the treatment. The results revealed differences in treatment efficacy among F. circinatum isolates, with some showing very low virus titers at the end of the experiment. Both thermal and antibiotic treatment effectively reduced the viral load in all isolates. In addition, the antibiotic cycloheximide and rifampicin +cAMP reduced the viral titer more than ribavirin and kanamycin. The isolate Fc179 was found to be more prone to antibiotic treatment than the other two isolates (001 and Va221). This study demonstrated the possibility of using some isolates of F. circinatum for fine-tuning cures for mitovirus, in order to create virus-free strains for biological control in the future.

Magic angle spinning nuclear magnetic resonance (MAS NMR) is well suited for the determination of protein structure. The key structural information is obtained in the form of spectral cross peaks between spatially close nuclear spins, but assigning these cross peaks unambiguously to unique spin pairs is often a tedious task because of spectral overlap. Here, we use a seven-helical membrane protein Anabaena Sensory Rhodopsin (ASR) as a model system to demonstrate that transverse Paramagnetic Relaxation Enhancements (PRE) extracted from 2D MAS NMR spectra could be used to obtain a protein structural model. Starting with near complete assignments (93%) of ASR residues, TALOS + predicted backbone dihedral angles and secondary structure restraints in the form of backbone hydrogen bonds are combined with PRE-based restraints and used to generate a coarse model. This model is subsequently utilized as a template reference to facilitate automated assignments of highly ambiguous internuclear correlations. The template is used in an iterative cross peak assignment process and is progressively improved through the inclusion of disambiguated restraints, thereby converging to a low root-mean-square-deviation structural model. In addition to improving structure calculation conversion, the inclusion of PREs also improves packing between helices within an alpha-helical bundle.

The structure and function of membrane proteins depend on their interactions with lipids that constitute membranes. Actinoporins are α-pore-forming proteins that bind preferentially to sphingomyelin-containing membranes, where they oligomerize and form transmembrane pores. Through a comprehensive cryo-electron microscopic analysis of a pore formed by an actinoporin Fav from the coral Orbicella faveolata, we show that the octameric pore interacts with 112 lipids in the upper leaflet of the membrane, reveal the roles of lipids, and demonstrate that the actinoporin surface is suited for binding multiple receptor sphingomyelin molecules. When cholesterol is present in the membrane, it forms a cluster of four molecules associated with each protomer. Atomistic simulations support the structural data and reveal additional effects of the pore on the lipid membrane. These data reveal a complex network of protein-lipid and lipid-lipid interactions and an underrated role of lipids in the structure and function of transmembrane protein complexes.

Interactions between species give rise to chemical pathways of communication that regulate the interactions of transboundary species. The communication between nematodes and other species primarily occurs through the regulation of chemicals, with key species including plants, insects, bacteria, and nematode-trapping fungi that are closely associated with nematodes. G protein-coupled receptors (GPCRs) play a crucial role in interspecies communication. Certain flp genes, which function as GPCRs, exert varying degrees of influence on how nematodes interact with other species. These receptors facilitate the transmission of corresponding signals, thereby completing the interactions between species. This paper introduces the interactions between nematodes and other species and discusses the role of GPCRs in these organisms, contributing to a deeper understanding of the impact and significance of GPCRs in cross-border regulation between nematodes and other species. Furthermore, it is essential to leverage GPCRs in efforts to control pests.

Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computational efforts alike. The folding kinetics is moreover associated with topological modulations of the biological milieu. Studying such structural changes in membrane-embedded proteins and understanding the associated topological signatures in membrane leaflets, therefore, remain relatively unexplored. Herein, we first aim to estimate the free energy barrier and the minimum free energy path (MFEP) connecting the membrane-embedded fully and partially inserted states of the bacteriorhodopsin fragment. To achieve this, we have considered independent sets of simulations from membrane-mimicking and membrane-embedded environments, respectively. An autoencoder model is used to elicit state-distinguishable collective variables for the system utilizing membrane-mimicking simulations. Our in-house Expectation Maximized Molecular Dynamics algorithm is initially used to deduce the barrier height between the two membrane-embedded states. Next, we develop the Geometry Optimized Local Direction search as a post-processing algorithm to identify the MFEP and the corresponding peptide conformations from the autoencoder-projected trajectories. Finally, we apply a graph attention neural network (GAT) model to learn the membrane surface topology as a function of the associated peptide structure, supervised by the membrane-embedded simulations. The resultant GAT model is then utilized to predict the membrane leaflet topology for the peptide structures along MFEP, obtained from membrane-mimicking simulations. The combined framework is expected to be useful in capturing key phenomena accompanying folding transitions in membranes. We discuss opportunities and avenues for further development.

Gardnerella vaginalis is the most frequently identified bacterium in approximately 95% of bacterial vaginosis (BV) cases. This species often exhibits resistance to multiple antibiotics, posing challenges for treatment. Therefore, there is an urgent need to develop and explore alternative therapeutic strategies for managing bacterial vaginosis. The objective of this study was to identify virulence factors and potential drug targets against Gardnerella vaginalis by utilizing in silico methods, including subtractive and comparative genomics. These methods enabled the systematic comparison of genetic sequences to pinpoint specific features unique to G. vaginalis and crucial for its pathogenicity, which could then inform the development of targeted therapeutic strategies. The analysis of the pathogen's proteomic data aimed to identify proteins that fulfilled specific criteria. These included being non-homologous to human proteins, essential for bacterial survival, amenable to drug targeting, involved in virulence, and contributing to antibiotic resistance. Following these analyses and an extensive literature review, the phospho-2-dehydro-3-deoxyheptonate aldolase enzyme emerged as a promising drug target. To deepen our understanding of the biological function of the identified protein, comprehensive protein structural modeling, validation studies, and network topology analyses were conducted. The subsequent structural analysis, encompassing modeling, validation, and network topology assessment, is aimed at further characterizing the protein. Using a library of around 9,000 FDA-approved compounds from the DrugBank database, a virtual screening was conducted to identify potential compounds that could effectively target the proposed drug target. This approach facilitated the evaluation of existing drugs for their ability to inhibit the target, potentially offering an efficient pathway for developing new treatments against the pathogen. Leveraging the established efficacy, safety, pharmacokinetics, and pharmacodynamics of these compounds, the study suggests repurposing them for Gardnerella vaginalis infections. Among the screened compounds, five specific agents-DB03332, DB07452, DB01262, DB02076, and DB00727-were identified as cost-effective therapeutic options for treating infections related to Gardnerella vaginalis. These compounds were selected based on their efficacy in targeting the pathogen while maintaining economic feasibility. While the results indicate potential efficacy in treating infections caused by the pathogen, further experimental studies are essential to validate these findings.

Native ambient mass spectrometry (NAMS) enables analysis of protein structure directly from biological substrates by use of liquid junction sampling techniques together with sampling solvents which mimic the proteins' natural environment. Here, we demonstrate detection of membrane and membrane-associated proteins directly from E. coli by combining liquid extraction surface analysis (LESA) with a straightforward washing protocol, which attenuates soluble proteins and enables detection of membrane proteins.

Membrane proteins (MPs) are important yet challenging targets for drug discovery. MPs can be reconstituted in protein-lipid Nanodiscs (NDs), which resemble the native membrane environment. Drug-membrane interactions can affect the apparent binding stoichiometry and affinity, as well as the kinetics of ligands for a particular target, which is important for the extrapolation to pharmacokinetic studies. To investigate the role of the membrane, we have applied fragment-based drug discovery (FBDD) methods to cytochrome P450 3A4 (CYP3A4), reconstituted in NDs composed of different phosphocholine lipids: 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), dipalmitoylphosphatidylcholine (DPPC), or 1,2-diphytanoyl-sn-glycero-3-phosphocholine (DPhPC). Surface plasmon resonance screening of fragments and marketed drugs revealed extensive binding to the empty ND, correlating with analyte hydrophobicity, and the binding was critically dependent on ND lipid composition. POPC NDs showed much higher binding of fragments than DMPC and DPhPC NDs, resulting in a lower hit rate for CYP3A4 in POPC NDs, which demonstrated that the choice of the ND lipid is crucial to the outcome of a screen. The number of binders that were rejected based on atypical binding kinetics was lower for monomeric CYP3A4 in NDs than for non-native oligomeric CYP3A4 without the ND. Several fragments were exclusively identified as hits for CYP3A4 in the presence of the ND membrane. It is concluded that the nature of the ND is a critical factor for fragment screening of membrane proteins.

Heart failure (HF) is a prominent fatal cardiovascular disorder afflicting 3.4% of the adult population despite the advancement of treatment options. Therefore, a better understanding of the pathogenesis of HF is essential for exploring novel therapeutic strategies. Hypertrophy and fibrosis are significant characteristics of pathological cardiac remodeling, contributing to HF. The mechanisms involved in the development of cardiac remodeling and consequent HF are multifactorial, and in this review, the key underlying mechanisms are discussed. These have been divided into the following categories thusly: (i) mitochondrial dysfunction, including defective dynamics, energy production, and oxidative stress; (ii) cardiac lipotoxicity; (iii) maladaptive endoplasmic reticulum (ER) stress; (iv) impaired autophagy; (v) cardiac inflammatory responses; (vi) programmed cell death, including apoptosis, pyroptosis, and ferroptosis; (vii) endothelial dysfunction; and (viii) defective cardiac contractility. Preclinical data suggest that there is merit in targeting the identified pathways; however, their clinical implications and outcomes regarding treating HF need further investigation in the future. Herein, we introduce the molecular mechanisms pivotal in the onset and progression of HF, as well as compounds targeting the related mechanisms and their therapeutic potential in preventing or rescuing HF. This, therefore, offers an avenue for the design and discovery of novel therapies for the treatment of HF.

Esketamine ameliorates propofol-induced brain damage and cognitive impairment in mice. However, the precise role and underlying mechanism of esketamine in perioperative neurocognitive disorders (PND) remain unclear. Therefore, this study aimed to investigate the key genes associated with the role of esketamine in PND through animal modeling and transcriptome sequencing.

The present study established a mice model of PND and administered esketamine intervention to the model, and mice were divided into control, surgical group, and surgical group with esketamine. Behavioral assessments were conducted using the Morris water maze and Y maze paradigms, while transcriptome sequencing was performed on hippocampal samples obtained from 3 groups. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on sequencing data to identify candidate genes related to esketamine treating PND. Thereafter, protein-protein interaction (PPI) network analysis was implemented to select key genes. The genes obtained from each step were subjected to enrichment analysis, and a regulatory network for key genes was constructed.

The Morris water maze and Y maze findings demonstrated the successful construction of our PND model, and indicated that esketamine exhibits a certain therapeutic efficacy for PND. Ank1, Cbln4, L1cam, Gap43, and Shh were designated as key genes for subsequent analysis. The 5 key genes were significantly enriched in cholesterol biosynthesis, nonsense mediated decay (NMD), formation of a pool of free 40s subunits, major pathway of rRNA processing in the nucleolus and cytosol, among others. Notably, the miRNAs, mmu-mir-155-5p and mmu-mir-1a-3p, functionally co-regulated the expression of Ank1, Gap43, and L1cam.

We uncovered the therapeutic efficacy of esketamine in treating PND and identified 5 key genes (Ank1, Cbln4, L1cam, Gap43, and Shh) that contribute to its therapeutic effects, providing a valuable reference for further mechanistic studies on esketamine's treatment of PND.

Nanodiscs have become a popular membrane mimetic system offering a well-defined bilayer environment to stabilize membrane proteins for in vitro analyses using a range of analytical methods; however, lipid compositions common to their deployment are simplistic and often fail to model native membrane complexity. Furthermore, there has been a general lack of rigorous analytical and biophysical characterization of nanodiscs comprising more than one lipid. To address these challenges, we coupled a nanodisc formation and purification workflow with targeted LC-MS/MS analysis to quantify lipids in nanodiscs made with different compositions. We screened lipids with a variety of headgroups and acyl chains and found that lipids did not always incorporate into nanodiscs at expected levels. Disparities in lipid incorporation were found to increase upon the addition of lipids known to induce curvature or rigidity to the membrane. Additionally, we found that adding just one additional type of lipid to nanodiscs changes the particle diameter and dispersity compared to nanodiscs containing a single lipid. We also formed and characterized nanodiscs using a complex starting composition inspired by the endoplasmic reticulum membrane and observed native-like cholesterol dynamics that modulated the lipid fluidity in the model bilayer system. Taken together, this work serves as a foundation for understanding nonstoichiometric lipid incorporation into nanodiscs and provides a basis for more thorough nanodisc characterization and quality control, which is critical to ensure multilipid nanodiscs synthesized accurately model the biological system of interest, enabling robust characterization of how the lipid landscape affects membrane protein structure and activity.

Understanding the dynamics of membrane protein-ligand interactions within a native lipid bilayer is a major goal for drug discovery. Typically, cell-based assays are used, however, they are often blind to the effects of protein modifications. In this study, using the archetypal G protein-coupled receptor rhodopsin, we found that the receptor and its effectors can be released directly from retina rod disc membranes using infrared irradiation in a mass spectrometer. Subsequent isolation and dissociation by infrared multiphoton dissociation enabled the sequencing of individual retina proteoforms. Specifically, we categorized distinct proteoforms of rhodopsin, localized labile palmitoylations, discovered a Gβγ proteoform that abolishes membrane association and defined lipid modifications on G proteins that influence their assembly. Given reports of undesirable side-effects involving vision, we characterized the off-target drug binding of two phosphodiesterase 5 inhibitors, vardenafil and sildenafil, to the retina rod phosphodiesterase 6 (PDE6). The results demonstrate differential off-target reactivity with PDE6 and an interaction preference for lipidated proteoforms of G proteins. In summary, this study highlights the opportunities for probing proteoform-ligand interactions within natural membrane environments.

Pulsed dipolar electron paramagnetic resonance spectroscopy (PDS), combined with site-directed spin-labelling, represents a powerful tool for the investigation of biomacromolecules, emerging as a keystone approach in structural biology. Increasingly, PDS is applied to study highly complex integral membrane protein systems, such as mechanosensitive ion channels, transporters, G-protein coupled receptors, ion pumps, and outer membrane proteins elucidating their dynamics and revealing conformational ensembles. Indeed, PDS offers a platform to study intermediate or lowly-populated states that are otherwise invisible to other modern methods, such as X-ray crystallography, cryo-EM, and hydrogen-deuterium exchange-mass spectrometry. Importantly, advances in spin labelling strategies welcome a new era of membrane protein investigation under near-native or in-cell conditions. Here, we review recent integral membrane protein PDS applications, and highlight well-suited, emerging spin labelling strategies that show promise for future studies.

Vesicles play critical roles in cellular materials storage and signal transportation, even in the formation of organelles and cells. Natural vesicles are composed of a lipid layer that forms a membrane for the enclosure of substances inside. Here we report a coacervate vesicle formed by the liquid-liquid phase separation of cholesterol-modified DNA and histones. Unlike a phospholipid-based membrane-bounded vesicle, a coacervate vesicle lacks a membrane structure on the surface and is organized with a high-density liquid layer and a water-filled cavity. Through a straightforward coacervation process, we demonstrate that various biological agents, including virus particles, mRNA, cytokines and peptides, can be innocuously and directly enriched in the liquid phase. In contrast to the droplet-like coacervates that are prone to aggregation challenges, coacervate vesicles display superior kinetic stability, positioning them as a versatile delivery vehicle for biopharmaceuticals. We validate that incorporating oncolytic viruses into these coacervate vesicles endows them with potent oncolytic efficacy and elicits robust anti-tumour immune responses in mouse models.

The traditional process of developing new drugs is time-consuming and often unsuccessful, making drug repurposing an appealing alternative due to its speed and safety. Graph neural networks (GCNs) have emerged as a leading approach for predicting drug-disease associations by integrating drug and disease-related networks with advanced deep learning algorithms. However, GCNs generally infer association probabilities only for existing drugs and diseases, requiring network re-establishment and retraining for novel entities. Additionally, these methods often struggle with sparse networks and fail to elucidate the biological mechanisms underlying newly predicted drugs.

To address the limitations of traditional methods, we developed HEDDI-Net, a heterogeneous embedding architecture designed to accurately detect drug-disease associations while preserving the interpretability of biological mechanisms. HEDDI-Net integrates graph and shallow learning techniques to extract representative diseases and proteins, respectively. These representative diseases and proteins are used to embed the input features, which are then utilized in a multilayer perceptron for predicting drug-disease associations.

In experiments, HEDDI-Net achieves areas under the receiver operating characteristic curve of over 0.98, outperforming state-of-the-art methods. Rigorous recovery analyses reveal a median recovery rate of 73% for the top 100 diseases, demonstrating its efficacy in identifying novel target diseases for existing drugs, known as drug repurposing. A case study on Alzheimer's disease highlighted the model's practical applicability and interpretability, identifying potential drug candidates like Baclofen, Fluoxetine, Pentoxifylline and Phenytoin. Notably, over 40% of the predicted candidates in the clusters of commonly prescribed clinical drugs Donepezil and Galantamine had been tested in clinical trials, validating the model's predictive accuracy and practical relevance.

HEDDI-NET represents a significant advancement by allowing direct application to new diseases and drugs without the need for retraining, a limitation of most GCN-based methods. Furthermore, HEDDI-Net provides detailed affinity patterns with representative proteins for predicted candidate drugs, facilitating an understanding of their physiological effects. This capability also supports the design and testing of alternative drugs that are similar to existing medications, enhancing the reliability and interpretability of potential repurposed drugs. The case study on Alzheimer's disease further underscores HEDDI-Net's ability to predict promising drugs and its applicability in drug repurposing.

The human cytomegalovirus (HCMV) encoded chemokine receptor US28 plays a critical role in viral pathogenesis, mediating several processes such as cellular migration, differentiation, transformation, and viral latency and reactivation. Despite significant research examining the signal transduction pathways utilized by US28, the precise mechanism by which US28 activates these pathways remains unclear. We performed a mutational analysis of US28 to identify signaling domains that are critical for functional activities. Our results indicate that specific residues within the third intracellular loop (ICL3) of US28 are major determinants of G-protein coupling and downstream signaling activity. Alanine substitutions at positions S218, K223, and R225 attenuated US28-mediated activation of MAPK and RhoA signal transduction pathways. Furthermore, we show that mutations at positions S218, K223, or R225 result in impaired coupling to multiple Gα isoforms. However, these substitutions did not affect US28 plasma membrane localization or the receptor internalization rate. Utilizing CD34+ HPC models, we demonstrate that attenuation of US28 signaling via mutation of residues within the ICL3 region results in an inability of the virus to efficiently reactivate from latency. These results were recapitulated in vivo, utilizing a humanized mouse model of HCMV infection. Together, our results provide new insights into the mechanism by which US28 manipulates host signaling networks to mediate viral latency and reactivation. The results reported here will guide the development of targeted therapies to prevent HCMV-associated disease.IMPORTANCEHuman cytomegalovirus (HCMV) is a β-herpesvirus that infects between 44% and 100% of the world population. Primary infection is typically asymptomatic and results in the establishment of latent infection within CD34+hematopoietic progenitor cells (HPCs). However, reactivation from latent infection remains a significant cause of morbidity and mortality in immunocompromised individuals. The viral chemokine receptor US28 influences various cellular processes crucial for viral latency and reactivation, yet the precise mechanism by which US28 functions remains unclear. Through mutational analysis, we identified key residues within the third intracellular loop (ICL3) of US28 that govern G-protein coupling, downstream signaling, and viral reactivation in vitro and in vivo. These findings offer novel insights into how US28 manipulates host signaling networks to regulate HCMV latency and reactivation and expand our understanding of HCMV pathogenesis.

Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF, hence the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome. Validation of docking protocols were achieved with reference inhibitors of the respective targets. Our in silico docking screen identified five drugs (dexamethasone, tadalafil, mefloquine, ergocalciferol, and flunarizine) with strong predicted binding affinity to LASV proteins involved in the formation of the vRNP. We used cell-based functional assays to evaluate the antiviral activity of the five selected drugs. We found that flunarizine, a calcium-entry blocker, inhibited the vRNP activity of LASV and LCMV and virus surface glycoprotein fusion activity required for mammarenavirus cell entry. Consistently with these findings, flunarizine significantly reduced peak titers of LCMV in a multi-step growth kinetics assay in human A549 cells. Flunarizine is being used in several countries worldwide to treat vertigo and migraine, supporting the interest in exploring its repurposing as a candidate drug to treat LASV infections.

In this Letter, a two-term formalism for constructing protein solubility curves in thermal proteome profiling (TPP) is considered, which takes into account the efficiency of the drug-protein binding reaction. When the reaction is incomplete, this results in distortion of the otherwise sigmoidal shape of the curve after drug treatment, which is often observed in experiments. This distortion may be significant enough to disqualify the corresponding protein from the list of drug target candidates, thus negatively affecting the results of TPP data analysis. To further assist this analysis, we also developed the solubility curve simulation software to visualize the discussed effect. Several experimental data sets from recent TPP studies have been reprocessed, and we demonstrate in a few examples that the proposed two-term equation fits correctly the observed protein solubility curves with distorted shapes, also highlighting the previously unrecognized targets.

Membrane proteins are involved in a variety of dynamic cellular processes and exploration of the structural basis of membrane proteins is of significance for a better understanding of their functions. In situ analysis of membrane proteins and their dynamics is, however, challenging for conventional techniques. Surface-enhanced Raman spectroscopy (SERS) is powerful in protein structural characterization, allowing for sensitive, in-situ and real-time identification and dynamic monitoring under physiological conditions. In this review, the applications of SERS in probing membrane proteins are outlined, discussed and prospected. It starts with a brief introduction to membrane proteins, SERS theories and SERS-based strategies that commonly-used for membrane proteins. How to assemble phospholipid biolayers on SERS-active materials is highlighted, followed by respectively discussing about direct and indirect strategies for membrane protein sensing. SERS-based monitoring of protein-ligand interactions is finally introduced and its potential in biomedical applications is discussed in detail. The review ends with critical discussion about current challenges and limitations of this research field, and the promising perspectives in both fundamental and applied sciences.

In this work, a model for anisotropic interactions between proteins and cellular membranes is proposed for large-scale continuum simulations. The framework of the model is based on dynamic density functional theory, which provides a formalism to describe the lipid densities within the membrane as continuum fields while still maintaining the fidelity of the underlying molecular interactions. Within this framework, we extend recent results to include the anisotropic effects of protein-lipid interactions. As applications, we consider two membrane proteins of biological interest: a RAS-RAF complex tethered to the membrane and a membrane embedded G protein-coupled receptor. A strong qualitative and quantitative agreement is found between the numerical results and the corresponding molecular dynamics simulations. Combining the scope of continuum level simulations with the details from molecular level particle simulations enables research into protein-membrane behaviors at a more biologically relevant scale, which crucially can also be accessed via experiment.

Ovarian cancer is known to be a challenging disease to detect at an early stage and is a major cause of death among women. The current treatment for ovarian cancer typically involves a combination of surgery and the use of drugs such as platinum-based cytotoxic agents, anti-angiogenic drugs, etc. However, current treatment methods are not always effective in preventing the recurrence of ovarian cancer. As a result, the treatments administered after a relapse need to be more aggressive, leading to increased toxicity and drug resistance. To address this issue, researchers are exploring the potential of combining existing anticancer agents with novel or repurposed drugs to reduce the side effects and improve the effectiveness of treatment. In this study, we have investigated the use of rosiglitazone, a well-known anti-diabetic drug, in combination with the chemotherapeutic drug, paclitaxel for the prevention of ovarian cancer. The study utilized the SKOV-3 ovarian cancer cell line to assess the effects of this combination treatment. The results of the study showed that the combination of paclitaxel with rosiglitazone inhibited cell proliferation at much lower concentrations of paclitaxel as compared to paclitaxel alone. The combined treatment also induced cell cycle arrest at the G2/M phase and increased apoptosis by altering the mitochondrial membrane potential of the cells. Additionally, the combination treatment activated the PPAR-γ pathway and downregulated expression of genes associated with cancer stemness, such as NANOG, OCT4, and EHF. Furthermore, the CAM assay substantiated the anti-angiogenic potential of the synergistic treatment of paclitaxel and rosiglitazone. The findings of the study suggest that repurposing rosiglitazone as an anticancer agent in combination with paclitaxel has immense potential to target cancer cell cycle progression and apoptosis, making it a promising therapeutic approach for sensitizing chemo-resistant population of ovarian cancer cells.

Membrane transporters are responsible for moving a wide variety of molecules across biological membranes, making them integral to key biological pathways in all organisms. Identifying all membrane transporters within a (meta-)proteome, along with their specific substrates, provides important information for various research fields, including biotechnology, pharmacology, and metabolomics. Protein datasets are frequently annotated with thousands of molecular functions that form complex networks, often with partial or full redundancy and hierarchical relationships. This complexity, along with the low sample count for more specific functions, makes them unsuitable as classes for supervised learning methods, meaning that the creation of an optimal subset of annotations is required. However, selection of this subset requires extensive manual effort, along with knowledge about the biology behind the respective functions. Here, we present an automated pipeline to address this problem. Unlike previous approaches for reducing redundancy in GO datasets, we employ machine learning to identify a subset of functional annotations in a training dataset. Classes in the resulting predictive model meet four essential criteria: sufficient sample size for training predictive models, minimal redundancy, strong class separability, and relevance to substrate transport. Furthermore, we implemented a pipeline for creating training datasets of transmembrane transporters that cover a wide range of organisms, including plants, bacteria, mammals, and single-cell eukaryotes. For a dataset containing 98.1% of transporters from S. cerevisiae, the pipeline automatically reduced the number of functional annotations from 287 to 11 GO terms that could be classified with a median pairwise F1 score of 0.87±0.16. For a meta-organism dataset containing 96% of all transport proteins from S. cerevisiae, A. thaliana, E. coli and human, the number of classes was reduced from 695 to 49, with a median F1 score of 0.92±0.10 between pairs of GO terms. When lowering the percentage of covered proteins down to 67%, the pipeline found a subset of 30 GO terms with a median F1 score of 0.95±0.06.

Ion channels represent a druggable family of transmembrane pore-forming proteins with important (patho)physiological functions. While electrophysiological measurement (manual patch clamp) remains the only direct method for detection of ion currents, it is a labor-intensive technique. Although automated patch clamp instruments have become available to date, their high costs limit their use to large pharma companies or commercial screening facilities. Therefore, fluorescence-based assays are particularly important for initial screening of compound libraries. Despite their numerous disadvantages, they are highly amenable to high-throughput screening and in many cases, no sophisticated instrumentation or materials are required. These features predispose them for implementation in early phases of drug discovery pipelines (hit identification), even in an academic environment. This review summarizes the advantages and pitfalls of individual methodological approaches for identification of ion channel modulators employing fluorescent probes (i. e., membrane potential and ion flux assays) with emphasis on practical aspects of their adaptation to high-throughput format.