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G-protein-coupled receptors (GPCRs) remain major drug targets, despite our incomplete understanding of how they signal through 16 non-visual G-protein signal transducers (collectively named the transducerome) to exert their actions. To address this gap, we have developed an open-source suite of 14 optimized bioluminescence resonance energy transfer (BRET) Gαβγ biosensors (named TRUPATH) to interrogate the transducerome with single pathway resolution in cells. Generated through exhaustive protein engineering and empirical testing, the TRUPATH suite of Gαβγ biosensors includes the first Gα15 and GαGustducin probes. In head-to-head studies, TRUPATH biosensors outperformed first-generation sensors at multiple GPCRs and in different cell lines. Benchmarking studies with TRUPATH biosensors recapitulated previously documented signaling bias and revealed new coupling preferences for prototypic and understudied GPCRs with potential in vivo relevance. To enable a greater understanding of GPCR molecular pharmacology by the scientific community, we have made TRUPATH biosensors easily accessible as a kit through Addgene. Development of BRET sensors for nearly all major G proteins show that GPCR–G-protein coupling ranges from promiscuous to extremely specific, Switch III is a novel site for G-protein engineering, and optimal donor–acceptor positioning is non-obvious.

The aim of this Special Issue on “Plant Natural Products for Human Health” is to compile a series of scientific reports to demonstrate the medicinal potential of plant natural products, such as in vitro and in vivo activities, clinical effects, mechanisms of action, structure-activity relationships, and pharmacokinetic properties. With the global trend growing in popularity for botanical dietary supplements and phytopharmaceuticals, it is hoped that this Special Issue would serve as a timely reference for researchers and scholars who are interested in the discovery of potentially useful molecules from plant sources for health-related applications.

In our society today, pain has become a main source of strain on most individuals. It is crucial to develop novel treatments against pain while focusing on decreasing their adverse effects. Throughout the extent of development for new pain therapies, the nociceptin/orphanin FQ receptor (NOP receptor) has appeared to be an encouraging focal point. Concentrating on NOP receptor to treat chronic pain with limited range of unwanted effects serves as a suitable alternative to prototypical opioid morphine that could potentially lead to life-threatening effects caused by respiratory depression in overdose, as well as generate abuse and addiction. In addition to these harmful effects, the uprising opioid epidemic is responsible for becoming one of the most disastrous public health issues in the US. In this article, the contributing molecular and cellular structure in controlling the cellular trafficking of NOP receptor and studies that support the role of NOP receptor and its ligands in pain management are reviewed.

An X-ray structure of the D2 dopamine receptor bound to the atypical antipsychotic drug risperidone reveals an extended binding pocket and indicates structural features that could be used to design drugs that specifically target the D2 receptor. Dopamine's unusual binding technique D2 dopamine receptors are the principal targets for antipsychotic drugs for the treatment of schizophrenia, and offer possibilities for treating depression and Parkinson's disease. However, molecular-level understanding of these receptors is limited, and many available drugs cause serious side-effects as a result of activity at other dopamine receptors. Here, Bryan Roth and colleagues report the crystal structure of the D2 receptor in complex with the antipsychotic drug risperidone. This structure shows an unusual binding mode of the drug, distinct from those observed in the related D3 and D4 receptors, whereby a hydrophobic patch formed by a tryptophan residue regulates the entry and exit of the drug. Mutation at this position reduces the drug residence time, which is believed to be related to side-effects of common antipsychotics. This work hints at ways to develop safer antipsychotic drugs that are selective for D2. Dopamine is a neurotransmitter that has been implicated in processes as diverse as reward, addiction, control of coordinated movement, metabolism and hormonal secretion. Correspondingly, dysregulation of the dopaminergic system has been implicated in diseases such as schizophrenia, Parkinson’s disease, depression, attention deficit hyperactivity disorder, and nausea and vomiting. The actions of dopamine are mediated by a family of five G-protein-coupled receptors 1 . The D2 dopamine receptor (DRD2) is the primary target for both typical 2 and atypical 3 , 4 antipsychotic drugs, and for drugs used to treat Parkinson’s disease. Unfortunately, many drugs that target DRD2 cause serious and potentially life-threatening side effects due to promiscuous activities against related receptors 4 , 5 . Accordingly, a molecular understanding of the structure and function of DRD2 could provide a template for the design of safer and more effective medications. Here we report the crystal structure of DRD2 in complex with the widely prescribed atypical antipsychotic drug risperidone. The DRD2–risperidone structure reveals an unexpected mode of antipsychotic drug binding to dopamine receptors, and highlights structural determinants that are essential for the actions of risperidone and related drugs at DRD2.

Recent studies show that GPCRs rapidly interconvert between multiple states although our ability to interrogate, monitor and visualize them is limited by a relative lack of suitable tools. We previously reported two nanobodies (Nb39 and Nb6) that stabilize distinct ligand- and efficacy-delimited conformations of the kappa opioid receptor. Here, we demonstrate via X-ray crystallography a nanobody-targeted allosteric binding site by which Nb6 stabilizes a ligand-dependent inactive state. As Nb39 stabilizes an active-like state, we show how these two state-dependent nanobodies can provide real-time reporting of ligand stabilized states in cells in situ. Significantly, we demonstrate that chimeric GPCRs can be created with engineered nanobody binding sites to report ligand-stabilized states. Our results provide both insights regarding potential mechanisms for allosterically modulating KOR with nanobodies and a tool for reporting the real-time, in situ dynamic range of GPCR activity. Recent studies revealed that G protein-coupled receptors rapidly interconvert between multiple states. Here, authors use the kappa opioid receptor (KOR) and show how two state-dependent nanobodies provide real-time reporting of ligand stabilized states with KOR and other GPCRs.

Wind shapes high‐altitude winter snow mass balance and influences water resources by controlling snow accumulation, erosion, and sublimation loss, yet accurately quantifying these processes remains challenging in high‐altitude regions like the Tibetan Plateau due to complex wind‐snow interactions and extreme measurement conditions. To address these challenges, we present an integrated observation system to monitor wind‐blown snow processes and develop a Gaussian kernel‐based probabilistic classification method that incorporates measurement uncertainties to identify wind‐driven snow events. This method enables more robust analysis of rapid snow mass changes compared to traditional classification. The study site is in the northeastern Tibetan Plateau at 4,147 m elevation with strong winds and frequent winter snowfall. Our results show that wind‐driven snow deposition and erosion events account for 68.5% of observed snow mass changes, while purely precipitation‐driven accumulation events only contribute 3.1% of total changes, with the remaining 28.4% being mixed events involving both precipitation and wind‐driven processes. Our results provide observational evidence that blowing snow sublimation is amplified when wind speeds exceed approximately 8 m s−1${\\mathrm{s}}^{-1}$ , highlighting the pivotal role of suspended particles in enhancing evapotranspiration losses. This continuous wind‐driven reshaping of the snowpack leads to rapid changes in snow depth, density, and even thermal properties, challenging traditional modeling approaches that assume more gradual layer evolution. This study provides a robust method for identifying wind‐driven snow events and quantifying their influences on snow mass balance. Our findings emphasize the importance of incorporating wind‐driven processes in high‐altitude snow models and monitoring systems to better understand snow dynamics.

Long-term snow depth/snow water equivalent (SWE) products derived from passive microwave remote sensing data are fundamental for climatological and hydrological studies. However, the temporal continuity of the products is affected by the updating or replacement of passive microwave sensors or satellite platforms. In this study, we inter-calibrated brightness temperature (Tb) data obtained from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMI/S). Then, we evaluated the consistency of the snow cover area (SCA) and snow depth derived from the Scanning Multichannel Microwave Radiometer (SMMR), SSM/I and SSMI/S. The results indicated that (1) the spatial pattern of the SCA derived from the SMMR and SSM/I data was more consistent after calibration than before; (2) the relative biases in the SCA and snow depth in China between the SSM/I and SSMI/S data decreased from 42.42% to 1.65% and from 66.18% to −1.5%, respectively; and (3) the SCA and snow depth derived from the SSM/I data carried on F08, F11 and F13 were highly consistent. To obtain consistent snow depth and SCA products, inter-sensor calibrations between SMMR, SSM/I and SSMI/S are important. In consideration of the snow data product continuation, we suggest that the brightness temperature data from all sensors be calibrated based on SSMI/S.

Rheumatoid arthritis (RA) is a chronic, debilitating illness characterized by painful swelling of the joints, inflammation of the synovial lining of the joints, and damage to cartilage and bone. Several anti-inflammatory and disease-modifying drugs are available for RA therapy. However, the prolonged use of these drugs is associated with severe side effects. Furthermore, these drugs are effective only in a proportion of RA patients. Hence, there is a need to search for new therapeutic agents that are effective yet safe. Interestingly, a variety of herbs and other natural products offer a vast resource for such anti-arthritic agents. We discuss here the basic features of RA pathogenesis; the commonly used animal models of RA; the mainstream drugs used for RA; the use of well-characterized natural products possessing anti-arthritic activity; the application of nanoparticles for efficient delivery of such products; and the interplay between dietary products and the host microbiome for maintenance of health and disease induction. We believe that with several advances in the past decade in the characterization and functional studies of natural products, the stage is set for widespread clinical testing and/or use of these products for the treatment of RA and other diseases.

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D 4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D 4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D 4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D 4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D 4 dopamine receptor. Using a make-on-demand library that contains hundreds-of-millions of molecules, structure-based docking was used to identify compounds that, after synthesis and testing, are shown to interact with AmpC β-lactamase and the D 4 dopamine receptor with high affinity.