Cryo-EM Structure of the Human Cannabinoid Receptor CB2-Gi Signaling Complex.

Drugs selectively targeting CB2 hold promise for treating neurodegenerative disorders, inflammation, and pain while avoiding psychotropic side effects mediated by CB1. The mechanisms underlying CB2 activation and signaling are poorly understood but critical for drug design. Here we report the cryo-EM structure of the human CB2-Gi signaling complex bound to the agonist WIN 55,212-2. The 3D structure reveals the binding mode of WIN 55,212-2 and structural determinants for distinguishing CB2 agonists from antagonists, which are supported by a pair of rationally designed agonist and antagonist. Further structural analyses with computational docking results uncover the differences between CB2 and CB1 in receptor activation, ligand recognition, and Gi coupling. These findings are expected to facilitate rational structure-based discovery of drugs targeting the cannabinoid system.

Differential performance of RoseTTAFold in antibody modeling.

Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody-antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling.

Development of an alcoholic liver disease model for drug evaluation from human induced pluripotent stem cell-derived liver organoids.

Alcoholic liver disease (ALD) poses a significant health challenge, so comprehensive research efforts to improve our understanding and treatment strategies are needed. However, the development of effective treatments is hindered by the limitation of existing liver disease models. Liver organoids, characterized by their cellular complexity and three-dimensional (3D) tissue structure closely resembling the human liver, hold promise as ideal models for liver disease research. In this study, we use a meticulously designed protocol involving the differentiation of human induced pluripotent stem cells (hiPSCs) into liver organoids. This process incorporates a precise combination of cytokines and small molecule compounds within a 3D culture system to guide the differentiation process. Subsequently, these differentiated liver organoids are subject to ethanol treatment to induce ALD, thus establishing a disease model. A rigorous assessment through a series of experiments reveals that this model partially recapitulates key pathological features observed in clinical ALD, including cellular mitochondrial damage, elevated cellular reactive oxygen species (ROS) levels, fatty liver, and hepatocyte necrosis. In addition, this model offers potential use in screening drugs for ALD treatment. Overall, the liver organoid model of ALD, which is derived from hiPSC differentiation, has emerged as an invaluable platform for advancing our understanding and management of ALD in clinical settings.

Decreased sagittal slope of the medial tibial spine and deep concavity of the lateral tibial spine are risk factors for noncontact anterior cruciate ligament injury.

PURPOSE: This study aimed to assess the relationship between the geometric features of tibial eminence and susceptibility to noncontact anterior cruciate ligament (ACL) injuries. METHODS: Patients with unilateral noncontact knee injuries between 2015 and 2021 were consecutively enroled in this study. Based on knee magnetic resonance imaging (MRI) and arthroscopic visualisation, patients were categorised into the case group (ACL rupture) and control group (ACL intact). Using MRI, the geometric features of tibial eminence were characterised by measuring the sagittal slopes, depth of concavity and coronal slopes of the inclined surfaces of the tibial spines. Univariate and multivariate logistic regressions were conducted to explore independent associations between quantified geometric indices of tibial eminence and the risk of noncontact ACL injuries. RESULTS: This study included 187 cases and 199 controls. A decreased sagittal slope of the medial tibial spine (MTSSS) (combined group: odds ratio [OR]: 0.87 [0.82, 0.92], p < 0.001; females: OR: 0.88 [0.80, 0.98], p = 0.020; males: OR: 0.87 [0.81, 0.93], p < 0.001) and an increased depth of concavity in the lateral tibial spine (LTSD) (combined group: OR: 1.51 [1.24, 1.85], p < 0.001; females: OR: 1.65 [1.12, 2.43], p = 0.012; males: OR: 1.44 [1.11, 1.89], p = 0.007) were independent risk factors for noncontact ACL injuries. Moreover, a steeper coronal slope of the inclined surface of the medial tibial spine was a significant predictor of noncontact ACL injuries for males (MTSCS: OR: 1.04 [1.01, 1.08], p = 0.015) but not for females. CONCLUSION: Geometric features of tibial eminence, particularly a decreased MTSSS and an increased LTSD, were identified as independent risk factors for noncontact ACL injuries. These findings will help clinicians identify individuals at high risk of ACL injury and facilitate the development of targeted prevention strategies. LEVEL OF EVIDENCE: Level III.

MCCS, a novel characterization method for protein-ligand complex.

Delineating the fingerprint or feature vector of a receptor/protein will facilitate the structural and biological studies, as well as the rational design and development of drugs with high affinities and selectivity. However, protein is complicated by its different functional regions that can bind to some of its protein partner(s), substrate(s), orthosteric ligand(s) or allosteric modulator(s) where cogent methods like molecular fingerprints do not work well. We here elaborate a scoring-function-based computing protocol Molecular Complex Characterizing System to help characterize the binding feature of protein-ligand complexes. Based on the reported receptor-ligand interactions, we first quantitate the energy contribution of each individual residue which may be an alternative of MD-based energy decomposition. We then construct a vector for the energy contribution to represent the pattern of the ligand recognition at a receptor and qualitatively analyze the matching level with other receptors. Finally, the energy contribution vector is explored for extensive use in similarity and clustering. The present work provides a new approach to cluster proteins, a perspective counterpart for determining the protein characteristics in the binding, and an advanced screening technique where molecular docking is applicable.

Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research.

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

IsAb: a computational protocol for antibody design.

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs.

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Metabolic associated fatty liver disease and bone mineral density: a cross-sectional study of the National Health and Nutrition Examination Survey 2017-2018.

This research is a cross-sectional study based on the participants aged 50 years and older from National Health and Nutrition Examination Survey (NHANES) database. The metabolic associated fatty liver disease (MAFLD) population has higher BMD and a lower risk of osteoporosis than those without MAFLD. INTRODUCTION: MAFLD is a new definition presented by panel of experts based on non-alcoholic fatty liver disease in 2020. However, the link between MAFLD and bone mineral density (BMD) is uncertain. Thus, the present study aimed to investigate the relationship between MAFLD and BMD. METHODS: This cross-sectional study included subjects aged ≥ 50 years from the National Health and Nutrition Examination Survey 2017-2018. Multivariate linear regression models were performed to investigate the association between MAFLD and BMD. Moreover, the relationship between MAFLD and osteoporosis was assessed using multiple logistic regression models. RESULTS: Finally, 817 participants (non-MAFLD, n = 436; MAFLD, n = 381) were included in the final analysis. The results demonstrated that participants with MAFLD showed higher femoral BMDs than those without MAFLD, especially among males aged ≥ 50 years and females aged ≥ 65 years. Moreover, the results showed that obese men (BMI ≥ 30 kg/m2) with MAFLD had higher femoral BMDs than the control group according to subgroup analyses stratified by BMI, but this trend was not present in women. In addition, multiple logistic regression models showed that participants with MAFLD had no increased risks of osteoporosis. CONCLUSION: The present study found that the MAFLD population has higher BMD and a lower risk of osteoporosis than those without MAFLD. Because the present study was a cross-sectional study, we could not identify the cause-effect relation between MAFLD and BMD. Therefore, additional research needs to be performed to explore the influences of MAFLD on bone metabolism in the future.

Fluvoxamine prompts the antitumor immune effect via inhibiting the PD-L1 expression on mice-burdened colon tumor.

A colon tumor, one of the digestive tract malignant tumors, is harmful to human health. A potential new treatment still deserves attention. The development of a new drug needs more resources, including time and expense. Therefore, the old drug with new targets has become a current research hotspot. Fluvoxamine, as an antidepressant, could play an effect on inhibiting 5-hydroxytryptamine reuptake. In the present research, the antitumor effects and possible mechanisms of fluvoxamine are validated. The results showed that fluvoxamine significantly suppressed the migration and proliferation of tumor cells, and increased the apoptosis in vitro. Additionally, fluvoxamine significantly delays tumor development, and prompts the apoptosis in tumor tissues of mice-burdened colon tumors in vivo. The tumor suppression might be related with that fluvoxamine inhibits the expression of phosphorylated signal transducer and activator of transcription 3, matrix metalloproteinase 2, and cleaved-caspase 3. Importantly, fluvoxamine significantly reduces the expression level of programmed cell death ligand 1. This could be a possible reason that treatment with fluvoxamine drives the infiltration of T lymphocytes and M1-type macrophages in tumor tissues. Taken together, this research suggests that fluvoxamine might be a promising drug to treat colon cancer by inhibiting the proliferation and migration, inducing apoptosis, and even increasing the immune response of antitumor.

Combination of an optical waveguide platform and ultra-thin spectrometer that enables increased surface plasmon resonance sensor compactness.

A novel integrated surface plasmon resonance (SPR) sensor that combines an optical waveguide platform and an ultra-thin spectrometer is proposed. The core of the proposed method is a special-shaped optical waveguide structure that employs a wedge-shaped incident surface, which changes the position of the total reflection of the incident light on the sagittal plane without affecting the direction of propagation on the tangential plane. The parameters of the sensing module with the integrated SPR sensor and spectrometer module were designed and optimized to achieve higher performance in a compact optical waveguide platform. An experimental system was built based on the theoretical model, and the spectral sensitivity of the system was analyzed before sample detection, and the results showed that the spectral resolution in the working range could reach 9.9 nm. The refractive index sensitivity of this novel SPR sensor was 3186 nm/RIU with good stability by detecting different concentrations of sodium chloride samples. This new structure does not require an external spectrometer, thereby enabling an increase in the compactness of the SPR sensing system. The proposed method can provide a novel idea for the miniaturization of SPR sensors.

Nocturnal sleep duration and bone mineral density: a cross-sectional study of the National Health and Nutrition Examination Survey (NHANES) 2007-2014.

BACKGROUND: This study aimed to investigate the association between sleep duration and bone mineral density (BMD) and determine whether vitamin D (VD) status influenced the association between sleep duration and BMD. METHODS: National Health and Nutrition Examination Survey 2007-2014 participants aged ≥ 40 years were included in this study. BMD testing was conducted with dual-energy X-ray absorptiometry examinations. Moreover, all individuals were divided into four groups according to self-reported nocturnal sleep duration (7-8 h; 6 h; < 6 h; and > 8 h). In addition, the differences in BMD between the normal sleep duration group and other groups were calculated using multiple linear regression models. RESULTS: Overall, the median age of the overall study population was 55.00 years old, with 46.97% of men distributed. Participants sleeping > 8 h/night had lower BMDs than those sleeping 7-8 h/night. Moreover, the association between unhealthy sleep duration (especially > 8 h/night) and low BMD was more pronounced in older individuals, men, postmenopausal women, and subjects with inadequate VD intakes (< 15.00 µg/day) or deficient/insufficient serum 25-hydroxyvitamin D (< 75.00 nmol/L). CONCLUSIONS: In conclusion, unhealthy sleep duration, especially long sleep duration, was associated with decreased BMD, particularly among individuals aged > 60 years, men, or postmenopausal women. Moreover, VD status might influence the association between sleep duration and BMD, especially in the context of inadequate VD intake or deficient/insufficient serum 25-hydroxyvitamin D levels. However, given the limitations of the present study, further investigation is warranted to confirm this association and to explore potential mechanisms.

In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators.

Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-ß-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future.

High-Performance Ultra-Thin Spectrometer Optical Design Based on Coddington's Equations.

A unique method to design a high-throughput and high-resolution ultrathin Czerny-Turner (UTCT) spectrometer is proposed. This paper reveals an infrequent design process of spectrometers based on Coddington's equations, which will lead us to develop a high-performance spectrometer from scratch. The spectrometer is composed of cylindrical elements except a planar grating. In the simulation design, spot radius is sub-pixel size, which means that almost all of the energy is collected by the detector. The spectral resolution is 0.4 nm at central wavelength and 0.75 nm at edge wavelength when the width of slit is chosen to be 25 µm and the groove density is 900 lines/mm.

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affecting the diagnosis. Therefore, in this study, we investigate a dual residual convolution neural network (DRCNN) for low-dose CT (LDCT) imaging, whereby the CT images are reconstructed directly from the sinogram by integrating analytical domain transformations, thus reducing the loss of projection information. With this new framework, feature extraction is performed simultaneously on both the sinogram-domain sub-net and the image-domain sub-net, which utilize the residual shortcut networks and play a complementary role in suppressing the projection noise and reducing image error. This new DRCNN approach helps not only decrease the sinogram noise but also preserve significant structural information. The experimental results of simulated and real projection data demonstrate that our DRCNN achieve superior performance over other state-of-art methods in terms of visual inspection and quantitative metrics. For example, comparing with RED-CNN and DP-ResNet, the value of PSNR using our DRCNN is improved by nearly 3 dB and 1 dB, respectively.

An effective sinogram inpainting for complementary limited-angle dual-energy computed tomography imaging using generative adversarial networks.

Dual-energy computed tomography (DECT) provides more anatomical and functional information for image diagnosis. Presently, the popular DECT imaging systems need to scan at least full angle (i.e., 360°). In this study, we propose a DECT using complementary limited-angle scan (DECT-CL) technology to reduce the radiation dose and compress the spatial distribution of the imaging system. The dual-energy total scan is 180°, where the low- and high-energy scan range is the first 90° and last 90°, respectively. We describe this dual limited-angle problem as a complementary limited-angle problem, which is challenging to obtain high-quality images using traditional reconstruction algorithms. Furthermore, a complementary-sinogram-inpainting generative adversarial networks (CSI-GAN) with a sinogram loss is proposed to inpainting sinogram to suppress the singularity of truncated sinogram. The sinogram loss focuses on the data distribution of the generated sinogram while approaching the target sinogram. We use the simultaneous algebraic reconstruction technique namely, a total variable (SART-TV) algorithms for image reconstruction. Then, taking reconstructed CT images of pleural and cranial cavity slices as examples, we evaluate the performance of our method and numerically compare different methods based on root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Compared with traditional algorithms, the proposed network shows advantages in numerical terms. Compared with Patch-GAN, the proposed network can also reduce the RMSE of the reconstruction results by an average of 40% and increase the PSNR by an average of 26%. In conclusion, both qualitative and quantitative comparison and analysis demonstrate that our proposed method achieves a good artifact suppression effect and can suitably solve the complementary limited-angle problem.

NCAM regulates the proliferation, apoptosis, autophagy, EMT, and migration of human melanoma cells via the Src/Akt/mTOR/cofilin signaling pathway.

The neural cell adhesion molecule (NCAM) plays critical roles in multiple cellular processes in neural cells, mesenchymal stem cells, and various cancer cells. However, the effect and mechanism of NCAM in human melanoma cells are still unclear. In this study, we found that NCAM regulated the proliferation, apoptosis, autophagy, migration, and epithelial-to-mesenchymal transition of human melanoma cells by determining the biological behavior of NCAM knockdown A375 and M102 human melanoma cells. Further studies revealed that NCAM knockdown impaired the organization of actin cytoskeleton and reduced the phosphorylation of cofilin, an actin-cleaving protein. When cells were transfected with cofilin S3A (dephosphorylated cofilin), biological behavior similar to that of NCAM knockdown cells was observed. Research on the underlying molecular mechanism showed that NCAM knockdown suppressed activation of the Src/Akt/mTOR pathway. Specific inhibitors of Src and PI3K/Akt were employed to further verify the relationship between Src/Akt/mTOR signaling and cofilin, and the results showed that the phosphorylation level of cofilin decreased following inhibition of the Src/Akt/mTOR pathway. These results indicated that NCAM may regulate the proliferation, apoptosis, autophagy, migration, and epithelial-to-mesenchymal transition of human melanoma cells via the Src/Akt/mTOR/cofilin pathway-mediated dynamics of actin cytoskeleton.

Combination of attenuated Salmonella carrying PD-1 siRNA with nifuroxazide for colon cancer therapy.

Colon cancer is a member of malignant tumors in the digestive system. Traditional treatment strategies are ineffective and improving the treatment of colon cancer is an urgent need. Targeting programmed cell death-1 (PD-1) by monoclonal antibodies has shown some therapeutic effectiveness and has advantages. Additionally, the Stat3 inhibitor nifuroxazide was employed to promote the antitumor activity. Here, we hypothesized that combining nifuroxazide with PD-1 small interfering RNA carried by attenuated Salmonella would exert a synergistic antitumor effect on colon cancer. Indeed, treatment with this combination effectively inhibited the development of colon cancer in mice and improved the survival rate. These two novel anticancer agents worked synergistically to elicit potent antitumor immunity and achieve improved therapeutic efficacy. The underlying mechanisms are mainly involved with immune regulation and cell apoptosis. This study provides a previous framework for combining this Stat3 inhibitor with RNAi designed to block immune checkpoint signaling for cancer therapy.

Simultaneous multipolarization and high-resolution oxygen A-band spectrometer optical design with the astigmatism-corrected method.

Simultaneous multipolarization and high-resolution oxygen A-band spectrometer (SPHABS) is proposed. The astigmatism correction theory is used to separate beams from different fields of view and make it possible to obtain multiple polarization information simultaneously. SPHABS' design and the basic principle of SPHABS and the astigmatism correction theory are elaborated in detail. The ray-tracing results of the model showed that the resolution reached 0.016 nm and information from four fields of view could be obtained simultaneously on the image surface.

Pain-CKB, A Pain-Domain-Specific Chemogenomics Knowledgebase for Target Identification and Systems Pharmacology Research.

A traditional single-target analgesic, though it may be highly selective and potent, may not be sufficient to mitigate pain. An alternative strategy for alleviation of pain is to seek simultaneous modulation at multiple nodes in the network of pain-signaling pathways through a multitarget analgesic or drug combinations. Here we present a comprehensive pain-domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated computing tools for target identification and systems pharmacology research. Pain-CKB is constructed on the basis of our established chemogenomics technology with new features, including multiple compound support, multicavity protein support, and customizable symbol display. The determination of bioactivity is also revised to avoid the use of complex machine learning models. Our one-stop computing platform describes the chemical molecules, genes, and proteins involved in pain regulation. To date, Pain-CKB has archived 272 analgesics in the market, 84 pain-related targets with 207 available 3D crystal or cryo-EM structures, and 234 662 chemical agents reported for these target proteins. Moreover, Pain-CKB implements user-friendly web-interfaced computing tools and applications for the prediction and analysis of the relevant protein targets and visualization of the outputs, including HTDocking, TargetHunter, BBB permeation predictor, NGL viewer, Spider Plot, etc. The Pain-CKB server is accessible at https://www.cbligand.org/g/pain-ckb.