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The antineoplastic drug carmofur is shown to inhibit the SARS-CoV-2 main protease (Mpro). Here, the X-ray crystal structure of Mpro in complex with carmofur reveals that the carbonyl reactive group of carmofur is covalently bound to catalytic Cys145, whereas its fatty acid tail occupies the hydrophobic S2 subsite. Carmofur inhibits viral replication in cells (EC50 = 24.30 μM) and is a promising lead compound to develop new antiviral treatment for COVID-19.A crystal structure of SARS-CoV-2 with inhibitor carmofur reveals the mechanism of action of this compound and opens the way to develop more potent drugs.

The enhancer of zeste homolog 2 (EZH2) is the main enzymatic subunit of the PRC2 complex, which catalyzes trimethylation of histone H3 lysine 27 (H3K27me3) to promote transcriptional silencing. EZH2 is overexpressed in multiple types of cancer including triple-negative breast cancer (TNBC), and high expression levels correlate with poor prognosis. Several EZH2 inhibitors, which inhibit the methyltransferase activity of EZH2, have shown promise in treating sarcoma and follicular lymphoma in clinics. However, EZH2 inhibitors are ineffective at blocking proliferation of TNBC cells, even though they effectively reduce the H3K27me3 mark. Using a hydrophobic tagging approach, we generated MS1943, a first-in-class EZH2 selective degrader that effectively reduces EZH2 levels in cells. Importantly, MS1943 has a profound cytotoxic effect in multiple TNBC cells, while sparing normal cells, and is efficacious in vivo, suggesting that pharmacologic degradation of EZH2 can be advantageous for treating the cancers that are dependent on EZH2. A hydrophobic tagging method is used to develop a selective degrader for the chromatin regulator EZH2, which inhibits proliferation of triple-negative breast cancer cell lines in vitro and in vivo.

China's rapid urbanization and high traffic accident frequency have received many researchers' attention. It is important to reveal how urban infrastructures and other risk factors affects the traffic accident frequency. A growing amount of research has examined the local risk factors impact on traffic accident frequency at certain time. Some studies considered these spatial influences but overlooked the temporal correlation/heterogeneity of traffic accidents and related risk factors. This study explores risk factors' influence on urban traffic accidents frequency while considering both the spatial and temporal correlation/heterogeneity of traffic accidents. The study area is split into 100 equally sized rectangle traffic analysis zones (TAZs), and the urban traffic accident frequency and attributes in each TAZ are extracted. The linear regression model, spatial lag model (SLM), spatial error model (SEM) and time-fixed effects error model (T-FEEM) are established and compared respectively. The proposed methodologies are illustrated using ten-month traffic accident data from the urban area of Guiyang City, China. The results reveal that the time-fixed effects error model, which considers both spatial and temporal correlation/heterogeneity of traffic accidents, is superior to other models. More traffic accidents will happen in those TAZs that have more hospitals or schools. Moreover, hospitals have a greater influence on traffic accidents than schools. Because of the location in the margin of the city, those TAZs that have passenger stations have more traffic accidents. This study provides policy makers with more detailed characterization about the impact of related risk factors on traffic accident frequencies, and it is suggested that not only the spatial correlation/heterogeneity but also the temporal correlation/heterogeneity should be taken into account in guiding traffic accident control of urban area.

The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC 50 and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/ ) and at github ( https://github.com/fenglei104/DeepPROTACs ). The rational design of PROTACs is difficult due to their obscure structure-activity relationship. Here the authors present a deep neural network model - DeepPROTACs - for predicting the degradation capacity of a proposed PROTAC molecule.

In this paper, we develop a new car-following model with consideration of varying road condition based on the empirical data. Firstly, we explore the effects of road condition on uniform flow from analytical and numerical perspectives. The results indicate that road condition has great influences on uniform flow, i.e., good road condition can enhance the velocity and flow and their increments will increase when road condition becomes better; bad road conditions will reduce the velocity and flow and their reductions will increase when road condition turns worse. Secondly, we study the effects of road conditions on the starting and braking processes. The numerical results show that good road condition will speed up the two processes and that bad road condition will slow down the two processes. Finally, we study the effects of road condition on small perturbation. The numerical results indicate that the stop-and-go phenomena resulted by small perturbation will become more serious when the road condition becomes better.

One of the significant features of the transverse flux linear motors (TFLMs) is the relatively higher thrust density, since the main flux loop of TFLM is located on a perpendicular plane to the motion direction. As one type of reluctance TFLM, the transverse-flux flux-reversal linear motor (TF-FRLM) is an interesting topology for the long stroke scene, which owns a passive reluctance type secondary, and the high-priced permanent magnets are only fixed on the short primary. To further enhance the practicality of the TF-FRLM, this paper focuses on the topology improvement and the control methods of TF-FRLM. Based on an improved TF-FRLM, a model-free predictive current control (MFPCC) method with suppressed sampling noise is proposed in this paper. Firstly, the details of structural improvements on the TF-FRLM topology are described, and some typical performances of TF-FRLMs are simulated by the three-dimensional finite element method and tested by a prototype. Then, based on the proposed basic principle of MFPCC, the reference current for inner-loop control is predicted. To ensure the prediction accuracy of the current in the MFPCC control method, the average filtering principle is used to suppress the impact of current sampling noise on performance. Finally, through comparative experiments on MFPCC schemes on the prototype platform, the effectiveness of the proposed control method is verified.

Rho family mechano-signaling through the actin cytoskeleton positively regulates physiological TEAD/YAP transcription, while the evolutionarily conserved Hippo tumor suppressor pathway antagonizes this transcription through YAP cytoplasmic localization/degradation. The mechanisms responsible for oncogenic dysregulation of these pathways, their prevalence in tumors, as well as how such dysregulation can be therapeutically targeted are not resolved. We demonstrate that p53 DNA contact mutants in human tumors, indirectly hyperactivate RhoA/ROCK1/actomyosin signaling, which is both necessary and sufficient to drive oncogenic TEAD/YAP transcription. Moreover, we demonstrate that recurrent lesions in the Hippo pathway depend on physiological levels of ROCK1/actomyosin signaling for oncogenic TEAD/YAP transcription. Finally, we show that ROCK inhibitors selectively antagonize proliferation and motility of human tumors with either mechanism. Thus, we identify a cancer driver paradigm and a precision medicine approach for selective targeting of human malignancies driven by TEAD/YAP transcription through mechanisms that either upregulate or depend on homeostatic RhoA mechano-signaling.

Inflammasome activity is important for the immune response and is instrumental in numerous clinical conditions. Here we identify a mechanism that modulates the central Caspase-1 and NLR (Nod-like receptor) adaptor protein ASC (apoptosis-associated speck-like protein containing a CARD). We show that the function of ASC in assembling the inflammasome is controlled by its modification with SUMO (small ubiquitin-like modifier) and identify that the nuclear ZBTB16 (zinc-finger and BTB domain-containing protein 16) promotes this SUMOylation. The physiological significance of this activity is demonstrated through the reduction of acute inflammatory pathogenesis caused by a constitutive hyperactive inflammasome by ablating ZBTB16 in a mouse model of Muckle-Wells syndrome. Together our findings identify an further mechanism by which ZBTB16-dependent control of ASC SUMOylation assembles the inflammasome to promote this pro-inflammatory response. Inflammasomes are multiprotein complexes, including the protein ASC, that assemble in response to inflammatory stimulation. Here the authors characterise the regulation of ASC during inflammasome formation and show the involvement of SUMOylation and zinc-finger and BTB domain-containing protein 16 (ZBTB16).

Accumulating evidence suggests that tumor-infiltrating immune cells (TICs) in the tumor microenvironment (TME) serve as promising therapeutic targets. CXCL8 (IL-8) may also be a potential therapeutic target in cancer. CXCL8 is a potent chemotactic factor for neutrophils, myeloid-derived suppressor cells (MDSCs) and monocytes, which are considered immunosuppressive components in cancer-bearing hosts. Here, we identified the TME-related gene CXCL8 in a high-ImmuneScore population that contributed to better survival in colorectal cancer (CRC) patients from The Cancer Genome Atlas (TCGA) database. An integrated gene profile and functional analysis of TIC proportions revealed that the dendritic cell (DC) activation markers CD80, CD83, and CD86 were positively correlated with CXCL8 expression, suggesting that CXCL8 may be functional as antitumor immune response status in the TME. The gene signature was further validated in independent GSE14333 and GSE38832 cohorts from the Gene Expression Omnibus (GEO). To test the differential contributions of immune and tumor components to progression, three CRC cell lines, CT26, MC38 and HCT116, were used. In vitro results suggested no significant growth or survival changes following treatment with an inhibitor of the CXCL8 receptor (CXCR1/2) such as reparixin or danirixin. In vivo treatment with danirixin (antagonists of CXCR2) promoted tumor progression in animal models established with CT26 cells. CXCR2 antagonism may function via an immune component, with CXCR2 antagonist treatment in mice resulting in reduced activated DCs and correlating with decreased Interferon gamma (IFN-γ) or Granzyme B expressed CD8 + T cells. Furthermore, CXCL8 induced DC migration in transwell migration assays. Taken together, our data suggested that targeting the CXCL8-CXCR2 axis might impede DC activation or recruitment, and this axis could be considered a favorable factor rather than a target for critical antitumor effects on CRC.