Timing a switch in tissue integrity: In plants, sperm cells travel through the pollen tube as it grows toward the ovule. Successful fertilization depends on the pollen tube rupturing to release the sperm cells (see the Perspective by Stegmann and Zipfel). Ge et al. and Mecchia et al. elucidated the intercellular cross-talk that maintains pollen tube integrity during growth but destroys it at just the right moment. The signaling peptides RALF4 and RALF19, derived from the pollen tube, maintain its integrity as it grows. Once in reach of the ovule, a related signaling peptide, RALF34, which derives from female tissues, takes over and causes rupture of the pollen tube. Science , this issue p. 1596 , p. 1600 ; see also p. 1544

Drug-induced liver injury (DILI) has been the single most frequent cause of safety-related drug marketing withdrawals for the past 50 years. Recently, deep learning (DL) has been successfully applied in many fields due to its exceptional and automatic learning ability. In this study, DILI prediction models were developed using DL architectures, and the best model trained on 475 drugs predicted an external validation set of 198 drugs with an accuracy of 86.9%, sensitivity of 82.5%, specificity of 92.9%, and area under the curve of 0.955, which is better than the performance of previously described DILI prediction models. Furthermore, with deep analysis, we also identified important molecular features that are related to DILI. Such DL models could improve the prediction of DILI risk in humans. The DL DILI prediction models are freely available at http://www.repharma.cn/DILIserver/DILI_home.php.

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

The 3C-like proteinase of severe acute respiratory syndrome (SARS) coronavirus has been proposed to be a key target for structural-based drug design against SARS. In order to understand the active form and the substrate specificity of the enzyme, we have cloned, expressed, and purified SARS 3C-like proteinase. Analytic gel filtration shows a mixture of monomer and dimer at a protein concentration of 4 mg/ml and mostly monomer at 0.2 mg/ml, which correspond to the concentration used in the enzyme assays. The linear decrease of the enzymatic-specific activity with the decrease of enzyme concentration revealed that only the dimeric form is active and the dimeric interface could be targeted for structural-based drug design against SARS 3C-like proteinase. By using a high pressure liquid chromatography assay, SARS 3C-like proteinase was shown to cut the 11 peptides covering all of the 11 cleavage sites on the viral polyprotein with different efficiency. The two peptides corresponding to the two self-cleavage sites are the two with highest cleavage efficiency, whereas peptides with non-canonical residues at P2 or P1′ positions react slower. The P2 position of the substrates seems to favor large hydrophobic residues. Secondary structure studies for the peptide substrates revealed that substrates with more β-sheetlike structure tend to react fast. This study provides a basic understanding of the enzyme catalysis and a full substrate specificity spectrum for SARS 3C-like proteinase, which are helpful for structural-based inhibitor design against SARS and other coronavirus.

COVID-19 has become a global pandemic and there is an urgent call for developing drugs against the virus (SARS-CoV-2). The 3C-like protease (3CL^pro) of SARS-CoV-2 is a preferred target for broad spectrum anti-coronavirus drug discovery. We studied the anti-SARS-CoV-2 activity of S. baicalensis and its ingredients. We found that the ethanol extract of S. baicalensis and its major component, baicalein, inhibit SARS-CoV-2 3CL^pro activity in vitro with IC₅₀’s of 8.52 µg/ml and 0.39 µM, respectively. Both of them inhibit the replication of SARS-CoV-2 in Vero cells with EC₅₀’s of 0.74 µg/ml and 2.9 µM, respectively. While baicalein is mainly active at the viral post-entry stage, the ethanol extract also inhibits viral entry. We further identified four baicalein analogues from other herbs that inhibit SARS-CoV-2 3CL^pro activity at µM concentration. All the active compounds and the S. baicalensis extract also inhibit the SARS-CoV 3CL^pro, demonstrating their potential as broad-spectrum anti-coronavirus drugs.