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 DC50 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).

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In this paper, we report a unique type of core-shell crystalline material that combines an inorganic zeolitic cage structure with a macrocyclic host arrangement and that can remove trace levels of iodine from water effectively. These unique assemblies are made up of an inorganic Archimedean truncatedhexahedron (tcu) polyhedron in the kernel which possesses six calixarene-like shell cavities. The cages have good adaptability to guests and can be assembled into a series of supramolecular structures in the crystalline state with different lattice pore shapes. Due to the unique core-shell porous structures, the compounds are not only stable in organic solvents but also in water. The characteristics of the cages enable rapid iodine capture from low concentration aqueous I2/KI solutions (down to 4 ppm concentration). We have studied the detailed process and mechanism of iodine capture and aggregation at the molecular level. The facile synthesis, considerable adsorption capacity, recyclability, and β- and γ-radiation resistance of the cages should make these materials suitable for the extraction of iodine from aqueous effluent streams (most obviously, radioactive iodide produced by atomic power generation).

Subarachnoid hemorrhage (SAH) is a severe type of stroke featuring exceptionally high rate of morbidity and mortality due to the lack of effective management. Ferroptosis can be defined as a novel iron-dependent programmed cell death in contrast to classical apoptosis and necrosis. Astragaloside IV (AS-IV) is an active ingredient extracted from Astragalus membranaceus with established therapeutic effect on CNS diseases. However, the exact role of ferroptosis in Astragaloside IV-mediated neuroprotection after SAH is yet to be demonstrated. In the present study, the SAH model of SD male rats with endovascular perforation was used to gauge the neuroprotective effect of AS-IV on SAH-induced early brain injury (EBI) and to clarify the potential molecular mechanism. We found that the induction of SAH reduced the levels of SLC7A11 and glutathione peroxidase 4 (GPX4) in the brain, exacerbated iron accumulation, enhanced lipid reactive oxygen species (ROS) level, and stimulated neuronal ferroptosis. However, the administration of AS-IV and the ferroptosis inhibitor Ferrostatin-1 (Fer-1) enhanced the antioxidant capacity after SAH and suppressed the accumulation of lipid peroxides. Meanwhile, AS-IV triggered Nrf2/HO-1 signaling pathway and alleviated ferroptosis due to the induction of SAH. The Nrf2 inhibitor ML385 blocked the beneficial effects of neuroprotection. These results consistently suggest that ferroptosis is profoundly implicated in facilitating EBI in SAH, and that AS-IV thwarts the process of ferroptosis in SAH by activating Nrf2/HO-1 pathway.