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Porous imidazole polymerized ionic liquids (PILs-Im) with fast Li+ diffusion kinetics and numerous molecular pores have been used as anodes in lithium-ion batteries for the first time.

Huanglongmycin (HLM) congeners G-N (7-14) were isolated from Streptomyces sp. CB09001. Among them, 10-12 possesses a tricyclic scaffold with benzene-fused pyran/pyrone, confirmed by X-ray single crystal diffraction analysis of 12. The structure-activity relationship study of 1, 13, and 14 revealed not only the stronger cytotoxicity of 14 against tested cancer cells but also the critical role of the C-7 ethyl group of 14 in its binding to the DNA-topoisomerase I complex.

Three siderophores mirubactins B-D (4-6) were identified as the degradation products of previously isolated mirubactin (1). Their structures were revealed by HR-ESI-MS/MS, NMR analyses, and density functional calculations, among which 4 contains an unusual cyclic amidine functionality. Cyclic voltammetry showed that 5 and 6 have reduced iron complexing capacity. Mirubactin (1) and Fe(III) could also form a stable complex, which may be an ingenious approach to compete for iron acquisition by the producing organisms.

Metals play essential roles in life by coordination with small molecules, proteins, and nucleic acids. Although the coordination of copper ions in many proteins and methanobactins is known, the coordination chemistry of Cu(II) in natural products and their biological functions remain underexplored. Herein, we report the discovery of a Cu(II)-binding natural product, chalkophomycin (CHM, 1), from Streptomyces sp. CB00271, featuring an asymmetric square-coordination system of a bidentate diazeniumdiolate and a conjugated 1H-pyrrole 1-oxide-oxazoline. The structure of 1 may inspire the synthesis of Cu(II) chelators against neurodegenerative diseases or Cu(II)-based antitumor therapeutics.

Huanglongmycin (HLM) congeners G-N (

Organics with the merit of renewability have been viewed as the promising alternative of inorganic electrode materials in lithium-ion batteries, but most of them display inferior performance due to the sluggish ion/electron diffusion and the potential dissolution in aprotic electrolytes. Here, covalent triazine frameworks (CTFs-1), full of vertical pores and layered spaces for Li+ transfer, have been synthesized with p-dicyanobenzene as the monomer by a facile two-step method including a prepolymerization with CF3SO3H as the catalyst and deep polymerization in molten ZnCl2. CTFs-1-400, obtained at the deep polymerization temperature of 400 °C, exhibits the superlithiation property with the specific capacities of 1626 mA h g-1 at 25 °C and 1913 mA h g-1 at 45 °C at 100 mA g-1, indicating the formation of Li6C6/Li6C3N3 in the reduction process. Electrochemical analysis and density functional theory calculation indicate that the ultrahigh capacity is mainly contributed by the capacitance of micropores and the redox capacity of benzene and triazine rings. Moreover, CTFs-1-400 displays the specific capacity of 740 mA h g-1 for 1000 cycles at 1 A g-1 with almost no capacity fading.

There is great interest to develop artificial intelligence-based protein–ligand binding affinity models due to their immense applications in drug discovery. In this paper, PointNet and PointTransformer, two pointwise multi-layer perceptrons have been applied for protein–ligand binding affinity prediction for the first time. Three-dimensional point clouds could be rapidly generated from PDBbind-2016 with 3772 and 11 327 individual point clouds derived from the refined or/and general sets, respectively. These point clouds (the refined or the extended set) were used to train PointNet or PointTransformer, resulting in protein–ligand binding affinity prediction models with Pearson correlation coefficients R = 0.795 or 0.833 from the extended data set, respectively, based on the CASF-2016 benchmark test. The analysis of parameters suggests that the two deep learning models were capable to learn many interactions between proteins and their ligands, and some key atoms for the interactions could be visualized. The protein–ligand interaction features learned by PointTransformer could be further adapted for the XGBoost-based machine learning algorithm, resulting in prediction models with an average Rp of 0.827, which is on par with state-of-the-art machine learning models. These results suggest that the point clouds derived from PDBbind data sets are useful to evaluate the performance of 3D point clouds-centered deep learning algorithms, which could learn atomic features of protein–ligand interactions from natural evolution or medicinal chemistry and thus have wide applications in chemistry and biology.

Metal-organic frameworks (MOFs) with intrinsic pores and redox organic ligands/metallic ions have been viewed as the promising anodes with high capacity in lithium ion batteries (LIBs). However, conventional MOFs faces the disadvantages of poor electron conductivity and weak coordinate bond between organic ligands and central metal ions, inhibiting the application in LIBs. In this study, a conductive MOFs of Ni3(HITP)2 has been fabricated by reacting the strong-field ligand of 2,3,6,7,10,11-hexaiminotriphenylene (HITP) with Ni2+. The as-prepared Ni3(HITP)2 presents the spindle-like shape that are regular arranged by multi-nano fibers. While utilized in LIBs, Ni3(HITP)2 can deliver the reversible capacity of 703 mAh g−1 at 50 mA g−1 with almost no capacity fading. According to the theoretical calculation and XPS results, both amino groups in ligand and Ni2+ ions have participated in the redox reaction during the charge/discharge processes. The superior Li+ storage performance of Ni3(HITP)2 are contributed by the intercalation capacity of the redox groups and capacitance of the molecular pores.