Computational drug discovery provides an efficient tool for helping large-scale lead molecule screening. One of the major tasks of lead discovery is identifying molecules with promising binding affinities toward a target, a protein in general. The accuracies of current scoring functions that are used to predict the binding affinity are not satisfactory enough. Thus, machine learning or deep learning based methods have been developed recently to improve the scoring functions. In this study, a deep convolutional neural network model (called OnionNet) is introduced; its features are based on rotation-free element-pair-specific contacts between ligands and protein atoms, and the contacts are further grouped into different distance ranges to cover both the local and nonlocal interaction information between the ligand and the protein. The prediction power of the model is evaluated and compared with other scoring functions using the comparative assessment of scoring functions (CASF-2013) benchmark and the v2016 core set of the PDBbind database. The robustness of the model is further explored by predicting the binding affinities of the complexes generated from docking simulations instead of experimentally determined PDB structures.

Synthetic strategies that enable rapid construction of covalent organic nanotubes with an angstrom‐scale tubular pore remain scarcely reported. Reported here is a remarkably simple and mild one‐pot polymerization protocol, employing POCl3 as the polymerization agent. This protocol efficiently generates polypyridine amide foldamer‐based covalent organic nanotubes with a 2.8 nm length at a yield of 50 %. Trapping single‐file water chains in the 2.8 Å tubular cavity, rich in hydrogen‐bond donors and acceptors, these tubular polypyridine ensembles rapidly and selectively transport water at a rate of 1.6×109 H2O⋅S−1⋅channel−1 and protons at a speed as fast as gramicidin A, with a high rejection of ions.

In this study, we show that berberine chloride (BBR) has antimicrobial activities against all 43 tested strains of Staphylococcus aureus, an important human and animal pathogen. However, the response mechanisms of S. aureus to BBR are still poorly understood. Affymetrix GeneChips were used to determine the global transcription of S. aureus triggered by treatment with subinhibitory concentrations of BBR. 468 genes were up-regulated and 262 genes were down-regulated upon exposure to BBR. There was elevated transcription of various transporter genes, including genes involved in multidrug resistance, members of the multidrug and toxin extrusion family, the ferrous iron transporter, the amino acid transporter, the Na(+)/H(+) antiporter, and the potassium cation transporter. Measurements of active transport were used to demonstrate a phenotypic correlation between efflux transporter overexpression and inhibition of BBR uptake. Furthermore, BBR induced the expression of urease genes, sortase enzyme, and iron-regulated surface determinant genes, but repressed transcription of a gene encoding arylamine N-acetyltransferase activity (N315-SA2490). To our knowledge, this is the first analysis of a genome-wide transcription profile of S. aureus cells in response to BBR treatment. These results will pave the way to exploring the mechanisms of BBR against S. aureus.

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