Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug–target benchmark datasets. However, a notable drawback of a binary representation of interaction data is that missing endpoints for non-interacting drug–target pairs are not differentiated from inactive cases, and that predicted levels of activity depend on pre-defined binarization thresholds. In this paper, we present a method called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions. Additionally, we propose a version of the method called SimBoostQuant which computes a prediction interval in order to assess the confidence of the predicted affinity, thus defining the Applicability Domain metrics explicitly. We evaluate SimBoost and SimBoostQuant on two established drug–target interaction benchmark datasets and one new dataset that we propose to use as a benchmark for read-across cheminformatics applications. We demonstrate that our methods outperform the previously reported models across the studied datasets.

Cationic antimicrobial peptides (CAMPs) are potent therapeutics for drug-resistant bacterial infections. However, the clinical application of CAMPs is hampered by its poor proteolytic stability and hemolytic activity toward eukaryotic cells. Great efforts have been made to design and generate derivatives of CAMPs with improved pharmacological properties. Here, we report a novel stapling protocol, which tethers two ε-amino groups of the lysine residue by the N-alkylation reaction on the hydrophilic face of amphiphilic antimicrobial peptides. A series of lysine-tethered stapled CAMPs were synthesized, employing the antimicrobial peptide OH-CM6 as a model. Biological screening of the stapled CAMPs provided an analogue with strong antimicrobial activity, high proteolytic stability, and low hemolytic activity. This novel stapling approach offers an important chemical tool for developing CAMP-based antibiotics.

In this study, the dihydropteroate synthase of Staphylococcus aureus was obtained, and its recognition mechanisms for 31 sulfonamide drugs were studied. Results showed that their core structure matched well with the binding pocket of paraaminobenzoic acid, and all the sulfonamide side chains were out of the binding pocket. Hydrogen bonds and hydrophobic interactions were the main intermolecular forces, and the key amino acids were Gly171 and Lys203. The binding sites in sulfonamide molecules were mainly around the para-aminobenzenesulfonamide part. This enzyme was used to develop a fluorescence polarization assay for detection of these drugs in chicken muscles. The change trends of half of inhibition concentrations and crossreactivities for the 31 drugs were identical with the receptor−ligand affinities. The limits of detection were in the range of 2.0−38.5 ng/g, and one assay could be finished within several minutes. Therefore, this method could be used for multiscreening of sulfonamide residues in meat samples.

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Antimicrobial peptides (AMPs) have great potentials for developing novel antibiotics against multi‐drug resistant (MDR) bacteria. However, the clinical application of AMPs is limited due to their poor protease stability and high hemolytic toxicity. Various strategies have been widely explored to improve the pharmacological properties of natural or artificial antimicrobial peptides, including D‐ or non‐natural amino acid residue replacement, backbone modification, cyclization, PEGlytion, and lipidation. Among others, peptide cyclization, which has been widely applied to enhance the biostability and target selectivity of bioactive peptide, is a very appealing and promising strategy for developing novel antibiotics based on AMPs. Herein, we summarize the current strategies for synthesizing cyclic antimicrobial peptides and the resulting influence of peptide cyclization on the biological activities.

Cationic antimicrobial peptides (CAMPs) are promising for treatment of multidrug-resistant (MDR) bacteria-caused infections. However, clinical application of CAMPs has been hampered mostly due to their poor proteolytic stability and hemolytic toxicity. Recently, lysine-stapled CAMPs developed by us had been proved to increase peptide stability in vitro without induction of hemolysis. Herein, the applicability of the lysine stapling strategy was further explored by using five natural or artificial CAMPs as model peptides. Lysine stapling screening was implemented to provide 13 cyclic analogues in total. Biological screening of these cyclic analogues showed that CAMPs with a better amphiphilic structure were inclined to exhibit improved antimicrobial activity, protease stability, and biocompatibility after lysine-stapling. One of the stapled analogues of BF15-a1 was found to have extended half-life in plasma, enhanced antimicrobial activity against clinically isolated MDR ESKAPE pathogens, and remained highly effective in combating MRSA infection in a mouse model.

Antimicrobial peptides (AMPs) have attracted great attention as next generation antibiotics for the treatment of multidrug-resistant (MDR) bacterial infections. Poor proteolytic stability has however undermined clinical applications of AMPs. A novel peptide cyclization approach is described to enhance the in vivo antibacterial activity of AMPs. Bicyclic antimicrobial peptides were synthesized by cross-linking the ε-amino groups of three lysine residues with a 1,3,5-trimethylene benzene spacer. In a proof of principal study, four bicyclic peptides were synthesized from the cationic AMP OH-CM6. One bicyclic peptide retained strong antimicrobial activity and low toxicity but exhibited a prolonged half-life in serum. Antibacterial activity was consequently improved in vivo without renal or hepato-toxicity. The novel peptide cyclization approach represents an important tool for enhancing AMP proteolytic stability for improved treatment of bacterial infection.

In order to improve water quality retrievals of multi-spectral image accurately, this paper puts forward a method for water quality remote retrieva based on support vector regression with parameters optimized by genetic algorithm. The method uses SPOT-5A data and the water quality field data, chose four representative water quality parameters, support vector regression are trained and tested, the parameters of support vector regression are optimized by genetic algorithms. The result of experiment shows that the method has more accuracy than the routine method. It provides a new approach for remote sensing monitoring of environment.