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 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.