No abstract

No abstract

Virtual screening has identified rhodanine containing carboxylic acids as new inhibitors of histone acetyltransferases.

ADP-ribosyltransferases (ADP-RTs) use NAD(+) to transfer an ADP-ribosyl group to target proteins. Although some ADP-RTs are bacterial toxins only few inhibitors are known. Here we present the development of fluorescence-based assays and a focussed library screening using kinase inhibitors as a new approach towards inhibitors of ADP-RTs. Different screening setups were established using surrogate small molecule substrates or the quantitation of the cofactor NAD(+). Proof-of-principle screening experiments were performed using a kinase inhibitor library in order to target the NAD(+) binding pockets. This led to the discovery of structurally different lead inhibitors for the mono-ADP-ribosyltransferases Mosquitocidal toxin (MTX) from Bacillus sphaericus SSII-1, C3bot toxin from Clostridium botulinum and CDTa from Clostridium difficile. The interaction of the inhibitors with the toxin proteins was analyzed by means of docking and binding free energy calculations. Binding at the nicotinamide subpocket, which shows a significant difference in the three enzymes, is used to explain the selectivity of the identified inhibitors and offers an opportunity for further development of potent and selective inhibitors.

Abstract Our previous work was focused on the fundamental physical and chemical concepts behind “drug-likeness” and “natural product (NP)-likeness”. Herein, we discuss further details on the concepts of “drug-likeness”, “lead-likeness” and “NP-likeness”. The discussion will first focus on NPs as drugs, then a discussion of previous studies in which the complexities of the scaffolds and chemical space of naturally occurring compounds have been compared with synthetic, semisynthetic compounds and the Food and Drug Administration-approved drugs. This is followed by guiding principles for designing “drug-like” natural product libraries for lead compound discovery purposes. In addition, we present a tool for measuring “NP-likeness” of compounds and a brief presentation of machine-learning approaches. A binary quantitative structure–activity relationship for classifying drugs from nondrugs and natural compounds from nonnatural ones is also described. While the studies add to the plethora of recently published works on the “drug-likeness” of NPs, it no doubt increases our understanding of the physicochemical properties that make NPs fall within the ranges associated with “drug-like” molecules.