Allosteric inhibition of the neuropeptidase neurolysin.

Neuropeptidases specialize in the hydrolysis of the small bioactive peptides that play a variety of signaling roles in the nervous and endocrine systems. One neuropeptidase, neurolysin, helps control the levels of the dopaminergic circuit modulator neurotensin and is a member of a fold group that includes the antihypertensive target angiotensin converting enzyme. We report the discovery of a potent inhibitor that, unexpectedly, binds away from the enzyme catalytic site. The location of the bound inhibitor suggests it disrupts activity by preventing a hinge-like motion associated with substrate binding and catalysis. In support of this model, the inhibition kinetics are mixed, with both noncompetitive and competitive components, and fluorescence polarization shows directly that the inhibitor reverses a substrate-associated conformational change. This new type of inhibition may have widespread utility in targeting neuropeptidases.

Predicting the points of interaction of small molecules in the NF-κB pathway.

BACKGROUND: The similarity property principle has been used extensively in drug discovery to identify small compounds that interact with specific drug targets. Here we show it can be applied to identify the interactions of small molecules within the NF-κB signalling pathway. RESULTS: Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis. CONCLUSIONS: The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway. The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis.

'Metabolite-likeness' as a criterion in the design and selection of pharmaceutical drug libraries.

Present drug screening libraries are constrained by biophysical properties that predict desirable pharmacokinetics and structural descriptors of 'drug-likeness' or 'lead-likeness'. Recent surveys, however, indicate that to enter cells most drugs require solute carriers that normally transport the naturally occurring intermediary metabolites and many drugs are likely to interact similarly. The existence of increasingly comprehensive summaries of the human metabolome allows the assessment of the concept of 'metabolite-likeness'. We compare the similarity of known drugs and library compounds to naturally occurring metabolites (endogenites) using relevant cheminformatics molecular descriptor spaces in which known drugs are more akin to such endogenites than are most library compounds.

Assessment of additive/nonadditive effects in structure-activity relationships: implications for iterative drug design.

Free-Wilson (FW) analysis is common practice in medicinal chemistry and is based on the assumption that the contributions to activity made by substituents at different substitution positions are additive. We analyze eight near complete combinatorial libraries assayed on several different biological response(s) (GPCR, ion channel, kinase and P450 targets) and show that only half-exhibit clear additive behavior, which leads us to question the concept of additivity that is widely taken for granted in drug discovery. Next, we report a series of retrospective experiments in which subsets are extracted from the libraries for FW analysis to determine the minimum attributes (size, distribution of substituents, and activity range) necessary to reach the same conclusion about additive/nonadditive effects. These attributes can provide guidelines on when it is appropriate to apply FW analysis as well as for library design, and they also have important implications for further steps in iterative drug design.

A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP.

Three commercially available pharmacophore generation programs, Catalyst/HipHop, DISCO and GASP, were compared on their ability to generate known pharmacophores deduced from protein-ligand complexes extracted from the Protein Data Bank. Five different protein families were included Thrombin, Cyclin Dependent Kinase 2, Dihydrofolate Reductase, HIV Reverse Transcriptase and Thermolysin. Target pharmacophores were defined through visual analysis of the data sets. The pharmacophore models produced were evaluated qualitatively through visual inspection and according to their ability to generate the target pharmacophores. Our results show that GASP and Catalyst outperformed DISCO at reproducing the five target pharmacophores.