Antibacterial sideroxylonals and loxophlebal A from Eucalyptus loxophleba foliage.

Loxophlebal A, a new antibacterial formylated phloroglucinol was isolated from the mother liquor obtained after separation of sideroxylonals from the chloroform-methanol extract of leaves of Eucalyptus loxophleba ssp lissophloia. The structure of loxophlebal A was determined to be 3-desformyl sideroxylonal A by spectroscopic methods including 1D- and 2D-NMR. The stereochemistry of loxophlebal A was determined by chemical correlation with sideroxylonal A. This article also reports an efficient, simple and economic method for large scale isolation of sideroxylonals in a purity of >90% from the leaves of Eucalyptus loxophleba ssp lissophloia.

Structure function analysis of Leishmania sirtuin: an ensemble of in silico and biochemical studies.

Novel anti-leishmanial target LmSir2 has few subtle but prudent structural differences in ligand binding and catalytic domain as compared to its human counterpart. In silico screening and validation followed by in vitro deacetylation and cell killing assays described herein give a proof of concept for development of strategies exploiting such minor differences for screening libraries of small molecules to identify selective inhibitors.

Selective mapping of chemical space for Pseudomonas aeruginosa deacetylase LpxC inhibitory potential.

UDP-3-O-[R-3-hydroxymyristoyl]-GlcNAc deacetylase enzyme of Pseudomonas aeruginosa is an interesting target for development of anti-infective drugs against this gram-negative bacterium. Many segregated studies analyzing the P. aeruginosa UDP-3-O-[R-3-hydroxymyristoyl]-GlcNAc deacetylase and its inhibitors have been reported in the recent past. In the present study, an attempt has been made to integrate this knowledge for the development of an effective multilayer screening approach. Eventually, an extensive chemical space was screened to filter out three potential P. aeruginosa UDP-3-O-[R-3-hydroxymyristoyl]-GlcNAc deacetylase inhibitors.

Evaluation of Pseudomonas aeruginosa deacetylase LpxC inhibitory activity of dual PDE4-TNFalpha inhibitors: a multiscreening approach.

In this study, we have focused on the implication of a multiscreening approach in the evaluation of Pseudomonas aeruginosa deacetylase LpxC inhibitory activity of dual PDE4-TNFalpha inhibitors. A genetic function approximation (GFA) directed quantitative structure-activity relationship (QSAR) model was developed for LpxC inhibition on the basis of reported biological activity (Kline and Andersen, J. Med. Chem. 2002, 45, 3112-3129). Subsequently, reported PDE4-TNFalpha inhibitors (Klienman and Campbell, J. Med. Chem. 1998, 41, 266-270) were screened using the QSAR model. Whereby, the compounds were predicted to have equipotent activity with the most potent compound in reported LpxC inhibitor series. A docking analysis of these compounds carried out on the LpxC homology model corroborated the initial results. The compounds were then validated using surface electronic properties analysis and subjected to an adsorption, distribution, metabolism, excretion, and toxicity filter. Taken together, a multiscreening strategy was used to validate potential leads for LpxC inhibition.

Cluster analysis and two-dimensional quantitative structure-activity relationship (2D-QSAR) of Pseudomonas aeruginosa deacetylase LpxC inhibitors.

Compounds from a wide variety of structural classes inhibit Pseudomonas aeruginosa deacetylase LpxC. However, a single unified understanding of the relationship between the structures and activities of these compounds still eludes the researchers. We report herein, the development of cluster analysis-based 2D-QSAR models for LpxC inhibition. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and genetic function approximation (GFA) were employed for the development of the QSAR model. The conventional 2D-QSAR model derived for the complete set of three-structural classes had unsatisfactory predictability with a correlation coefficient (r(2)) of 0.703 and a cross-validated correlation coefficient (q(2)) of 0.584. Descriptor-based cluster analysis indicated that the three-structural classes of LpxC inhibitors studied belonged to two clusters. Separate QSAR models for these two clusters showed substantially improved predictability with r(2) values of 0.904 and 0.944 and q(2) values of 0.805 and 0.906, respectively. Thus, we expect that compared to the conventional model, our two QSAR models can be better used to preliminarily screen molecules from a diverse chemical space while searching for novel LpxC inhibitors.