An influenza A hemagglutinin small-molecule fusion inhibitor identified by a new high-throughput fluorescence polarization screen.

Influenza hemagglutinin (HA) glycoprotein is the primary surface antigen targeted by the host immune response and a focus for development of novel vaccines, broadly neutralizing antibodies (bnAbs), and therapeutics. HA enables viral entry into host cells via receptor binding and membrane fusion and is a validated target for drug discovery. However, to date, only a very few bona fide small molecules have been reported against the HA. To identity new antiviral lead candidates against the highly conserved fusion machinery in the HA stem, we synthesized a fluorescence-polarization probe based on a recently described neutralizing cyclic peptide P7 derived from the complementarity-determining region loops of human bnAbs FI6v3 and CR9114 against the HA stem. We then designed a robust binding assay compatible with high-throughput screening to identify molecules with low micromolar to nanomolar affinity to influenza A group 1 HAs. Our simple, low-cost, and efficient in vitro assay was used to screen H1/Puerto Rico/8/1934 (H1/PR8) HA trimer against ∼72,000 compounds. The crystal structure of H1/PR8 HA in complex with our best hit compound F0045(S) confirmed that it binds to pockets in the HA stem similar to bnAbs FI6v3 and CR9114, cyclic peptide P7, and small-molecule inhibitor JNJ4796. F0045 is enantioselective against a panel of group 1 HAs and F0045(S) exhibits in vitro neutralization activity against multiple H1N1 and H5N1 strains. Our assay, compound characterization, and small-molecule candidate should further stimulate the discovery and development of new compounds with unique chemical scaffolds and enhanced influenza antiviral capabilities.

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.