Biomedical subjects

Rameshwar U Kadam

Publications and source records attributed to Rameshwar U Kadam.

3 recordsLinked to original sources

PubMed2006

Pharmacophoric features of Pseudomonas aeruginosa deacetylase LpxC inhibitors: an electronic and structural analysis.

Various electronic properties of structurally diverse synthetic LpxC inhibitors containing oxazoline, aroylserine and thiazoline rings were calculated and correlated with biological activity. These electronic features include the magnitude and locations of 3-dimensional molecular electrostatic potentials, hydrogen bond acceptor/donor density, lowest unoccupied molecular orbital, and highest occupied molecular orbital. Strong correlation of these stereo-electronic properties with LpxC inhibitory potency reveals the potential pharmacophoric features of specific LpxC inhibitors. Thus, these pharmacophoric features of LpxC inhibitors based on electronic and surface analysis could be successfully exploited for designing more potent LpxC inhibitors.

Amidohydrolases↗

PubMed2006

Comparative protein modeling and surface analysis of Leishmania sirtuin: A potential target for antileishmanial drug discovery.

Homology model of Leishmania SIR2 shed new light on the ligand binding features of this enzyme. The molecular electrostatic potentials (MESP), the cavity depth analysis, and LmSIR2-hSIRT2 models' superposition suggested that the nicotinamide binding catalytic domain has several minor but potentially important structural differences. These differences could be exploited for designing antileishmanial compounds.

Amino Acid Sequence↗

PubMed2006

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.

Amidohydrolases↗