Biomedical subjects

Emanuela Gancia

Publications and source records attributed to Emanuela Gancia.

4 recordsLinked to original sources

PubMed2006

8-Fluoroimidazo[1,2-a]pyridine: synthesis, physicochemical properties and evaluation as a bioisosteric replacement for imidazo[1,2-a]pyrimidine in an allosteric modulator ligand of the GABA A receptor.

8-Fluoroimidazo[1,2-a]pyridine has been established as a physicochemical mimic of imidazo[1,2-a]pyrimidine, using both in silico and traditional techniques. Furthermore, a novel synthesis of a 3,7-disubstituted-8-fluoroimidazopyridine 3 has been developed and the utility of the physicochemical mimicry has been demonstrated in an in vitro system. Here, the 8-fluoroimidazopyridine ring contained in ligand 3 acts as a bioisosteric replacement for imidazopyrimidine in the GABA(A) receptor modulator 2.

Allosteric Regulation↗

PubMed2003

Mycobacterium tuberculosis chaperonin 10 is secreted in the macrophage phagosome: is secretion due to dissociation and adoption of a partially helical structure at the membrane?

To confirm that Mycobacterium tuberculosis chaperonin 10 (Cpn10) is secreted outside the live bacillus, infected macrophages were examined by electron microscopy. This revealed that the mycobacterial protein accumulates both in the wall of the bacterium and in the matrix of the phagosomes in which ingested mycobacteria survive within infected macrophages. To understand the structural implications underlying this secretion, a structural study of M. tuberculosis Cpn10 was performed under conditions that are generally believed to mimic the membrane environment. It was found that in buffer-organic solvent mixtures, the mycobacterial protein forms two main species, namely, a partially helical monomer that prevails in dilute solutions at room temperature and a dimer that folds into a beta-sheet-dominated structure and prevails in either concentrated protein solutions at room temperature or in dilute solutions at low temperature. A partially helical monomer was also found and was completely associated with negatively charged detergents in a micelle-bound state. Remarkably, zwitterionic lipids had no effect on the protein structure. By using N- and C-truncated forms of the protein, the C- and N-terminal sequences were identified as possessing an amphiphilic helical character and as selectively associating with acidic detergent micelles. When the study was extended to other chaperonins, it was found that human Cpn10 is also monomeric and partially helical in dilute organic solvent-buffer mixtures. In contrast, Escherichia coli Cpn10 is mostly dimeric and predominately beta-sheet in both dilute and concentrated solutions. Interestingly, human Cpn10 also crosses biological membranes, whereas the E. coli homologue is strictly cytosolic. These results suggest that dissociation to partially helical monomers and interaction with acidic lipids may be two important steps in the mechanism of secretion of M. tuberculosis Cpn10 to the external environment.

Animals↗

PubMedDate not supplied

Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks.

A series of neural networks has been trained, using consensus methods, to recognize compounds that act at biological targets belonging to specific gene families. The MDDR database was used to provide compounds targeted against gene families and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. In each case, the networks identified over 80% of the compounds targeting a gene family. The technique was applied to purchasing compounds from external suppliers, and results from screening against one gene family demonstrated impressive abilities to predict the activity of the majority of known hit compounds.

Databases, Factual↗

PubMedDate not supplied

A consensus neural network-based technique for discriminating soluble and poorly soluble compounds.

BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.

Journal Article↗