Molecular modeling of mono- and bis-quaternary ammonium salts as ligands at the alpha4beta2 nicotinic acetylcholine receptor subtype using nonlinear techniques

QSAR study on maximal inhibition (Imax) of quaternary ammonium antagonists for S-(-)-nicotine-evoked dopamine release from dopaminergic nerve terminals in rat striatum

Maximal inhibition (I(max)) of the agonist effect is an important pharmacological property of inhibitors that interact with multiple receptor subtypes that are activated by the same agonist and which elicit the same functional response. This report represents the first QSAR study on a set of 66 mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating nicotine-evoked dopamine release, conducted using multi-linear regression (MLR) and neural network (NN) analysis with the maximal inhibition (I(max)) values of the antagonists as target values. The statistical results for the generated MLR model were: r(2)=0.89, rmsd=9.01, q(2)=0.83 and loormsd=11.1; the statistical results for the generated NN model were: r(2)=0.89, rmsd=8.98, q(2)=0.83 and loormsd=11.2. The maximal inhibition values of the compounds exhibited a good correlation with the predictions made by the QSAR models developed, which provide a basis for rationalizing selection of compounds for synthesis in the discovery of effective and selective second generation inhibitors of nAChRs mediating nicotine-evoked dopamine release.

Synthesis, anti-fungal activity evaluation and QSAR studies on podophyllotoxin derivatives

The anti-fungal and cytotoxic activites of podophyllotoxin and seven C-4 substituted podophyllotoxin ester derivatives, viz: trans-cinnamyl, cis-cinnamyl, o-methoxy cinnamyl, dimethyl acrylyl, p-methoxy phenyl acetyl, 3,4-dimethoxy phenyl acetyl and 2,5-dimethoxy phenyl acetyl esters were evaluated on four fungi, viz: Macrophomina phaseolina, Fusarium oxysporum, Myrrothecium verrucarria and Asperigillus candidus, The podophyllotoxin derivatives were synthesised and their structures were elucidated. Quantitative structure activity relationships were developed between the activity of these compounds against the four fungi and molecular descriptors. The linear regression models developed had one to two descriptors. For all the cases the r 2 was in the range of 0.73 to 0.96, indicating good data fit and q 2 was in the range of 0.60 to 0.68, indicating that the predictive capabilities of the models were acceptable. Solvent accessible surface area (namely the partial positive solvent-accessible surface area), A log P, highest occupied molecular orbital and conformational energy were identified as important descriptors.

Central Nicotinic Receptors: Structure, Function, Ligands, and Therapeutic Potential

The growing interest in nicotinic receptors, because of their wide expression in neuronal and non-neuronal tissues and their involvement in several important CNS pathologies, has stimulated the synthesis of a high number of ligands able to modulate their function. These membrane proteins appear to be highly heterogeneous, and still only incomplete information is available on their structure, subunit composition, and stoichiometry. This is due to the lack of selective ligands to study the role of nAChR under physiological or pathological conditions; so far, only compounds showing selectivity between alpha4beta2 and alpha7 receptors have been obtained. The nicotinic receptor ligands have been designed starting from lead compounds from natural sources such as nicotine, cytisine, or epibatidine, and, more recently, through the high-throughput screening of chemical libraries. This review focuses on the structure of the new agonists, antagonists, and allosteric ligands of nicotinic receptors, it highlights the current knowledge on the binding site models as a molecular modeling approach to design new compounds, and it discusses the nAChR modulators which have entered clinical trials.

Conformational analysis of lipid molecules by self-organizing maps

The authors have studied the use of the self-organizing map (SOM) in the analysis of lipid conformations produced by atomic-scale molecular dynamics simulations. First, focusing on the methodological aspects, they have systematically studied how the SOM can be employed in the analysis of lipid conformations in a controlled and reliable fashion. For this purpose, they have used a previously reported 50 ns atomistic molecular dynamics simulation of a 1-palmitoyl-2-linoeayl-sn-glycero-3-phosphatidylcholine (PLPC) lipid bilayer and analyzed separately the conformations of the headgroup and the glycerol regions, as well as the diunsaturated fatty acid chain. They have elucidated the effect of training parameters on the quality of the results, as well as the effect of the size of the SOM. It turns out that the main conformational states of each region in the molecule are easily distinguished together with a variety of other typical structural features. As a second topic, the authors applied the SOM to the PLPC data to demonstrate how it can be used in the analysis that goes beyond the standard methods commonly used to study the structure and dynamics of lipid membranes. Overall, the results suggest that the SOM method provides a relatively simple and robust tool for quickly gaining a qualitative understanding of the most important features of the conformations of the system, without a priori knowledge. It seems plausible that the insight given by the SOM could be applied to a variety of biomolecular systems and the design of coarse-grained models for these systems.

QSAR Studies on Chalcones and Flavonoids as Anti-tuberculosis Agents Using Genetic Function Approximation (GFA) Method

Design of compounds having good anti-tubercular activity is gaining much importance in the field of tuberculosis research due to reemergence of antibiotic resistance strains. In this paper quantitative structure activity relationships (QSAR) were developed on chalcones, chalcone-like compounds, flavones and flavanones to understand the relationship between biological activity and structural features. Genetic function approximation (GFA) method was used to identify the descriptors that would lead to good regression equations. The best molecular descriptors identified were Jurs descriptors (Jurs charged partial surface area), hydrogen bond donor, principal moment of inertia, molecular energy, dipole magnetic, molecular area, absorption, distribution, metabolism and excretion (ADME) properties and Chi indices (Kier & Hall chi connectivity indices). Excellent statistically significant models were developed by this approach (r(2)=0.8-0.97) for the four groups of compounds. The cross validated r(2) (XV r(2)) which is an indication of the predictive capability of the model for all the cases was also very good (=0.79-0.94).