GABAA a3

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Abstract

Selective modulators of GABAA α3 (Gamma Amino Butyric Acid α3) receptor are known to alleviate the side effects associated with non specific modulators. A follow up study was undertaken on a series of functionally selective phthalazines with an ideological credo of identifying more potent isofunctional chemotypes. A bioisosteric database enumerated using combichem approach endorsed mining in a lead like chemical space. Primary screening of the massive library was undertaken using “Miscreen” toolkit, which uses sophisticated bayesian statistics for calculating bioactivity score. The resulting subset thus obtained was mined using a novel proteo-chemometric method that integrates molecular docking and QSAR formalism termed CoIFA (Comparative Interaction Fingerprint Analysis). CoIFA encodes protein- ligand interaction terms as propensity values based on a statistical inference to construct categorical QSAR models that assist in decision making during virtual screening. In the absence of an experimentally resolved structure of GABAA α3 receptor, standard comparative modeling techniques were employed to construct a homology model of GABAA α3 receptor. A typical docking study was then carried out on the modeled structure and interaction fingerprints generated based on the docked binding mode, were used to derive propensity values for the interacting atom pairs that served as pseudo energy variables to generate CoIFA model. The classification accuracy of the CoIFA model was validated using different metrics derived from a confusion matrix. Further predictive lead mining was carried out using a consensus 2D QSAR approach, which offers a better predictive protocol compared to the arbitrary choice of a single QSAR model. The predictive ability of the generated model was validated using different statistical metrics and similarity based coverage estimation was carried out to define applicability boundaries. Few analogs designed using the concept of bioisosterism were found to be promising and could be considered for synthesis and subsequent screening.

Abbreviations

CoIFA – Comparative Interaction Fingerprint Analysis,

GABAA – Gamma Amino Butyric Acid type

A, HTS – High Throughput Screening,

KNN- K-Nearest Neighbor,

GOLD -Genetic optimization in ligand Docking,

RD-QSAR- Receptor Dependent QSAR.

Introduction

Gamma Amino Butyric Acid type A (GABAA) ionotropic receptor is the major inhibitory neuronal receptor of the mammalian brain conferring fast synaptic inhibition1.The physiological role exerted by GABAA in regulating brain excitability and its pharmacological significance as a valuable drug target for many neuronal disorders has surged an renewed interest for a cohesive understanding of its structure and function. The heterogeneous nature of GABAA receptor and its low abundance (pmol/mg of protein), coupled with the inherent difficulties associated in isolation and purification of integral membrane proteins have precluded structural investigations on GABAA receptor2,3. Purification, cloning and sequencing of the GABAA receptor and its composite subunits have allowed identifying 21 subunits arranged within 8 families (α1-6, β1-4, γ1-4, 1δ, 1ε, 1π, 1θ and ρ1-3)4. Given the heterogeneity of GABAA receptor, the pharmacological significance of identifying subtype selective modulators is increasingly being recognized5,6,7. Of the numerous structural classes of drugs that have been shown to bind to the BZ (benzodiazepine) site of GABAA receptor,

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