Dataset Information

Crystal structures of a GABAA-receptor chimera reveal new endogenous neurosteroid-binding sites.

ABSTRACT: ?-Aminobutyric acid receptors (GABAARs) are vital for controlling excitability in the brain. This is emphasized by the numerous neuropsychiatric disorders that result from receptor dysfunction. A critical component of most native GABAARs is the ? subunit. Its transmembrane domain is the target for many modulators, including endogenous brain neurosteroids that impact anxiety, stress and depression, and for therapeutic drugs, such as general anesthetics. Understanding the basis for the modulation of GABAAR function requires high-resolution structures. Here we present the first atomic structures of a GABAAR chimera at 2.8-Å resolution, including those bound with potentiating and inhibitory neurosteroids. These structures define new allosteric binding sites for these modulators that are associated with the ?-subunit transmembrane domain. Our findings will enable the exploitation of neurosteroids for therapeutic drug design to regulate GABAARs in neurological disorders.

SUBMITTER: Laverty D

PROVIDER: S-EPMC6853794 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Crystal structures of a GABAA-receptor chimera reveal new endogenous neurosteroid-binding sites.

Laverty Duncan D Thomas Philip P Field Martin M Andersen Ole J OJ Gold Matthew G MG Biggin Philip C PC Gielen Marc M Smart Trevor G TG

Nature structural & molecular biology 20171002 11

γ-Aminobutyric acid receptors (GABAARs) are vital for controlling excitability in the brain. This is emphasized by the numerous neuropsychiatric disorders that result from receptor dysfunction. A critical component of most native GABAARs is the α subunit. Its transmembrane domain is the target for many modulators, including endogenous brain neurosteroids that impact anxiety, stress and depression, and for therapeutic drugs, such as general anesthetics. Understanding the bas ...[more]

PMID: 28967882

Similar Datasets

Project description:BackgroundRecognizing similarities and deriving relationships among protein molecules is a fundamental requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.ResultsHere we present a new algorithm PocketMatch for comparison of binding sites in a frame invariant manner. Each binding site is represented by 90 lists of sorted distances capturing shape and chemical nature of the site. The sorted arrays are then aligned using an incremental alignment method and scored to obtain PMScores for pairs of sites. A comprehensive sensitivity analysis and an extensive validation of the algorithm have been carried out. A comparison with other site matching algorithms is also presented. Perturbation studies where the geometry of a given site was retained but the residue types were changed randomly, indicated that chance similarities were virtually non-existent. Our analysis also demonstrates that shape information alone is insufficient to discriminate between diverse binding sites, unless combined with chemical nature of amino acids.ConclusionA new algorithm has been developed to compare binding sites in accurate, efficient and high-throughput manner. Though the representation used is conceptually simplistic, we demonstrate that along with the new alignment strategy used, it is sufficient to enable binding comparison with high sensitivity. Novel methodology has also been presented for validating the algorithm for accuracy and sensitivity with respect to geometry and chemical nature of the site. The method is also fast and takes about 1/250th second for one comparison on a single processor. A parallel version on BlueGene has also been implemented.

Dataset Information

Crystal structures of a GABAA-receptor chimera reveal new endogenous neurosteroid-binding sites.

Publications

Crystal structures of a GABA<sub>A</sub>-receptor chimera reveal new endogenous neurosteroid-binding sites.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets