Dataset Information

Structural insight to mutation effects uncover a common allosteric site in class C GPCRs.

ABSTRACT:

Motivation

Class C G protein-coupled receptors (GPCRs) regulate important physiological functions and allosteric modulators binding to the transmembrane domain constitute an attractive and, due to a lack of structural insight, a virtually unexplored potential for therapeutics and the food industry. Combining pharmacological site-directed mutagenesis data with the recent class C GPCR experimental structures will provide a foundation for rational design of new therapeutics.

Results

We uncover one common site for both positive and negative modulators with different amino acid layouts that can be utilized to obtain selectivity. Additionally, we show a large potential for structure-based modulator design, especially for four orphan receptors with high similarity to the crystal structures.

Availability and implementation

All collated mutagenesis data is available in the GPCRdb mutation browser at http://gpcrdb.org/mutations/ and can be analyzed online or downloaded in excel format.

Contact

david.gloriam@sund.ku.dk.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Harpsoe K

PROVIDER: S-EPMC5408886 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

Structural insight to mutation effects uncover a common allosteric site in class C GPCRs.

Harpsøe Kasper K Boesgaard Michael W MW Munk Christian C Bräuner-Osborne Hans H Gloriam David E DE

Bioinformatics (Oxford, England) 20170401 8

<h4>Motivation</h4>Class C G protein-coupled receptors (GPCRs) regulate important physiological functions and allosteric modulators binding to the transmembrane domain constitute an attractive and, due to a lack of structural insight, a virtually unexplored potential for therapeutics and the food industry. Combining pharmacological site-directed mutagenesis data with the recent class C GPCR experimental structures will provide a foundation for rational design of new therapeutics.<h4>Results</h4> ...[more]

PMID: 28011766

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Project description:Proteins often have both orthosteric and allosteric binding sites. Endogenous ligands, such as hormones and neurotransmitters, bind to the orthosteric site, while synthetic ligands may bind to orthosteric or allosteric sites, which has become a focal point in drug discovery. Usually, such allosteric modulators bind to a protein noncompetitively with its endogenous ligand or substrate. The growing interest in allosteric modulators has resulted in a substantial increase of these entities and their features such as binding data in chemical libraries and databases. Although this data surge fuels research focused on allosteric modulators, binding data is unfortunately not always clearly indicated as being allosteric or orthosteric. Therefore, allosteric binding data is difficult to retrieve from databases that contain a mixture of allosteric and orthosteric compounds. This decreases model performance when statistical methods, such as machine learning models, are applied. In previous work we generated an allosteric data subset of ChEMBL release 14. In the current study an improved text mining approach is used to retrieve the allosteric and orthosteric binding types from the literature in ChEMBL release 22. Moreover, convolutional deep neural networks were constructed to predict the binding types of compounds for class A G protein-coupled receptors (GPCRs). Temporal split validation showed the model predictiveness with Matthews correlation coefficient (MCC) = 0.54, sensitivity allosteric = 0.54, and sensitivity orthosteric = 0.94. Finally, this study shows that the inclusion of accurate binding types increases binding predictions by including them as descriptor (MCC = 0.27 improved to MCC = 0.34; validated for class A GPCRs, trained on all GPCRs). Although the focus of this study is mainly on class A GPCRs, binding types for all protein classes in ChEMBL were obtained and explored. The data set is included as a supplement to this study, allowing the reader to select the compounds and binding types of interest.

Dataset Information

Structural insight to mutation effects uncover a common allosteric site in class C GPCRs.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Structural insight to mutation effects uncover a common allosteric site in class C GPCRs.

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