Dataset Information

Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways.

ABSTRACT: Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor ?2AR. Markov state models aggregate independent simulations into a single statistical model that is validated by previous computational and experimental results. Moreover, our models provide an atomistic description of the activation of a G-protein-coupled receptor and reveal multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design.

SUBMITTER: Kohlhoff KJ

PROVIDER: S-EPMC3923464 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways.

Kohlhoff Kai J KJ Shukla Diwakar D Lawrenz Morgan M Bowman Gregory R GR Konerding David E DE Belov Dan D Altman Russ B RB Pande Vijay S VS

Nature chemistry 20131215 1

Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β2AR. Markov state model ...[more]

PMID: 24345941

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Project description:BACKGROUND:Bioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis. MAIN TEXT:We present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others. CONCLUSIONS:Keemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes.

Project description:Adenosine receptors and ?-adrenoceptors are G-protein-coupled receptors (GPCRs) that activate intracellular G proteins on binding the agonists adenosine or noradrenaline, respectively. GPCRs have similar structures consisting of seven transmembrane helices that contain well-conserved sequence motifs, indicating that they are probably activated by a common mechanism. Recent structures of ?-adrenoceptors highlight residues in transmembrane region 5 that initially bind specifically to agonists rather than to antagonists, indicating that these residues have an important role in agonist-induced activation of receptors. Here we present two crystal structures of the thermostabilized human adenosine A(2A) receptor (A(2A)R-GL31) bound to its endogenous agonist adenosine and the synthetic agonist NECA. The structures represent an intermediate conformation between the inactive and active states, because they share all the features of GPCRs that are thought to be in a fully activated state, except that the cytoplasmic end of transmembrane helix 6 partially occludes the G-protein-binding site. The adenine substituent of the agonists binds in a similar fashion to the chemically related region of the inverse agonist ZM241385 (ref. 8). Both agonists contain a ribose group, not found in ZM241385, which extends deep into the ligand-binding pocket where it makes polar interactions with conserved residues in H7 (Ser?277(7.42) and His?278(7.43); superscripts refer to Ballesteros-Weinstein numbering) and non-polar interactions with residues in H3. In contrast, the inverse agonist ZM241385 does not interact with any of these residues and comparison with the agonist-bound structures indicates that ZM241385 sterically prevents the conformational change in H5 and therefore it acts as an inverse agonist. Comparison of the agonist-bound structures of A(2A)R with the agonist-bound structures of ?-adrenoceptors indicates that the contraction of the ligand-binding pocket caused by the inward motion of helices 3, 5 and 7 may be a common feature in the activation of all GPCRs.

Dataset Information

Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways.

Publications

Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways.

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