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Kohlhoff et al., "Cloud-based simulations on Google Exacycle reveal ligand-modulation of GPCR activation pathways", Nature Chemistry, 01/2014, doi 10.1038/nchem.1821 (2013)
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Simulations can provide tremendous insight into atomistic details of biological mechanisms, but micro- to milliseconds timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative, bringing long-timescale processes within reach of a broader community. We used Google’s Exacycle cloud computing platform to simulate an unprecedented 2 milliseconds of dynamics of the β2 adrenergic receptor (β2AR)—a major drug target G protein-coupled receptor (GPCR). Markov state models aggregating independent simulations into a single statistical model are validated by previous computational and experimental results. Moreover, our models provide the first atomistic description of the activation of a GPCR, revealing multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design.

Provide links to Gromacs trajectory data for the Exacycle cloud computing paper on GPCR dynamics.

This project provides links to the GPCR trajectory data used for the analysis in the paper on cloud-based simulations on Google Exacycle. The data is available for download and can be used freely by anyone.


This project links to the download locations for large trajectory data sets in Gromacs 4.4.3 file format.

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