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 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.
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We are grateful to M. Stumpe for his contributions to setting up the initial molecular systems and P. Kasson and J. Hellerstein for helpful advice and support. This work was funded in part by a 450M CPU core-hour donation by Google Inc. through the Exacycle eScience program, the Simbios NIH National Center on Biocomputing through the NIH Roadmap for Medical Research Grant U54 GM07297 and a Stanford School of Medicine Dean's Fellowship (K.J.K.). We also thank the users of the Folding@home distributed-computing project for donating compute time for some preliminary simulations that ensured a stable production run. Additional computations for docking and chemotype clustering were performed on the Blue Waters supercomputer at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.
The authors declare no competing financial interests.
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Kohlhoff, K., Shukla, D., Lawrenz, M. et al. Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nature Chem 6, 15–21 (2014) doi:10.1038/nchem.1821
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