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

Nature Chemistry volume 6, pages 1521 (2014) | Download Citation

  • A Corrigendum to this article was published on 20 August 2015

This article has been updated


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.

  • Compound C26H37NO4


  • Compound C26H31N3O4


  • Compound C27H35NO4


  • Compound C24H32ClN5O3


  • Compound C25H31N5O3


  • Compound C27H34N2O3


  • Compound C29H30ClN5O5


  • Compound C28H32N4O5


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Change history

  • 24 July 2015

    In the version of this Article originally published, Figure 4 displayed incorrectly drawn chemical structures for five of the ligands. The correct structures were, however, used in the calculations. The hemiaminal group previously depicted in compounds 24 should have been a β-amino alcohol, compound 7 contained an extra benzylic carbon and compound 8 had an extra ring. The corresponding PubChem CID numbers for the correct ligands are as follows. Agonists: 1, 19044758; 2, 44216210; 3, 44209282; 4, 44213610. Antagonists: 5, 15020513; 6, 19823514; 7, 44209768; 8, 44209764. These drawing errors have now been corrected in the online versions of this Article. Additionally, the 'Inverse agonist' label at the top of Fig. 4b has been changed to 'Antagonist' as this was the original designation for this set of the GPCR ligand database used for docking (E. A. Gatica and C. N. Cavasotto, J. Chem. Inf. Model. 52, 1–6; 2012). Some ligands, particularly carazolol used in this study, may have inverse agonist activity.  For all calculations, functional groups were protonated according to pH = 7. Stereochemistry is not depicted in the figure because stereoisomer activity for these compounds has not been elucidated. The structures in Figure 4 are each representative of many ligands that define a 3D chemotype and share a similar binding pose in protein conformations with similar progress scores. Stereoisomers were enumerated for up to four chiral centers and docked. The isomer with the highest score, or approximated binding affinity, was selected for a given protein conformation. Different protein conformations score isomers differently, and protein conformations with the same progress score may select different isomers of the same compound. Further experiments on the known agonist and antagonist ligands would be needed in order to determine the activities of stereoisomers, as has been done for albuterol and fenoterol (R. Seifert and S. Dove, Mol. Pharmacol. 75,13–18; 2009).


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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.

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Author notes

    • Kai J. Kohlhoff
    • , Diwakar Shukla
    •  & Morgan Lawrenz

    These authors contributed equally


  1. Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, California 94305, USA

    • Kai J. Kohlhoff
    • , Diwakar Shukla
    •  & Russ B. Altman
  2. Department of Chemistry, Stanford University, 318 Campus Drive, Stanford, California 94305, USA

    • Diwakar Shukla
    • , Morgan Lawrenz
    • , Gregory R. Bowman
    •  & Vijay S. Pande
  3. Department of Genetics, Stanford University, 318 Campus Drive, Stanford, California 94305, USA

    • Russ B. Altman
  4. Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA

    • Kai J. Kohlhoff
    • , David E. Konerding
    •  & Dan Belov


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K.J.K., D.S. and M.L. contributed equally to this work. V.S.P., R.B.A, D.E.K. and D.B. conceived, and V.S.P. and R.B.A. supervised the project. K.J.K. and D.E.K. developed the platform for running MD simulations with Gromacs on Google Exacycle. K.J.K. set up the simulation systems. G.R.B helped with initial analysis. K.J.K. performed simulations on Google Exacycle and processed data on Google's production infrastructure. K.J.K. and G.R.B. performed preliminary simulations on Folding@home. D.S. and M.L. performed additional simulations. D.S., M.L and K.J.K. analysed the data and built MSMs. M.L. performed small-molecule docking calculations. D.S., M.L. and K.J.K. co-wrote the manuscript with inputs from G.R.B, R.B.A and V.S.P. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Kai J. Kohlhoff or Russ B. Altman or Vijay S. Pande.

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