Cloud-based simulations on Google Exacycle reveal ligand-modulation of GPCR activation pathways
By Kai J. Kohlhoff, D. Shukla, Morgan Lawrenz, Gregory R. Bowman, David E. Konerding, Dan Belov, Russ B. Altman, Vijay S. Pande. 2013
405 citations recorded by the source corpus.
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 2 milliseconds of dynamics of the β2 adrenergic receptor — a major drug target G protein-coupled receptor (GPCR). 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 GPCR, revealing multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design
Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy
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Citations in the corpus
- Control functionals for Monte Carlo integration (2014, background): “Xis expensive to simulate, or (ii) f is expensive to evaluate, in each case relative to the required estimator precision. Both situations are prevalent in scientic and engineering applications (e.g. Kohlho et al., 2014; Higdon et al., 2015). When working with complex models, root-n convergence can b”
- Multiensemble Markov models of molecular thermodynamics and kinetics (2016, neutral): “Si μkðxÞdx, [5]”
- Exploring the free-energy landscape of GPCR activation (2018, background): “Indeed, molecular dynamics (MD) simulation studies of GPCRs have provided interesting insights on the ligand binding and allosteric activation processes (13, 14).”