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

We present a method, CamShift, for the rapid and accurate prediction of NMR chemical shifts from protein structures. The calculations performed by CamShift are based on an approximate expression of the chemical shifts in terms of polynomial functions of interatomic distances. Since these functions are very fast to compute and readily differentiable, the CamShift approach can be utilized in standard protein structure calculation protocols.

This paper presents the Dexterity Network (DexNet) 1.0, a dataset of 3D object models and a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust grasp planning. The algorithm uses a MultiArmed Bandit model with correlated rewards to leverage prior grasps and 3D object models in a growing dataset that currently includes over 10,000 unique 3D object models and 2.5 million parallel-jaw grasps. Each grasp includes an estimate of the probability of force closure under uncertainty in object and gripper pose and friction. Dex-Net 1.0 uses Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for 3D object classification, to provide a similarity metric between objects, and the Google Cloud Platform to simultaneously run up to 1,500 virtual cores, reducing experiment runtime by up to three orders of magnitude. Experiments suggest that correlated bandit techniques can use a cloud-based network of object models to significantly reduce the number of samples required for robust grasp planning. We report on system sensitivity to variations in similarity metrics and in uncertainty in pose and friction. Code and updated information is available at http://berkeleyautomation.github.io/dex-net/.

We introduce a procedure to determine the structures of proteins by incorporating NMR chemical shifts as structural restraints in molecular dynamics simulations. In this approach, the chemical shifts are expressed as differentiable functions of the atomic coordinates and used to compute forces to generate trajectories that lead to the reduction of the differences between experimental and calculated chemical shifts. We show that this strategy enables the folding of a set of proteins with representative topologies starting from partially denatured initial conformations without the use of additional experimental information. This method also enables the straightforward combination of chemical shifts with other standard NMR restraints, including those derived from NOE, J-coupling, and residual dipolar coupling measurements. We illustrate this aspect by calculating the structure of a transiently populated excited state conformation from chemical shift and residual dipolar coupling data measured by relaxation dispersion NMR experiments.