Global hybrid (electron fluid, kinetic ions) and fully kinetic simulations of the magnetosphere have been used to show surprising interconnection between shocks, turbulence, and magnetic reconnection. In particular, collisionless shocks with their reflected ions that can get upstream before retransmission can generate previously unforeseen phenomena in the post shocked flows: (i) formation of reconnecting current sheets and magnetic islands with sizes up to tens of ion inertial length. (ii) Generation of large scale low frequency electromagnetic waves that are compressed and amplified as they cross the shock. These "wavefronts" maintain their integrity for tens of ion cyclotron times but eventually disrupt and dissipate their energy. (iii) Rippling of the shock front, which can in turn lead to formation of fast collimated jets extending to hundreds of ion inertial lengths downstream of the shock. The jets, which have high dynamical pressure, "stir" the downstream region, creating large scale disturbances such as vortices, sunward flows, and can trigger flux ropes along the magnetopause. This phenomenology closes the loop between shocks, turbulence, and magnetic reconnection in ways previously unrealized. These interconnections appear generic for the collisionless plasmas typical of space and are expected even at planar shocks, although they will also occur at curved shocks as occur at planets or around ejecta. V C 2014 AIP Publishing LLC. [http://dx.

This Brief Report presents experimental and computational results on bubble formation in microfluidic devices. Bubbles are generated at the right-angle intersection of four identical square microchannels. When the pressure gradient generated by the liquid flow dominates the pressure gradient generated by gas flow, the length of the produced confined bubbles follows a law based on the channel size and fluid volume fraction. This bubble production technique was used to produce monodisperse aqueous foam in two-dimensional and three-dimensional microchannels.

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

The Protein Data Bank (PDB) is the global archive for structural information on macromolecules, and a popular resource for researchers, teachers, and students, amassing more than one million unique users each year. Crystallographic structure models in the PDB (more than 100,000 entries) are optimized against the crystal diffraction data and geometrical restraints. This process of crystallographic refinement typically ignored hydrogen bond (H‐bond) distances as a source of information. However, H‐bond restraints can improve structures at low resolution where diffraction data are limited. To improve low‐resolution structure refinement, we present methods for deriving H‐bond information either globally from well‐refined high‐resolution structures from the PDB‐REDO databank, or specifically from on‐the‐fly constructed sets of homologous high‐resolution structures. Refinement incorporating HOmology DErived Restraints (HODER), improves geometrical quality and the fit to the diffraction data for many low‐resolution structures. To make these improvements readily available to the general public, we applied our new algorithms to all crystallographic structures in the PDB: using massively parallel computing, we constructed a new instance of the PDB‐REDO databank (https://pdb-redo.eu). This resource is useful for researchers to gain insight on individual structures, on specific protein families (as we demonstrate with examples), and on general features of protein structure using data mining approaches on a uniformly treated dataset.

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We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.ACM Reference FormatLorenzo Casalino1†, Abigail Dommer1†, Zied Gaieb1†, Emilia P. Barros1, Terra Sztain1, Surl-Hee Ahn1, Anda Trifan2,3, Alexander Brace2, Anthony Bogetti4, Heng Ma2, Hyungro Lee5, Matteo Turilli5, Syma Khalid6, Lillian Chong4, Carlos Simmerling7, David J. Hardy3, Julio D. C. Maia3, James C. Phillips3, Thorsten Kurth8, Abraham Stern8, Lei Huang9, John McCalpin9, Mahidhar Tatineni10, Tom Gibbs8, John E. Stone3, Shantenu Jha5, Arvind Ramanathan2∗, Rommie E. Amaro1∗. 2020. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. In Supercomputing ’20: International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI

Using the crystal structure of SARS-CoV-2 papain-like protease (PLpro) as a template, we developed a pharmacophore model of functional centers of the PLpro inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search identified 147 compounds that can be potential inhibitors of SARS-CoV-2 PLpro. The conformations of these compounds underwent 3D fingerprint similarity clusterization, followed by docking of possible conformers to the binding pocket of PLpro. Docking of random compounds to the binding pocket of protease was also done for comparison. Free energies of the docking interaction for the selected compounds were lower than for random compounds. The drug list obtained includes inhibitors of HIV, hepatitis C, and cytomegalovirus (CMV), as well as a set of drugs that have demonstrated some activity in MERS, SARS-CoV, and SARS-CoV-2 therapy. We recommend testing of the selected compounds for treatment of COVID-19