Mode of operation and low-resolution structure of a multi-domain and hyperthermophilic endo-beta-1,3-glucanase from Thermotoga petrophila

1,3-beta-Glucan depolymerizing enzymes have considerable biotechnological applications including biofuel production, feedstock-chemicals and pharmaceuticals. Here we describe a comprehensive functional characterization and low-resolution structure of a hyperthermophilic laminarinase from Thermotoga petrophila (TpLam). We determine TpLam enzymatic mode of operation, which specifically cleaves internal beta-1,3-glucosidic bonds. The enzyme most frequently attacks the bond between the 3rd and 4th residue from the non-reducing end, producing glucose, laminaribiose and laminaritriose as major products. Far-UV circular dichroism demonstrates that TpLam is formed mainly by beta structural elements, and the secondary structure is maintained after incubation at 90 degrees C. The structure resolved by small angle X-ray scattering, reveals a multi-domain structural architecture of a V-shape envelope with a catalytic domain flanked by two carbohydrate-binding modules. Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved.

A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.