Posts Tagged Molecular

Rethinking the way we publish

In two weeks (15th September, 2011) there will be a webinar on the above topic:

“The scientific publishing world is witnessing rapid change, especially in the speed-of-light world of genetics and genomics. You are invited to join Professor Andre van Wijnen, Editor-in-Chief of GENE and Bart Wacek, Publisher (Elsevier), together with the wider genetics community to discuss how authors, reviewers, and editors can not only benefit from, but contribute to, the editorial process.”

See here for details and registration.
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A documentary on network-theory

As I met Marc Vidal personally, this is just about one-degree of separation for me 😉 – Enjoy!

(found via Barabasi lab, thanks to Henning Stehr for hints)

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Speeding up molecular shape comparison

gpGPU acceleration is steadily contributing to progress in the computational LifeSciences, for example in rational DrugDesign. OpenEye scientific software announced a performance-increase of their molecular shape comparison ROCS by about 2-3 orders of magnitude (100x-1000x(!)) using GPUs. With this they won the “Best Show” award at the 2011 BioIT-World.

Now FastROCS processes 2 million conformations per second on a Quad Fermi box.

This enables all vs. all shape comparison across entire compound libraries. Below is an interview with Joe Corkery at the BioIT World, and they also have a couple of interesting posts on their blog.

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The guardian of the genome

P53 is a very central hub of the cellular (protein-)interaction network. Its’ importance is also underlined by the over 50.000 articles already published on it, most of it related to cancer: entering ‘P53 AND cancer’ into pubmed returns 43919 articles (17726 entries on ‘P53 and mutation‘ and 5237 articles about p53 gene function. In case you wanted a somewhat lighter executive musical summary, see the video below (found via SchockWellenReiter): “to keep your genome mutation free, it’s the concentrated passion of P53”

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Lively Molecules in a Crowded Cytoplasm

cytoplasm model at the end of a Brownian dynamics simulation performed with the ‘full’ energy model

Proteins are not static entities – since we live at about 300 degrees above absolute zero there is constant Brownian motion. However, looking at deposited X-Ray structures, one might get the impression that the structures are rigidly sitting in vacuum – nothing could be further from the truth! I like the analogy with early photography :

Photography, ca. 1893

because the photoplates were not that sensitive, long exposure times were necessary. Hence people had to hold very, very still for several minutes in order to get a decent picture. Photographers had special setups and chairs with neckbraces to keep the poor subject in place. This apparatus is the analogy to a protein crystal – it keeps the proteins in place, floppy and moving parts will not show up on the resulting electron-density maps.
The photographs of our great-grandfathers leave us with the impression that they were very stiff people, largely devoid of any humour. That’s probably not true, but how happy and lively would you look if you had to sit still for quite some time in your best outfit with your head squeezed onto some weird mechanical contraption? The same holds true for proteins. Read the rest of this entry »

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Speeding through the CLOUD

video tutorial to set up your HPC environment in less than 10 minutes.

Along the lines of Cloud and High Performance Computing (HPC) Amazon pushes its web-services into research applications : “With Amazon Web Services businesses and researchers can easily fulfill their high performance computational requirements with the added benefit of ad-hoc provisioning and pay-as-you-go pricing.” The image above has a link to the tutorial on setting up a HPC environment in less than 10 minutes – demonstrating the setup of a 7-node virtual cluster and running a molecular dynamics simulation with CHARMM.

NVIDIA unveiled the Tesla M2090 GPU this week. Equipped with 512 CUDA parallel processing cores, it delivers 665 GigaFLOPS of peak double-precision performance and 178 GB/sec memory bandwidth...

Similarly, the power of current many-core (>500 cores!) general-purpose graphics processing units (gpGPUs) can be awesome, of course the code has to be adjusted to take full advantage of the architecture. The release of the CUDA Toolkit 4.0 (Common Universal Device Architecture) in April simplified parallel programming already
and foreshadowed future CPU-GPU architectures. Think yesterdays beowulf-cluster shrunk to a single card that fits into your desktop machine. Depending on the application, GPUs seems to pack at least 10x the punch of a comparable CPU, for Amber this factor seems to be about 20x. NVidia has a test-drive of Amber available … Simulators, start your engines! Also there are a couple of standard Bioinformatics applications readily available for GPUs. Just last week, they announced “New NVIDIA Tesla GPU Smashes World Record in Scientific Computation“. Allegedly, this was achieved on only 4(!) GPUs – that fits into a midi-tower under the desk. Imagine if you’d stuff a full-height 19inch rack with these beasts – you’d not quite get to something like the Nebulae or Tianhe1A but definitely would land somewhere among the TOP500 Supercomputers in the world.

And finally in this category, also Googles App-Engine (GAE) is developing nicely, although I haven’t yet found a bioinformatics-related pet-project that would motivate me to test it more thoroughly. “App Engine enables your application to scale automatically without worrying about managing machines.“. Yep, that’s the spirit.

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