Scaling to thousands of processors powers faster,
better protein structure predictions.
Decoding proteins
T e r a G r i d
S c i e n c e
H i g h l i g h t s
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Best CASP7 prediction for Target T0380. Shown are the
native protein structure (blue), which is made public only
after predictions were submitted, the initial starting structure (pink), and the Rosetta prediction (green). Following
preparations on the TeraGrid Blue Gene system at SDSC,
the calculation was performed in an unprecedented three
hours on 40,960 processors of the IBM Watson Blue Gene
system. Image courtesy of Ross Walker, SDSC, and Srivatsan
Raman, University of Washington.
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http://www.bakerlab.org/
P
David Baker, Howard
Hughes Institute, University
of Washington.
Only two short years ago Baker, whose Rosetta code is
proving to be one of the best at predicting protein structures,
had never used NSF TeraGrid resources. Today, he routinely
runs on 6,000 processors of the IBM Blue Gene Data system
at SDSC.
“Having access to the massive computing resources of the
TeraGrid is revolutionizing the approaches that are open for
protein structure prediction,” says Baker.
Using TeraGrid resources at NCSA and SDSC, Baker's group
ran targets for the most recent CASP7 competition (Critical
Assessment of Structure Prediction), organized to encourage
improvements in computational methods. They consumed
1.3 million processor hours on TeraGrid Condor resources at
NCSA. From these computations, 22 targets were determined
and more than 730,000 processor-hours on SDSC's Blue
Gene Data system were used to generate the high-resolution
structures.
In collaboration with SDSC computational scientists, his code
was also prepared on the TeraGrid Blue Gene system and
successfully run on more than 40,000 processors of the
IBM Blue Gene system at the Watson Research Center,
an important step toward petascale computing.
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As a pivotal step in understanding proteins, University of
Washington and Howard Hughes Medical Institute investigator David Baker is working with computational experts at
SDSC and NCSA to harness high-performance TeraGrid
resources. These supercomputers are speeding his predictions of elusive protein structures and holding out the
promise of rational design of new drugs for tomorrow's
medicine chest.
T e r a G r i d
roteins are the molecules of life, and the myriad
ways they function throughout the body depend on the precise details of their 3D shapes. While scientists know the
sequences of the amino acids that make up proteins, finding
out just how these building blocks are spatially arranged in a
protein continues to be a major challenge.
Decoding proteins
More information:
Protein structure prediction by the Rosetta code, showing the unknown
structure (blue), the X-ray structure (red, unknown when the prediction
was calculated), and a low-resolution NMR structure (green).
Image courtesy of Ross Walker, SDSC, and Srivatsan Raman, University of Washington.
In this run, a Rosetta protein structure prediction was
completed from scratch in only three hours, something
that normally takes weeks even on a 1,000-processor supercomputer. The prediction for a specific target protein led more
than 100 CASP7 entries from other groups who used a variety
of different methods.
To begin investigations of the design of enzymes for new
tasks, Baker's group has also used 2.5 million processor
hours on SDSC's Blue Gene Data system in 2007. By reversing the steps of protein structure prediction, the researchers
have used simulations that start with a protein structure
and find the amino acid sequence, which can then be used
to produce the new “designed” enzyme.
In addition to his own research, Baker offers a Web portal,
called Robetta, that allows other researchers to run Rosetta
jobs. Initially, jobs submitted through the Robetta portal were
running on a small cluster in Baker's lab, but this cluster
was overwhelmed by the volume of submitted jobs, forcing
scientists to endure months-long wait times. NCSA has
provided additional computational power to bring the
throughput time down to days instead of months, enabling
more researchers to complete more runs using Rosetta.