Abstract
One of the key challenges in computational biology is prediction of
three-dimensional protein structures from amino-acid sequences. For
most proteins, the ``native state'' lies at the bottom of a
free-energy landscape. Protein structure prediction involves varying
the degrees of freedom of the protein in a constrained manner until it
approaches its native state. In the Rosetta protein structure
prediction protocols, a large number of independent folding
trajectories are simulated, and several lowest-energy results are
likely to be close to the native state. The availability of
hundred-teraflop, and shortly, petaflop, computing resources is
revolutionizing the approaches available for protein structure
prediction. Here, we discuss issues involved in utilizing such machines
efficiently with the Rosetta code, including an overview of recent
results of the Critical Assessment of Techniques for Protein Structure
Prediction 7 (CASP7) in which the computationally demanding
structure-refinement process was run on 16 racks of the IBM Blue Gene/L
(TM) system at the IBM T. J. Watson Research Center. We highlight
recent advances in high-performance computing and discuss,future
development paths that make use of the next-generation petascale (>
10(12) floating-point operations per second) machines.
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