A paper was published in the journal Bioinformatics, which describes the use of Graphics Processing Units (GPU's) to accelerate computations comparing protein structures.
“Accelerated protein structure comparison using TM-score-GPU”
Lay Person Abstract:
As part of the analysis of the computed results of the Nutritious Rice for the World project, the researchers need to be able to compare protein structures and efficiently compute a similarity score. A scoring method based on “Template Modeling”, known as TM-score, provides significantly better results than the “root-mean-square-deviation” method, but requires much more computer processing time. To solve this problem the researchers developed a version of the TM-score algorithm which makes use of Graphics Processing Units (GPU's) which are found in newer video hardware, used particularly on gaming computers to enhance the visual experience. Using GPU's they were able to run millions of protein comparisons about 70 times faster. The paper describes how they accomplished this and they offer the software freely to other scientists, who may be able to use it for their research.
Motivation: Accurate comparisons of different protein structures play important roles in structural biology, structure prediction and functional annotation. The root-mean-square-deviation (RMSD) after optimal superposition is the predominant measure of similarity due to the ease and speed of computation. However, global RMSD is dependent on the length of the protein and can be dominated by divergent loops that can obscure local regions of similarity. A more sophisticated measure of structure similarity, Template Modeling (TM)-score, avoids these problems, and it is one of the measures used by the community-wide experiments of critical assessment of protein structure prediction to compare predicted models with experimental structures. TM-score calculations are, however, much slower than RMSD calculations. We have therefore implemented a very fast version of TM-score for Graphical Processing Units (TM-score-GPU), using a new and novel hybrid Kabsch/quaternion method for calculating the optimal superposition and RMSD that is designed for parallel applications. This acceleration in speed allows TM-score to be used efficiently in computationally intensive applications such as for clustering of protein models and genomewide comparisons of structure.
Results: TM-score-GPU was applied to six sets of models from Nutritious Rice for the World for a total of 3 million comparisons. TM-score-GPU is 68 times faster on an ATI 5870 GPU, on average, than the original CPU single-threaded implementation on an AMD Phenom II 810 quad-core processor. Availability and implementation: The complete source, including the GPU code and the hybrid RMSD subroutine, can be downloaded and used without restriction at http://software.compbio.washington.edu/misc/downloads/tmscore/. The implementation is in C++/OpenCL.
Access to Paper:
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