Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA

Dariusz Mrozek, Milosz Brozek, Bozena Malysiak-Mrozek
Institute of Informatics, Silesian University of Technology, Gliwice, Poland
Journal of Molecular Modeling, 20:2067, 2014




   journal={Journal of Molecular Modeling},




   title={Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA},


   publisher={Springer Berlin Heidelberg},

   keywords={3D protein structure; Similarity searching; Structure comparison; GPU; CUDA; Parallel programming; Structure matching; Structure alignment},

   author={Mrozek, Dariusz and Brozek, Milosz and Malysiak-Mrozek, Bozena},




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Searching for similar 3D protein structures is one of the primary processes employed in the field of structural bioinformatics. However, the computational complexity of this process means that it is constantly necessary to search for new methods that can perform such a process faster and more efficiently. Finding molecular substructures that complex protein structures have in common is still a challenging task, especially when entire databases containing tens or even hundreds of thousands of protein structures must be scanned. Graphics processing units (GPUs) and general purpose graphics processing units (GPGPUs) can perform many time-consuming and computationally demanding processes much more quickly than a classical CPU can. In this paper, we describe the GPU-based implementation of the CASSERT algorithm for 3D protein structure similarity searching. This algorithm is based on the two-phase alignment of protein structures when matching fragments of the compared proteins. The GPU (GeForce GTX 560Ti: 384 cores, 2GB RAM) implementation of CASSERT ("GPU-CASSERT") parallelizes both alignment phases and yields an average 180-fold increase in speed over its CPU-based, single-core implementation on an Intel Xeon E5620 (2.40GHz, 4 cores). In this paper, we show that massive parallelization of the 3D structure similarity search process on many-core GPU devices can reduce the execution time of the process, allowing it to be performed in real time. GPU-CASSERT is available at: http://zti.polsl.pl/dmrozek/science/gpucassert/cassert.htm.
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