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

Abstract Searching for similar 3D protein structures is one of the primary processes employed in the field of structural bioin-formatics. However, the computational complexity of this pro-cess means that it is constantly necessary to search for new methods that can perform such a process faster and more effi-ciently. 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 pro-cessing units (GPUs) and general purpose graphics processing units (GPGPUs) can perform many time-consuming and com-putationally demanding processes much more quickly than a classical CPU can. In this paper, we describe the GPU-based implementation of the CASSERTalgorithm for 3D protein struc-ture 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