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 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.

A parallel computational approach for similarity search using Bloom filters

“…Hwang et al implemented a GPU version of DUST in which they performed probability computations to accelerate the performance of the initial version of DUST . To identify the structural similarity in exponentially growing protein dataset, Mrozek et al proposed a GPU‐based CASSERT technique named GPU‐CASSERT . The Bloom filter–based similarity search is used for privacy preserving record linkage used in many real‐world health care systems by different countries such as Australia, Brazil, Germany, and Switzerland.…”

“…For efficiency, GPU uses a single program called kernel to process multiple data items in parallel. The GPU‐based implementations do not reduce the program complexity, but, because of massive parallel processing, it reduces the overall processing time . In such programming models, all datasets must be independent of each other.…”

“…24 To identify the structural similarity in exponentially growing protein dataset, Mrozek et al proposed a GPU-based CASSERT technique named GPU-CASSERT. 25 The Bloom filter-based similarity search is used for privacy preserving record linkage used in many real-world health care systems by different countries such as Australia, Brazil, Germany, and Switzerland. Randall et al introduced a Bloom filter-based method suitable for a large-scale linkage of patient records.…”

Computational Feasibility of an Exhaustive Search of Side‐Chain Conformations in Protein‐Protein Docking

“…A single‐thread A* search algorithm implemented on CUDA‐enabled NVIDIA Tesla K20c GPU yielded 45‐fold acceleration compared to the traditional C implementation on Intel Xeon E5‐1620 CPU . A 3D protein structure similarity searching algorithm implemented on GTX 560Ti GPU has shown a 180‐fold increase in speed compared to a single‐thread implementation on Intel Xeon E5620 . Dang et al reported speed up of about one order of magnitude for CUDA‐based implementation of a clustering algorithm on GeForce Titan GPU compared to a multi‐threaded CPU implementation on a Core‐i7 2700.…”

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

“…GPGPU (General-Purpose computing on Graphics Processing Unit) is a typical instance. Example implementations: two-list algorithm for the subset-sum problem [9] or protein structure similarity search engine [10] illustrate the approach-parallel algorithms execution on GPU requires adjusting to the specific architecture.…”