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

Mrozek D, Brożek M, Małysiak-Mrozek B - J Mol Model (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Informatics, Silesian University of Technology, Gliwice, Poland, dariusz.mrozek@polsl.pl.

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.

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Structural features included in molecular residue descriptors marked on part of a sample protein structure: residue type (Met, Gln, Ile, Phe), secondary structure type (β-strand in this case), length of the vector between the Cα atoms (/Ci/), and the γ angle
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Fig2: Structural features included in molecular residue descriptors marked on part of a sample protein structure: residue type (Met, Gln, Ile, Phe), secondary structure type (β-strand in this case), length of the vector between the Cα atoms (/Ci/), and the γ angle

Mentions: Each descriptor si is defined by the following vector of features:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {s}_i=<\left/{C}_i\right/,{\gamma}_i, SS{E}_i,{r}_i>, $$\end{document}where /Ci/ is the length of the vector between the Cα atoms of the ith and (i + 1)th amino acids in a protein chain, γi is the angle between the successive vectors Ci and Ci + 1, SSEi is the type of secondary structure formed by the ith residue, and ri is the type of amino acid represented by this residue (Fig. 2).Fig. 2


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

Mrozek D, Brożek M, Małysiak-Mrozek B - J Mol Model (2014)

Structural features included in molecular residue descriptors marked on part of a sample protein structure: residue type (Met, Gln, Ile, Phe), secondary structure type (β-strand in this case), length of the vector between the Cα atoms (/Ci/), and the γ angle
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3936136&req=5

Fig2: Structural features included in molecular residue descriptors marked on part of a sample protein structure: residue type (Met, Gln, Ile, Phe), secondary structure type (β-strand in this case), length of the vector between the Cα atoms (/Ci/), and the γ angle
Mentions: Each descriptor si is defined by the following vector of features:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {s}_i=<\left/{C}_i\right/,{\gamma}_i, SS{E}_i,{r}_i>, $$\end{document}where /Ci/ is the length of the vector between the Cα atoms of the ith and (i + 1)th amino acids in a protein chain, γi is the angle between the successive vectors Ci and Ci + 1, SSEi is the type of secondary structure formed by the ith residue, and ri is the type of amino acid represented by this residue (Fig. 2).Fig. 2

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Informatics, Silesian University of Technology, Gliwice, Poland, dariusz.mrozek@polsl.pl.

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.

Show MeSH
Related in: MedlinePlus