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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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Related in: MedlinePlus

Number of structures from the database that qualified for the second phase as a function of query protein length for various values of the qualification threshold
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Fig13: Number of structures from the database that qualified for the second phase as a function of query protein length for various values of the qualification threshold

Mentions: Figure 13 shows the relationship between query protein length and the number of structures that qualified for the second phase when various values of the qualification threshold QT were applied. For example, for QT = 0.01 (yellow line), we can see that almost all of the database structures qualified for the second phase, regardless of query protein length. In this cases, there is practically no filtering based on the secondary structures identified in the query protein. On the other hand, for QT = 0.8 (red line), we noticed that for query proteins over 150 residues in length, only single database structures are eligible for further processing.Fig. 13


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)

Number of structures from the database that qualified for the second phase as a function of query protein length for various values of the qualification threshold
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig13: Number of structures from the database that qualified for the second phase as a function of query protein length for various values of the qualification threshold
Mentions: Figure 13 shows the relationship between query protein length and the number of structures that qualified for the second phase when various values of the qualification threshold QT were applied. For example, for QT = 0.01 (yellow line), we can see that almost all of the database structures qualified for the second phase, regardless of query protein length. In this cases, there is practically no filtering based on the secondary structures identified in the query protein. On the other hand, for QT = 0.8 (red line), we noticed that for query proteins over 150 residues in length, only single database structures are eligible for further processing.Fig. 13

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