Research Article
A Multi-core Tool for Searching Protein Structural Similarities
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 50 - Issue 12 |
| Published: July 2012 |
| Authors: Ahmed Salah, Kenlili, Tarek F. Gharib, Abdul Fattah Mashat |
10.5120/7825-1015
|
Ahmed Salah, Kenlili, Tarek F. Gharib, Abdul Fattah Mashat . A Multi-core Tool for Searching Protein Structural Similarities. International Journal of Computer Applications. 50, 12 (July 2012), 34-36. DOI=10.5120/7825-1015
@article{ 10.5120/7825-1015,
author = { Ahmed Salah,Kenlili,Tarek F. Gharib,Abdul Fattah Mashat },
title = { A Multi-core Tool for Searching Protein Structural Similarities },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 50 },
number = { 12 },
pages = { 34-36 },
doi = { 10.5120/7825-1015 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Ahmed Salah
%A Kenlili
%A Tarek F. Gharib
%A Abdul Fattah Mashat
%T A Multi-core Tool for Searching Protein Structural Similarities%T
%J International Journal of Computer Applications
%V 50
%N 12
%P 34-36
%R 10.5120/7825-1015
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The analysis of protein structural similarities plays an important role in different biological fields. These fields vary from the process of developing new drugs to detecting the evolutionrelationships. As the number of protein structures grows rapidly there is an increasing demand for improving the speed of the computational tools that handles proteome. The wide prevalence of multi cores computers and its low price can be employed to speed up the existing tools used for searching protein structural similarities. In this report, we present a modified version of a PSISA tool, which efficiently used to find the structural similarities between different proteins and maintains the load balance between cores. Using an Intel 8 cores computer and the structural classification of proteins (SCOP) dataset, the experiments show an average speed up 1. 8 using 8 cores without affecting the memory usage or the accuracy of the tool.
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