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PTM-MatAlign: A Fast GPU-based Algorithm for Pairwise Protein Structure Alignment

by Nada M. A. Mohammed, Hala M. Ebeid, Mostafa G. M. Mostafa, Mahmoud E. A. Gadallah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 19
Year of Publication: 2020
Authors: Nada M. A. Mohammed, Hala M. Ebeid, Mostafa G. M. Mostafa, Mahmoud E. A. Gadallah
10.5120/ijca2020920147

Nada M. A. Mohammed, Hala M. Ebeid, Mostafa G. M. Mostafa, Mahmoud E. A. Gadallah . PTM-MatAlign: A Fast GPU-based Algorithm for Pairwise Protein Structure Alignment. International Journal of Computer Applications. 176, 19 ( May 2020), 31-40. DOI=10.5120/ijca2020920147

@article{ 10.5120/ijca2020920147,
author = { Nada M. A. Mohammed, Hala M. Ebeid, Mostafa G. M. Mostafa, Mahmoud E. A. Gadallah },
title = { PTM-MatAlign: A Fast GPU-based Algorithm for Pairwise Protein Structure Alignment },
journal = { International Journal of Computer Applications },
issue_date = { May 2020 },
volume = { 176 },
number = { 19 },
month = { May },
year = { 2020 },
issn = { 0975-8887 },
pages = { 31-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number19/31309-2020920147/ },
doi = { 10.5120/ijca2020920147 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:42:58.299569+05:30
%A Nada M. A. Mohammed
%A Hala M. Ebeid
%A Mostafa G. M. Mostafa
%A Mahmoud E. A. Gadallah
%T PTM-MatAlign: A Fast GPU-based Algorithm for Pairwise Protein Structure Alignment
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 19
%P 31-40
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Although the pairwise protein three-dimensional (3D) structure alignment is vital in structural bioinformatics, its complexity is categorized as non-deterministic polynomial-time hard (NP-hard). Hence, researchers strive to develop algorithms to overcome the heavy computation complexity. Most of their attempts tend to achieve more accurate alignment results regardless of the computational execution time. Therefore, finding a fast alignment algorithm with accurate results is still an outstanding task. Recently, General Purpose Graphical Processing Units (GPGPUs) can execute the many time-consuming algorithms faster than the CPUs can. This paper proposes the GPU-based implementation of the MatAlign algorithm which is based on the two-level alignment of protein. This GPU implementation yields about 11 increase in speed over its CPU-based, single-core implementation on GPU GeForce GTX 860M (640 cores, 2GB RAM) and Intel Core i7-4710HQ (2.50GHz, 8GB RAM, 8 cores) CPU. In order to achieve more accurate results, PTM-MatAlign is implemented to use the Template Modeling Score (TM-score) instead of the MatAlign regular score function.

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Index Terms

Computer Science
Information Sciences

Keywords

Structure Alignment CUDA GPU Parallel Computing TM-Score MatAlign.

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