Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Parallel Protein Structure Alignment: A Comparative Study of Two Parallel Programming Paradigms

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Nada M. A. Mohammed, Hala M. Ebeid, Mostafa G. M. Mostafa, Mahmoud E. A. Gadallah

Nada M A Mohammed, Hala M Ebeid, Mostafa G M Mostafa and Mahmoud E A Gadallah. Parallel Protein Structure Alignment: A Comparative Study of Two Parallel Programming Paradigms. International Journal of Computer Applications 150(7):43-48, September 2016. BibTeX

	author = {Nada M. A. Mohammed and Hala M. Ebeid and Mostafa G. M. Mostafa and Mahmoud E. A. Gadallah},
	title = {Parallel Protein Structure Alignment: A Comparative Study of Two Parallel Programming Paradigms},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2016},
	volume = {150},
	number = {7},
	month = {Sep},
	year = {2016},
	issn = {0975-8887},
	pages = {43-48},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016911563},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Protein 3D structure alignment process has become the key focus of interest in structural bioinformatics. Yet, obtaining perfect alignment in a short execution time was not successful to this point. To overcome this problem, researchers tend to use parallel programming techniques to enhance the performance of the alignment process. In this article, we compare between two parallel programming paradigms for implementing a parallel version of the well-known pairwise alignment algorithm MatAlign. This parallel algorithm is implemented by using two common APIs for C++ parallel programming, which are OpenMP for multi-core CPUs and CUDA for multi-core GPUs. The results show that beside the significant improvement of the parallel implementation over the sequential one, it also shows that the multi-core GPU parallel programming model improves speedup over multi-core CPU programming model.


  1. Alexandrov, N., and Fischer, D. 1996. Analysis of topological and nontopological structural similarities in the PDB: New examples with old structures. Proteins: Structure, Function, and Bioinformatics, 25(3), 354-365.
  2. Singh, A. P., and Brutlag, D. L. 2000. Protein Structure Alignment: A Comparison of Methods. Bioinformatics.
  3. Aung, Z., and Tan, K. 2006. MatAlign: Precise protein structure comparison by matrix alignment. Journal of Bioinformatics and Computational Biology, 4(06), 1197-1216.
  4. Clark, M. 2012. Introduction to GPU Computing, s.l.: Developer Technology Group, nVIDIA.
  5. Daniel, F., Arne, E., Danny, R., and David, E. 1996. Assessing the performance of fold recognition methods by means of a comprehensive benchmark. In Proceedings of Pacific Symposium on Biocomputing 397, 300-318.
  6. Dhraief, A., Issaoui, R., and Belghith, A. 2011. Parallel Computing the Longest Common Subsequence (LCS) on GPUs: Efficiency and Language Suitability. In The 1st International Conference on Advanced Communications and Computation (INFOCOMP).
  7. Godzik, A. 1996. The structural alignment between two proteins: is there a unique answer?. Protein Science: A Publication of the Protein Society, 5(7), 1325-1338.
  8. Halperin, I., Ma, B., Wolfson, H., and Nussinov, R. 2002. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins: Structure, Function, and Bioinformatics 47(4), 409-443.
  9. Hung, C.-L., and Lin, Y.-L. 2013. Implementation of a Parallel Protein Structure Alignment Service on Cloud. International Journal of Genomics.
  10. Jones, S., 2012. Introduction to Dynamic Parallelism. In GPU Technology Conference Presentation S 338, p. 2012.
  11. Koehl, P. 2001. Protein structure similarities. Curr Opin Struct Biol. 11, 348-353.
  12. Mrozek, D., Brożek, M., and Małysiak-Mrozek, B. 2014. Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA. J Mol Model, 20(2), p. 2067.
  13. Murzin, A. G., Brenner, S. E., Hubbard, T. & Chothia, C., 1995. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247(4), 536–540.
  14. Needleman, S. B., and Wunsch, C. D. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol, 48(3), 443-53.
  15. Samudrala, R., and Hung, L.-H. 2012. Accelerated Protein Structure Comparison using TM-Score-GPU. Bioinformatics, 28(16), 2191-2192.
  16. Mohammed, N. M., Ebeid, H. M., Mostafa, M. G., and Gadallah, M. E., 2016. PTM-MatAlign: A Fast GPU-Based Algorithm for Pairwise Protein Structure Alignment, submitted to International Journal of Computational Biology, (2016).
  17. Shin, D. H., Hou, J., Chandonia, J. M., Das, D., Choi, I. G., Kim, R., and Kim, S. H.  2007. Structure-based inference of molecular functions of proteins of unknown function from Berkeley Structural Genomics Center. Journal of Structural and Functional Genomics, 8(2-3), 99-105.
  18. Xu, Y., Xu, D., and Liang, J. 2007. Computational Methods for Protein Structure Prediction and Modeling.
  19. Zhang, Y. & Skolnick, J., 2004. Scoring function for automated assessment of protein structure template quality. Proteins, 57(4), 702-10.
  20. Zhang, C., and Lai, L. 2011. Towards structure-based protein drug design. Biochemical Society Transactions, 39(5), 1382-138


MatAlign, TM-Score, GPGPU, CUDA, OpenMP