On the Parallelization of the SProt Measure and the TM-Score Algorithm

  • Jakub Galgonek
  • Martin Kruliš
  • David Hoksza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7640)

Abstract

Similarity measures for the protein structures are quite complex and require significant computational time. We propose a parallel approach to this problem to fully exploit the computational power of current CPU architectures. This paper summarizes experience and insights acquired from the parallel implementation of the SProt similarity method, its database access method, and also the wellknown TM-score algorithm. The implementation scales almost linearly with the number of CPUs and achieves 21.4× speedup on a 24-core system. The implementation is currently employed in the web application http://siret.cz/p3s.

Keywords

protein structure similarity parallel optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Orengo, C.A., Michie, A.D., Jones, S., Jones, D.T., Swindells, M.B., Thornton, J.M.: CATH–a hierarchic classification of protein domain structures. Structure (London, England: 1993) 5(8), 1093–1108 (1997)CrossRefGoogle Scholar
  2. 2.
    Balaji, S., Srinivasan, N.: Use of a database of structural alignments and phylogenetic trees in investigating the relationship between sequence and structural variability among homologous proteins. Protein Eng. 14(4), 219–226 (2001)CrossRefGoogle Scholar
  3. 3.
    Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. The EMBO Journal 5(4), 823–826 (1986)Google Scholar
  4. 4.
    Aung, Z., Tan, K.L.: Rapid 3D protein structure database searching using information retrieval techniques. Bioinformatics 20(7), 1045–1052 (2004)CrossRefGoogle Scholar
  5. 5.
    Tung, C.H.H., Huang, J.W.W., Yang, J.M.M.: Kappa-alpha plot derived structural alphabet and BLOSUM-like substitution matrix for rapid search of protein structure database. Genome Biol. 8(3), R31 (2007)Google Scholar
  6. 6.
    Sacan, A., Toroslu, H.I., Ferhatosmanoglu, H.: Integrated search and alignment of protein structures. Bioinformatics 24(24), 2872–2879 (2008)CrossRefGoogle Scholar
  7. 7.
    Galgonek, J., Hoksza, D., Skopal, T.: SProt: sphere-based protein structure similarity algorithm. BMC Proteome Science 9(suppl. 1), S20 (2011)Google Scholar
  8. 8.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Skolnick, J.: Scoring function for automated assessment of protein structure template quality. Proteins 57(4), 702–710 (2004)CrossRefGoogle Scholar
  10. 10.
    Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Crystallogr. A 32(5), 922–923 (1976)CrossRefGoogle Scholar
  11. 11.
    Micó, M.L., Oncina, J., Vidal, E.: A new version of the nearest-neighbour approximating and eliminating search algorithm (AESA) with linear preprocessing time and memory requirements. Pattern Recognition Letters 15(1), 9–17 (1994)CrossRefGoogle Scholar
  12. 12.
    Moreno-Seco, F., Micó, L., Oncina, J.: Extending LAESA Fast Nearest Neighbour Algorithm to Find the k Nearest Neighbours. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SSPR and SPR 2002. LNCS, vol. 2396, pp. 718–724. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Chávez, E., Navarro, G., Baeza-Yates, R.A., Marroquín, J.L.: Searching in metric spaces. ACM Comput. Surv. 33(3), 273–321 (2001)CrossRefGoogle Scholar
  14. 14.
    Amdahl, G.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18-20, 1967, Spring Joint Computer Conference, pp. 483–485. ACM (1967)Google Scholar
  15. 15.
    Reinders, J.: Intel threading building blocks: outfitting C++ for multi-core processor parallelism. O’Reilly Media, Inc. (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jakub Galgonek
    • 1
  • Martin Kruliš
    • 1
  • David Hoksza
    • 1
  1. 1.Departement of Software EngineeringCharles University in PraguePraha 1Czech Republic

Personalised recommendations