Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2015, Article ID 563674, 13 pages
http://dx.doi.org/10.1155/2015/563674
Research Article

Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures

Department of Informatics and Telecommunications, University of Athens, Athens, Greece

Received 24 July 2015; Revised 4 October 2015; Accepted 5 October 2015

Academic Editor: Yudong Cai

Copyright © 2015 Anuj Sharma and Elias S. Manolakos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. A. Laskowski, J. D. Watson, and J. M. Thornton, “ProFunc: a server for predicting protein function from 3D structure,” Nucleic Acids Research, vol. 33, no. 2, pp. 89–93, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Pang, N. Zhao, M. Becchi, D. Korkin, and C.-R. Shyu, “Accelerating large-scale protein structure alignments with graphics processing units,” BMC Research Notes, vol. 5, no. 1, article 116, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. E. Bielska, X. Lucas, A. Czerwiniec, J. M. Kasprzak, K. H. Kaminska, and J. M. Bujnicki, “Virtual screening strategies in drug design methods and applications,” Journal of Biotechnology, Computational Biology and Bionanotechnology, vol. 92, no. 3, pp. 249–264, 2011. View at Google Scholar
  4. H. Kubinyi, “Structure-based design of enzyme inhibitors and receptor ligands,” Current Opinion in Drug Discovery and Development, vol. 1, no. 1, pp. 4–15, 1998. View at Google Scholar · View at Scopus
  5. G. Lancia and S. Istrail, “Protein structure comparison: algorithms and applications,” in Mathematical Methods for Protein Structure Analysis and Design, vol. 2666 of Lecture Notes in Computer Science, pp. 1–33, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  6. I. Eidhammer, I. Jonassen, and W. R. Taylor, “Structure comparison and structure patterns,” Journal of Computational Biology, vol. 7, no. 5, pp. 685–716, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. B. Ruiz-Blanco, W. Paz, J. Green, and Y. Marrero-Ponce, “ProtDCal: a program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins,” BMC Bioinformatics, vol. 16, no. 1, article 162, 2015. View at Publisher · View at Google Scholar
  8. D. Barthel, J. D. Hirst, J. Błażewicz, E. K. Burke, and N. Krasnogor, “ProCKSI: a decision support system for protein (structure) comparison, knowledge, similarity and information,” BMC Bioinformatics, vol. 8, article 416, 2007. View at Publisher · View at Google Scholar
  9. M. Arriagada and A. Poleksic, “On the difference in quality between current heuristic and optimal solutions to the protein structure alignment problem,” BioMed Research International, vol. 2013, Article ID 459248, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. D. E. Robillard, P. T. Mpangase, S. Hazelhurst, and F. Dehne, “SpeeDB: fast structural protein searches,” Bioinformatics, vol. 31, no. 18, pp. 3027–3034, 2015. View at Publisher · View at Google Scholar
  11. M. Veeramalai, D. Gilbert, and G. Valiente, “An optimized TOPS+ comparison method for enhanced TOPS models,” BMC Bioinformatics, vol. 11, no. 1, article 138, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. S. B. Pandit and J. Skolnick, “Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score,” BMC Bioinformatics, vol. 9, no. 1, article 531, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Mavridis and D. Ritchie, “3D-blast: 3D protein structure alignment, comparison, and classification using spherical polar Fourier correlations,” in Proceedings of the Pacific Symposium on Biocomputing, pp. 281–292, World Scientific Publishing, Kamuela, Hawaii, USA, January 2010, http://hal.inria.fr/inria-00434263/en/.
  14. A. A. Shah, G. Folino, and N. Krasnogor, “Toward high-throughput, multicriteria protein-structure comparison and analysis,” IEEE Transactions on Nanobioscience, vol. 9, no. 2, pp. 144–155, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Machine Learning, vol. 51, no. 2, pp. 181–207, 2003. View at Publisher · View at Google Scholar
  16. P. Sollich and A. Krogh, “Learning with ensembles: how overfitting can be useful,” in Proceedings of the Advances in Neural Information Processing Systems (NIPS '96), vol. 8, pp. 190–196, 1996.
  17. G. Brown, J. Wyatt, R. Harris, and X. Yao, “Diversity creation methods: a survey and categorisation,” Information Fusion, vol. 6, no. 1, pp. 5–20, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Chi, Efficient protein tertiary structure retrievals and classifications using content based comparison algorithms [Ph.D. dissertation], University of Missouri at Columbia, Columbia, Mo, USA, 2007.
  19. A. Poleksic, “Algorithms for optimal protein structure alignment,” Bioinformatics, vol. 25, no. 21, pp. 2751–2756, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. A. A. Shah, Studies on distributed approaches for large scale multi-criteria protein structure comparison and analysis [Ph.D. thesis], University of Nottingham, Nottingham, UK, 2011, http://eprints.nottingham.ac.uk/11735/.
  21. M. Pharr and R. Fernando, GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation, Pearson Eduction, Addison-Wesley Professional, 2005.
  22. M. Azimi, N. Cherukuri, D. Jayashima et al., “Integration challenges and tradeoffs for tera-scale architectures,” Intel Technology Journal, vol. 11, pp. 173–184, 2007. View at Google Scholar
  23. S. Sarkar, T. Majumder, A. Kalyanaraman, and P. P. Pande, “Hardware accelerators for biocomputing: a survey,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '10), pp. 3789–3792, IEEE, Paris, France, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. E. Kouskoumvekakis, D. Soudris, and E. S. Manolakos, “Many-core CPUs can deliver scalable performance to stochastic simulations of large-scale biochemical reaction networks,” in Proceedings of the International Conference on High Performance Computing & Simulation (HPCS '15), pp. 517–524, IEEE, Amsterdam, The Netherlands, July 2015. View at Publisher · View at Google Scholar
  25. A. A. Shah, D. Barthel, and N. Krasnogor, “Grid and distributed public computing schemes for structural proteomics: a short overview,” in Frontiers of High Performance Computing and Networking ISPA 2007 Workshops, vol. 4743, pp. 424–434, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  26. E. Totoni, B. Behzad, S. Ghike, and J. Torrellas, “Comparing the power and performance of Intel's SCC to state-of-the-art CPUs and GPUs,” in Proceedings of the IEEE International Symposium on Performance Analysis of Systems & Software (ISPASS '12), pp. 78–87, IEEE Computer Society, New Brunswick, NJ, USA, April 2012. View at Publisher · View at Google Scholar
  27. T. Bjerregaard and S. Mahadevan, “A survey of research and practices of network-on-chip,” ACM Computing Surveys, vol. 38, no. 1, article 1, Article ID 1132953, 2006. View at Publisher · View at Google Scholar
  28. B. Marker, E. Chan, J. Poulson et al., “Programming many-core architectures—a case study: dense matrix computations on the Intel single-chip cloud computer processor,” Concurrency and Computation: Practice and Experience, 2011. View at Publisher · View at Google Scholar
  29. G. Blake, R. G. Dreslinski, and T. Mudge, “A survey of multicore processors,” IEEE Signal Processing Magazine, vol. 26, no. 6, pp. 26–37, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. P. Guerrier and A. Greiner, “A generic architecture for on-chip packet-switched interconnections,” in Proceedings of the Conference on Design, Automation and Test in Europe (DATE '00), pp. 250–256, ACM, Paris, France, 2000. View at Publisher · View at Google Scholar
  31. D. Atienza, F. Angiolini, S. Murali, A. Pullini, L. Benini, and G. De Micheli, “Network-on-chip design and synthesis outlook,” Integration, vol. 41, no. 2, pp. 1–35, 2008. View at Google Scholar
  32. R. P. Mohanty, A. K. Turuk, and B. Sahoo, “Performance evaluation of multi-core processors with varied interconnect networks,” in Proceedings of the 2nd International Conference on Advanced Computing, Networking and Security (ADCONS '13), pp. 7–11, IEEE Computer Society, Mangalore, India, December 2013. View at Publisher · View at Google Scholar
  33. S. Isaza, Multicore architectures for bioinformatics applications [Ph.D. thesis], University of Lugano, Lugano, Switzerland, 2011.
  34. S. B. Needleman and C. D. Wunsch, “A general method applicable to the search for similarities in the amino acid sequence of two proteins,” Journal of Molecular Biology, vol. 48, no. 3, pp. 443–453, 1970. View at Publisher · View at Google Scholar
  35. S. Sarkar, G. R. Kulkarni, P. P. Pande, and A. Kalyanaraman, “Network-on-chip hardware accelerators for biological sequence alignment,” IEEE Transactions on Computers, vol. 59, no. 1, pp. 29–41, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Sharma, A. Papanikolaou, and E. S. Manolakos, “Accelerating all-to-all protein structures comparison with TMalign using a NoC many-cores processor architecture,” in Proceedings of the 27th IEEE International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW '13), pp. 510–519, IEEE, Cambridge, Mass, USA, May 2013. View at Publisher · View at Google Scholar
  37. I. N. Shindyalov and P. E. Bourne, “Protein structure alignment by incremental combinatorial extension (CE) of the optimal path,” Protein Engineering, vol. 11, no. 9, pp. 739–747, 1998. View at Publisher · View at Google Scholar
  38. N. Krasnogor and D. A. Pelta, “Measuring the similarity of protein structures by means of the universal similarity metric,” Bioinformatics, vol. 20, no. 7, pp. 1015–1021, 2004. View at Publisher · View at Google Scholar · View at Scopus
  39. Y. Zhang and J. Skolnick, “TM-align: a protein structure alignment algorithm based on the TM-score,” Nucleic Acids Research, vol. 33, no. 7, pp. 2302–2309, 2005. View at Publisher · View at Google Scholar
  40. N. Melot, K. Avdic, C. Kessler, and J. Keller, “Investigation of main memory bandwidth on Intel Single-Chip Cloud computer,” in Proceedings of the 3rd Many-Core Applications Research Community Symposium (MARC '11), pp. 107–110, Ettlingen, Germany, July 2011.
  41. S. Saini, H. Jin, R. Hood, D. Barker, P. Mehrotra, and R. Biswas, “The impact of hyper-threading on processor resource utilization in production applications,” in Proceedings of the 18th International Conference on High Performance Computing (HiPC '11), pp. 1–10, Bangalore, India, December 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. H. González-Vélez and M. Leyton, “A survey of algorithmic skeleton frameworks: high-level structured parallel programming enablers,” Software: Practice and Experience, vol. 40, no. 12, pp. 1135–1160, 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. D. K. G. Campbell, “Towards the classification of algorithmic skeletons,” Tech. Rep. YCS 276, 1996. View at Google Scholar
  44. A. Silberschatz, P. B. Galvin, and G. Gagne, Operating System Concepts, John Wiley & Sons, Wiley Publishing, 8th edition, 2008.
  45. D. M. Tax and R. P. W. Duin, “Feature scaling in support vector data descriptions,” in Learning from Imbalanced Datasets, N. Japkowicz, Ed., pp. 25–30, AAAI Press, Menlo Park, Calif, USA, 2000. View at Google Scholar
  46. A. G. Murzin, S. E. Brenner, T. Hubbard, and C. Chothia, “SCOP: a structural classification of proteins database for the investigation of sequences and structures,” Journal of Molecular Biology, vol. 247, no. 4, pp. 536–540, 1995. View at Publisher · View at Google Scholar · View at Scopus
  47. R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015, http://www.r-project.org/.
  48. M. Veeramalai, D. R. Gilbert, and G. Valiente, “An optimized TOPS+ comparison method for enhanced TOPS models,” BMC Bioinformatics, vol. 11, article 138, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. G. Maccari, G. L. B. Spampinato, and V. Tozzini, “SecStAnT: secondary structure analysis tool for data selection, statistics and models building,” Bioinformatics, vol. 30, no. 5, pp. 668–674, 2014. View at Publisher · View at Google Scholar · View at Scopus
  50. S. Mertens, “The easiest hard problem: number partitioning,” in Computational Complexity and Statistical Physics, A. Percus, G. Istrate, and C. Moore, Eds., pp. 125–139, Oxford University Press, New York, NY, USA, 2006. View at Google Scholar
  51. OpenMP API for parallel programming, version 4.0, http://openmp.org/wp/.
  52. J. Handl, J. D. Knowles, and D. B. Kell, “Computational cluster validation in post-genomic data analysis,” Bioinformatics, vol. 21, no. 15, pp. 3201–3212, 2005. View at Publisher · View at Google Scholar
  53. J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008. View at Publisher · View at Google Scholar · View at Scopus
  54. M. P. Forum, “MPI: a message-passing interface standard,” Tech. Rep., University of Tennessee, Knoxville, Tenn, USA, 1994. View at Google Scholar
  55. Q. Zou, X.-B. Li, W.-R. Jiang, Z.-Y. Lin, G.-L. Li, and K. Chen, “Survey of MapReduce frame operation in bioinformatics,” Briefings in Bioinformatics, 2013. View at Publisher · View at Google Scholar
  56. N. Malod-Dognin and N. Pržulj, “GR-Align: fast and flexible alignment of protein 3D structures using graphlet degree similarity,” Bioinformatics, vol. 30, no. 9, pp. 1259–1265, 2014. View at Publisher · View at Google Scholar · View at Scopus
  57. W. Xie and N. V. Sahinidis, “A reduction-based exact algorithm for the contact map overlap problem,” Journal of Computational Biology, vol. 14, no. 5, pp. 637–654, 2007. View at Publisher · View at Google Scholar · View at Scopus
  58. L. P. Chew and K. Kedem, “Finding the consensus shape for a protein family (extended abstract),” in Proceedings of the 18th ACM Annual Symposium on Computational Geometry (SCG '02), pp. 64–73, ACM, Barcelona, Spain, June 2002. View at Publisher · View at Google Scholar
  59. D. Fischer, A. Elofsson, D. Rice, and D. Eisenberg, “Assessing the performance of fold recognition methods by means of a comprehensive benchmark,” in Proceedings of the Pacific Symposium on Biocomputing, pp. 300–318, January 1996.
  60. B. Rost and C. Sander, “Prediction of protein secondary structure at better than 70% accuracy,” Journal of Molecular Biology, vol. 232, no. 2, pp. 584–599, 1993. View at Publisher · View at Google Scholar · View at Scopus
  61. A. Caprara, R. D. Carr, S. Istrail, G. Lancia, and B. Walenz, “1001 Optimal PDB structure alignments: integer programming methods for finding the maximum contact map overlap,” Journal of Computational Biology, vol. 11, no. 1, pp. 27–52, 2004. View at Publisher · View at Google Scholar
  62. R. Andonov, N. Yanev, and N. Malod-Dognin, “An efficient lagrangian relaxation for the contact map overlap problem,” in Algorithms in Bioinformatics: 8th International Workshop, WABI 2008, Karlsruhe, Germany, September 15–19, 2008. Proceedings, K. A. Crandall and J. Lagergren, Eds., vol. 5251 of Lecture Notes in Computer Science, pp. 162–173, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar