Mining Geometrical Motifs Co-occurrences in the CMS Dataset

  • Mirto MusciEmail author
  • Marco Ferretti
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 903)


Precise and efficient retrieval of structural motifs is a task of great interest in proteomics. Geometrical approaches to motif identification allow the retrieval of unknown motifs in unfamiliar proteins that may be missed by widespread topological algorithms. In particular, the Cross Motif Search (CMS) algorithm analyzes pairs of proteins and retrieves every group of secondary structure elements that is similar between the two proteins. These similarities are candidate to be structural motifs. When extended to large datasets, the exhaustive approach of CMS generates a huge volume of data. Mining the output of CMS means identifying the most significant candidate motifs proposed by the algorithm, in order to determine their biological significance. In the literature, effective data mining on a CMS dataset is an unsolved problem.

In this paper, we propose a heuristic approach based on what we call protein “co-occurrences” to guide data mining on the CMS dataset. Preliminary results show that the proposed implementation is computationally efficient and is able to select only a small subset of significant motifs.


Proteins Secondary structure Geometrical motifs Cross Motif Search Data mining 


  1. 1.
    Ferretti, M., Musci, M.: Geometrical motifs search in proteins: a parallel approach. Parallel Comput. 42, 60–74 (2015)CrossRefGoogle Scholar
  2. 2.
    Cantoni, V., et al.: Protein motif retrieval by secondary structure element geometry and biological features saliency. In: 25th International Workshop on Biological Knowledge Discovery and Data Mining (BIOKDD 2014), pp. 23–26 (2014)Google Scholar
  3. 3.
    Cantoni, V., Ferretti, M., Musci, M., Nugrahaningsih, N.: Structural motifs identification and retrieval: a geometrical approach. In: Pattern Recognition in Computational Molecular Biology: Techniques and Approaches, 1st edn. Wiley, Hoboken (2016)Google Scholar
  4. 4.
    Drago, G., Ferretti, M., Musci, M.: CCMS: a greedy approach to motif extraction. In: Petrosino, A., Maddalena, L., Pala, P. (eds.) ICIAP 2013. LNCS, vol. 8158, pp. 363–371. Springer, Heidelberg (2013). Scholar
  5. 5.
    Argentieri, T., Cantoni, V., Musci, M.: Extending cross motif search with heuristic data mining. In: 28th International Workshop on Biological Knowledge Discovery and Data Mining (BIOKDD 2017) (2017)Google Scholar
  6. 6.
    Ferretti, M., Musci, M.: Entire motifs search of secondary structures in proteins: a parallelization study. In: Proceedings of the 20th European MPI Users’ Group Meeting, New York, NY, USA, pp. 199–204 (2013)Google Scholar
  7. 7.
    Ferretti, M., Musci, M., Santangelo, L.: MPI-CMS: a hybrid parallel approach to geometrical motif search in proteins. Concurr. Comput. Pract. Exp. 27(18), 5500–5516 (2015)CrossRefGoogle Scholar
  8. 8.
    Ferretti, M., Musci, M., Santangelo, L.: A hybrid OpenMP and OpenMPI approach to geometrical motif search in proteins. In: IEEE International Conference on Cluster Computing (IEEE Cluster 2014), pp. 298–304 (2014)Google Scholar
  9. 9.
    Argentier, T., Cantoni, V., Musci, M.: Motifvisualizer: a interdisciplinary GUI for geometrical motif retrieval in proteins. In: Biological Knowledge Discovery and Data Mining - 27th International Conference on Database and Expert Systems Applications (2016)Google Scholar
  10. 10.
    Holm, L., Sander, C.: Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233, 123–138 (1993)CrossRefGoogle Scholar
  11. 11.
    Krissinel, E., Henrik, K.: Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallogr. D Biol. Crystallogr. 60, 2256–2268 (2004)CrossRefGoogle Scholar
  12. 12.
    Dror, H., Nussinov, B.R., Wolfson, H.: Mass: multiple structural alignment by secondary structures. Bioinformatics 19(1), i95–i104 (2003)CrossRefGoogle Scholar
  13. 13.
    Hutchinson, E.G., Thornton, J.M.: PROMOTIF—a program to identify and analyze structural motifs in proteins. Protein Sci. 5(2), 212–220 (1996)CrossRefGoogle Scholar
  14. 14.
    Shi, S., Chitturi, B., Grishin, N.V.: ProSMoS server: a pattern-based search using interaction matrix representation of protein structures. Nucleic Acids Res. 37(Web Server issue), W526–W531 (2009)CrossRefGoogle Scholar
  15. 15.
    Kumar, S., Nei, M., Dudley, J., Tamura, K.: MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. Brief Bioinform. 9(4), 299–306 (2008)CrossRefGoogle Scholar
  16. 16.
    Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577–2637 (1983)CrossRefGoogle Scholar
  17. 17.
    Touw, W.G., et al.: A series of PDB related databases for everyday needs. Nucleic Acids Res. 43(Database issue), D364–D368.
  18. 18.
    Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: Scop: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247, 536–540 (1995)Google Scholar
  19. 19.
    Hubbard, T.J.P., Murzin, A.G., Brenner, S.E., Chothia, C.: Scop: a structural classification of proteins database. Nucleic Acids Res. 25, 236–239 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringUniversity of PaviaPaviaItaly

Personalised recommendations