A Branch and Bound Algorithm for Matching Protein Structures

  • Janez Konc
  • Dušanka Janežič
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)


An efficient branch and bound algorithm for matching protein structures has been developed. The compared protein structures are represented as graphs and a product graph of these graphs is calculated. The resulting product graph is then the input to our algorithm. A maximum clique in the product graph corresponds to the maximum common substructure in the original graphs. Our algorithm, which gives an approximate solution to the maximum clique problem, is compared with exact algorithms commonly used in bioinformatics for protein structural comparisons. The computational results indicate that the new algorithm permits an efficient protein similarity calculation used for protein structure analysis and protein classification.


Maximum Clique Product Graph Maximum Clique Problem Large Clique Protein Structural Comparison 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schmitt, S., Kuhn, D., Klebe, G.: A new method to detect related function among proteins independent of sequence and fold homology. Journal of Molecular Biology 323, 387–406 (2002)CrossRefGoogle Scholar
  2. 2.
    Bron, C., Kerbosch, J.: Algorithm 457 - Finding all cliques of an undirected graph. Commun. ACM 16, 575–577 (1973)zbMATHCrossRefGoogle Scholar
  3. 3.
    Tomita, E., Seki, T.: An efficient branch-and-bound algorithm for finding a maximum clique. In: Calude, C.S., Dinneen, M.J., Vajnovszki, V. (eds.) DMTCS 2003. LNCS, vol. 2731, pp. 278–289. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A guide to the Theory of NP-Completeness. Freeman, San Francisco (1979)zbMATHGoogle Scholar
  5. 5.
    Östergård, P.R.J.: A fast algorithm for the maximum clique problem. Discrete Applied Mathematics 120, 197–207 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Pennec, X., Ayache, N.: A geometric algorithm to find small but highly similar 3D substructures in proteins. Bioinformatics 14, 516–522 (1998)CrossRefGoogle Scholar
  7. 7.
    Raymond, J.W., Willett, P.: Maximum common subgraph isomorphism algorithms for the matching of chemical structures. Journal of Computer-Aided Molecular Design 16, 521–533 (2002)CrossRefGoogle Scholar
  8. 8.
    Konc, J., Janežič, D.: A maximum clique problem revisited. European Journal of Operations Research (to appear)Google Scholar
  9. 9.
    Kabsch, W.: A discussion of the solution for the best rotation to relate two sets of vectors. Acta Cryst. A34, 827–828 (1978)Google Scholar
  10. 10.
    Konc, J., Hodošček, M., Janežič, D.: Molecular surface walk. Croat. Chem. Acta 79, 237–241 (2006)Google Scholar
  11. 11.
    Kristan, K., Krajnc, K., Konc, J., Stanislav, G., Stojan, J.: Phytoestrogens as inibitors of fungal 17β-hydroxysteroid dehydrogenase. Steroids 70, 626–635 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Janez Konc
    • 1
  • Dušanka Janežič
    • 1
  1. 1.National Institute of Chemistry, Hajdrihova 19, SI-1000 LjubljanaSlovenia

Personalised recommendations