You are here:

About Us

Project Description
Citation If you want to use results from the ProCKSI server, please cite/acknowledge the main ProCKSI reference and each Similarity Comparison Method, Additional Source of Information or Analysis and Visualisation Tool that you might have used, e.g. for producing the consensus similarity, as follows:
  1. Main References: ProCKSI
    ProCKSI: a decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information
    D. Barthel, J.D. Hirst, J. Blazewicz, E.K. Burke and N. Krasnogor, BMC Bioinformatics, 8, 416, 2007.
  2. Similarity Comparison Method: USM
    Measuring the Similarity of Protein Structures by Means of the Universal Similarity Metric
    N. Krasnogor and D. A. Pelta. Bioinformatics 20(7), 1015-1021, 2004.
  3. Similarity Comparison Method: MaxCMO
    A simple and fast heuristic for protein structure comparison
    D. A. Pelta, J. R. Gonzalez, M. Moreno Vega. BMC Bioinformatics 9, 161, 2008.
  4. Similarity Comparison Method: DaliLite
    DaliLite workbench for protein structure comparison
    L. Holm and J. Park, Bioinformatics 16, 566-567, 2000.
  5. Similarity Comparison Method: CE
    Protein structure alignment by incremental combinatorial extension (CE) of the optimal path
    I. N. Shindyalov and P. E. Bourne, Protein Engineering 11, 739-747, 1998.
  6. Similarity Comparison Method: TM-align
    TM-align: A protein structure alignment algorithm based on TM-score
    Y. Zhang and J. Skolnick, Nucleic Acids Research 33, 2302-2309, 2005.
  7. Similarity Comparison Method: FAST
    FAST: A Novel Protein Structure Alignment Algorithm
    J. Zhu and Y. Weng, Proteins: Structure, Function and Bioinformatics 14, 417-423, 2005.
  8. Similarity Comparison Method: Vorolign
    Vorolign - Fast Structural Alignment using Voronoi Contacts
    F. Birzele, J. E. Gewehr, G. Csaba and R. Zimmer, Bioinformatics 23, e205-e211, 2007.
  9. Similarity Comparison Method: URMS
    The URMS-RMS hybrid algorithm for fast and sensitive local protein structure alignment
    G. Yona and K. Kedem, Journal of Computational Biology 12, 12-32, 2005.
  10. Additional Source of Information: PDB
    The Protein Data Bank
    H. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat, H. Weissig, I. Shindyalov, P. Bourne, Nucleic Acids Res. 28, 235–242, 2000.
  11. Additional Source of Information: CATH
    CATH - A Hierarchic Classification of Protein Domain Structures
    C.A. Orengo, A.D. Michie, S. Jones, D.T. Jones, M.B. Swindells, J.M. Thornton, Structure 5, 1093–1108, 1997.
  12. Additional Source of Information: SCOP
    SCOP: a Structural Classification of Proteins database
    T.J. Hubbard, B. Ailey, S.E. Brenner, A.G. Murzin, C. Chothia, Nucleic Acids Research 27, 254–256, 1999.
  13. Additional Source of Information: iHOP
    A Gene Network for Navigating the Literature
    R. Hoffmann and A. Valencia, Nature Genetics 36, 664-664, 2004.
  14. Analysis and Visualisation Tools: Hierarchical Tree Visualisation
    Visualizing large hierarchical clusters in hyperbolic space
    J. Bingham, S. Sudarsanam, Bioinformatics 16(7), 660-661, 2000.
  15. Analysis and Visualisation Tools: Protein Structure Images
    MOLSCRIPT: A Program to Produce Both Detailed and Schematic Plots of Protein Structures
    Per J. Kraulis, J. Appl. Cryst. 24, 946-950, 1991.
  16. Analysis and Visualisation Tools: Clustering
    QCLUST V0.2 John Brzustowski, University of Alberta, United States of America.
Further readings on similarity comparison of proteins, and further literature and presentations on ProCKSI can be found in our Wiki.
External Software and Resources ProCKSI integrates a variety of external software and resources. All external links open a new window.
Acknowledgements We would like to thank the following students for their valuable contributions (in alphabetical order): The web design was created by the following collaborators:

Last but not least, we would like to thank the Technical Service Group of the School of Computer Science, University of Nottingham for their assistance: Nick Reynolds, William Armitage and Viktor Huddleston, and Chris Ritson from the School of Computing Science, Newcastle University for his help with the cluster migration.