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Data Mining in Protein Binding Cavities

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Classification — the Ubiquitous Challenge

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Abstract

The molecular function of a protein is coupled to the binding of a substrate or an endogenous ligand to a well defined binding cavity. To detect functional relationships among proteins, their binding-site exposed physicochemical characteristics were described by assigning generic pseudocenters to the functional groups of the amino acids flanking a particular active site. These pseudocenters were assembled into small substructures and their spatial similarity with appropriate chemical properties was examined. If two substructures of two binding cavities are found to be similar, they form the basis for an expanded comparison of the complete cavities. Preliminary tests indicate the benefit of this method and motivate further studies.

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References

  • ALTSCHUL, S.F., GISH, W., MILLER, W., MYERS, E.W. and LIPMAN, D.J. (1990): Basic local alignment search tool. J. Mol. Biol., 215, 403–410.

    Article  Google Scholar 

  • ARTYMIUK, P.J., GRINDLEY, H.M., RICE, D.W. and WILLETT, P. (1993): Identification of tertiary structure resemblance in proteins using a maximal common subgraph isomorphism algorithm. J. Mol. Biol., 229, 707–721.

    Article  Google Scholar 

  • ARTYMIUK, P.J., SPRIGGS, R.V. and WILLETT, P. (2003): Searching for patterns of amino acids in 3D protein structures. J. Chem. Inf. Comput. Sci., 43, 412–421.

    Article  Google Scholar 

  • BACHAR, O., FISCHER, D., NUSSINOV, R. and WOLFSON, H. (1993): A computer vision based technique for 3-D sequence-independent structural comparison of proteins. Protein Eng., 6, 279–288.

    Google Scholar 

  • BAIROCH, A. (2000): The ENZYME database in 2000. Nucleic Acids Res., 28, 304–305.

    Article  Google Scholar 

  • GIBRAT, J.F., MADEJ, T. and BRYANT, S.H. (1996): Surprising similarities in structure comparison. Curr. Opin. Struct. Biol., 6, 377–385.

    Article  Google Scholar 

  • HAMELRYCK, T. (2003): Efficient identification of side-chain patterns using a multidimensional index tree. Proteins, 51, 96–108.

    Article  Google Scholar 

  • HOLM, L. and SANDER, C. (1996): The FSSP database: fold classification based on structure-structure alignment of proteins. Nucleic Acids Res., 24, 206–209.

    Article  Google Scholar 

  • HOLM, S. (1998): Touring protein fold space with Dali/FSSP.

    Google Scholar 

  • KINOSHITA, K. and NAKAMURA, H. (2003): Identification of protein biochemical functions by similarity search using the molecular surface database eF-site. Protein Sci., 12, 1589–1595.

    Article  Google Scholar 

  • KLEYWEYGT, G.J. (1999): Recognition of spatial motifs in protein structures. J. Mol. Biol., 285, 1887–97.

    Article  Google Scholar 

  • LEHTONEN, J. V., DENESSIOUK, K., MAY, A. C. and JOHNSON, M.S. (1999): Finding local structural similarities among families of unrelated protein structures: a generic nonlinear alignment algorithm. Proteins, 34, 341–355.

    Article  Google Scholar 

  • LO CONTE, L., BRENNER, S.E., HUBBARD, T.J., CHOTHIA, C. and MURZIN, A. G. (2002): SCOP database in 2002: refinements accommodate structural genomics. Nucleic Acids Res., 30, 264–267.

    Article  Google Scholar 

  • MURZIN, A.G., BRENNER, S.E., HUBHARD, T. and CHOTHIA, C. (1995): SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247, 536–540.

    Article  Google Scholar 

  • NEEDLEMAN, S.B. and WUNSCH, C.D. (1970): A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol., 48, 443–453.

    Article  Google Scholar 

  • ORENGO, C.A., MICHIE, A.D., JONES, S., JONES, D.T., SWINDELLS, M.B. and THORNTON, J.M. (1997): CATH-a hierarchic classification of protein domain structures. Structure, 5, 1093–1108.

    Article  Google Scholar 

  • ORENGO, C.A., PEARL, F.M., LEE, D., BRAY, J.E., SILLITOE, I., TODD, A.E., HARRISON, A.P. and THORNTON, J.M. (2000): Assigning genomic sequences to CATH. Nucleic Acids Res, 28, 277–82.

    Article  Google Scholar 

  • PEARSON, W.R. and LIPMAN, D.J. (1988): Improved tools for biological sequence comparison. Proc Natl Acad Sci U S A, 85, 2444–2448.

    Google Scholar 

  • PEARSON, W.R. (1990): Rapid and sensitive sequence comparison with FASTP and FASTA. Methods Enzymol, 183, 63-98.

    Google Scholar 

  • POIRETTE, A.R., ARTYMIUK, P.J., RICE, D.W. and WILLETT, P. (1997): Comparison of protein surfaces using a genetic algorithm. J. Comput. Aided Mol. Des., 11, 557–569.

    Article  Google Scholar 

  • PONTING, C.P. and RUSSELL, R.R. (2002): The natural history of protein domains. Annu. Rev. Biophys. Biomol. Struct., 31, 45–71.

    Article  Google Scholar 

  • ROSEN, M., LIN, S.L., WOLFSON, H. and NUSSINOV, R. (1998): Molecular shape comparisons in searches for active sites and functional similarity. Protein Eng., 11, 263–277.

    Article  Google Scholar 

  • RUSSELL, R.B. (1998): Detection of protein three-dimensional side-chain patterns: new examples of convergent evolution. J. Mol. Biol., 279, 1211–1227.

    Article  Google Scholar 

  • SCHMITT, S., KUHN, D. and KLEBE, G. (2002): A new method to detect related function among proteins independent of sequence and fold homology. J. Mol. Biol., 323, 387–406.

    Article  Google Scholar 

  • SCHOMBURG, I., CHANG, A. and SCHOMBURG, D. (2002): BRENDA, enzyme data and metabolic information. Nucleic Acids. Res., 30, 47–49.

    Article  Google Scholar 

  • SIEMON, R. (2001): Einige Werkzeuge zum Einsatz von selbstorganisierenden Neuronalen Netzen zur Strukturanalyse von Wirkstoff-Rezeptoren. Diplomarbeit, 13.2.2001, FB Mathematik u. Informatik.

    Google Scholar 

  • STARK, A., SUNYAEV, S. and RUSSELL, R.B. (2003): A model for statistical significance of local similarities in structure. J. Mol. Biol., 326, 1307–1316.

    Article  Google Scholar 

  • STARK, A. and RUSSELL, R.B. (2003): Annotation in three dimensions. PINTS: Patterns in Non-homologous Tertiary Structures. Nucleic Acids Res., 31, 3341–3344.

    Article  Google Scholar 

  • WALLACE, A.C., LASKOWSKI, R.A. and THORNTON, J.M. (1996): Derivation of 3D coordinate templates for searching structural databases: application to Ser-His-Asp catalytic triads in the serine proteinases and lipases. Protein Sci., 5, 1001–1013.

    Article  Google Scholar 

  • WALLACE, A.C., BORKAKOTI, N. and THORNTON, J.M. (1997): TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites. Protein Sci., 6, 2308–2323.

    Article  Google Scholar 

  • WANGIKAR, P.P., TENDULKAR, A.V., RAMYA, S., MALI, D.N. and SARAWAGI, S. (2003): Functional sites in protein families uncovered via an objective and automated graph theoretic approach. J. Mol. Biol., 326, 955–978.

    Article  Google Scholar 

  • WATERMAN, M.S. (1984): General methods for sequence comparison. Bull. Math. Biol., 46, 473–500.

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Data Bionics Research Group, University of Marburg, D-35032, Marburg, Germany

    Katrin Kupas & Alfred Ultsch

Authors
  1. Katrin Kupas
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  2. Alfred Ultsch
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Editor information

Editors and Affiliations

  1. Fachbereich Statistik, Universität Dortmund, 44221, Dortmund

    Claus Weihs

  2. Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), 76128, Karlsruhe

    Wolfgang Gaul

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© 2005 Springer-Verlag Berlin · Heidelberg

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Kupas, K., Ultsch, A. (2005). Data Mining in Protein Binding Cavities. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_40

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