The presently available methods for the one-to-one docking problem are not well suited for the one-to-many docking problem for technical as well as quality reasons. In particular, there is a lack of efficiency when applying the methods to large protein databases. Therefore, new techniques to support the docking search are required. The project aims at the development of efficient methods for similarity search in protein database systems which are applicable as a filter step for the one-to-many docking prediction. As a new approach, we developed the approximation-based similarity measure for 3-D surface segments and successfully applied the model to docking segments in a protein database. For the efficient processing of similarity queries, we identified the ellipsoid query as a new query type which provides a wide and flexible applicability for similarity search in protein database systems. Our new algorithms support efficient processing of ellipsoid queries based on multidimensional index structures. In particular, applied to the filter step of one-to-many protein docking, promising results are obtained. Additional applications of the methods range from large molecular databases to multimedia and CAD database systems. (orig.)SIGLEAvailable from TIB Hannover: DtF QN1(58,34) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman
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