The volume of unstructured databases grows extremely whereas its annotation is problematic. The similarity search concept based on a similarity function defined for each pair of database objects is more suitable for this kind of data. The similarity is usually modelled by a distance function satisfying metric axioms, which allows efficient indexing. However, metric axioms can be very restrictive for domain experts who may prefer non-metric functions. Hence database experts have to solve this problem by converting non-metric functions to metric ones or by developing new types of non-metric indexing methods.
One of the areas where metric/non-metric similarity searching is used is computational proteomics. During the determination of the biological function of an "unknown" protein, retrieval of "known" proteins with similar structures (and thus probably with similar function) is very useful. Moreover, fast and cheap determination of protein structures is also an open problem. From database point of view, it is possible to use databases of known protein structures to address this problem. In this approach, sequence-structure similarity functions are used to obtain structures that can be similar to the searched structure of the protein.
Our goal is developing high-quality structure and sequence-structure similarity functions and methods for their indexing.