Abstract
Similarity search for protein 3D structure databases is much more complex and computationally expensive than sequence comparison. In most cases, existing comparison systems such as DALI and VAST do not provide search results on time. Therefore, we propose a fast filtering method for structural similarity search considering spatial features and chemical properties of protein. In our approach, the structural similarity search composes of a filter step to generate small cand idate set and a refinement step to compute structural alignment. This paper describes discovery of spatial topological patterns of SSEs in 3D space and the construction of histograms for chemical properties. To improve the accuracy of the filter step, we combine similarity of hist ograms on chemical properties using Euclidean form distance function together with the similarity of topological patterns. Our system is fully implemented using Oracle 8i spatial. Experimental results show that our method is approximately three times faster than DALI.
This work was supported by KISTEP, University IT Research Center for Project, and the RRC of MOST and KOSEF in Korea.
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Park, SH., Ryu, K.H. (2004). Effective Filtering for Structural Similarity Search in Protein 3D Structure Databases. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2004. Lecture Notes in Computer Science, vol 3180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30075-5_73
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DOI: https://doi.org/10.1007/978-3-540-30075-5_73
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