Abstract
A novel data mining approach for similarity search and knowledge discovery in protein structure databases is proposed. PADS (Protein structure Alignment by Directional shape Signatures) incorporates the three dimensional coordinates of the main atoms of each amino acid and extracts a geometrical shape signature along with the direction of each amino acid. As a result, each protein structure is presented by a series of multidimensional feature vectors representing local geometry, shape, direction, and biological properties of its amino acid molecules. Furthermore, a distance matrix is calculated and is incorporated into a local alignment dynamic programming algorithm to find the similar portions of two given protein structures followed by a sequence alignment step for more efficient filtration. The optimal superimposition of the detected similar regions is used to assess the quality of the results. The proposed algorithm is fast and accurate and hence could be used for analysis and knowledge discovery in large protein structures. The method has been compared with the results from CE, DALI, and CTSS using a representative sample of PDB structures. Several new structures not detected by other methods are detected.
This research was supported by the NSF grants under CNF-04-23336, IIS02-23022, IIS02-09112, and EIA00-80134.
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Aghili, S.A., Agrawal, D., El Abbadi, A. (2005). PADS: Protein Structure Alignment Using Directional Shape Signatures. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_5
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DOI: https://doi.org/10.1007/11408079_5
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