Protein structure similarity is one of the most important aims pursued by bioinformatics and structural biology, nowadays. Although quite a few similarity methods have been proposed lately, yet fresh algorithms that fulfill new preconditions are needed to serve this purpose. In this paper, we provide a new similarity measure for 3D protein structures that detects not only similar structures but also similar substructures to a query protein, supporting both multiple and pairwise comparison procedures and combining many comparison characteristics. In order to handle similarity queries we utilize efficient and effective indexing techniques such as M-trees and we provide interesting results using real, previously tested protein data sets. © 2012 IFIP International Federation for Information Processing.
CITATION STYLE
Iakovidou, N., Tiakas, E., & Tsichlas, K. (2012). DISCO: A new algorithm for detecting 3D protein structure similarity. In IFIP Advances in Information and Communication Technology (Vol. 382 AICT, pp. 622–631). https://doi.org/10.1007/978-3-642-33412-2_64
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