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Issue No.01 - January/February (2012 vol.9)
pp: 249-261
Hong Sun , The Ohio State University, Columbus
Ahmet Sacan , Drexel University, Philadelphia
Hakan Ferhatosmanoglu , The Ohio State University, Columbus
Yusu Wang , The Ohio State University, Columbus
Availability of an effective tool for protein multiple structural alignment (MSTA) is essential for discovery and analysis of biologically significant structural motifs that can help solve functional annotation and drug design problems. Existing MSTA methods collect residue correspondences mostly through pairwise comparison of consecutive fragments, which can lead to suboptimal alignments, especially when the similarity among the proteins is low. We introduce a novel strategy based on: building a contact-window based motif library from the protein structural data, discovery and extension of common alignment seeds from this library, and optimal superimposition of multiple structures according to these alignment seeds by an enhanced partial order curve comparison method. The ability of our strategy to detect multiple correspondences simultaneously, to catch alignments globally, and to support flexible alignments, endorse a sensitive and robust automated algorithm that can expose similarities among protein structures even under low similarity conditions. Our method yields better alignment results compared to other popular MSTA methods, on several protein structure data sets that span various structural folds and represent different protein similarity levels. A web-based alignment tool, a downloadable executable, and detailed alignment results for the data sets used here are available at http://sacan.biomed. and
Proteins, Protein engineering, Libraries, Measurement uncertainty, Sun, Educational institutions, Computer science,HOMSTRAD., Protein structure, multiple structure alignment, partial order curve comparison, structural motif library, secondary structure elements (SSE), distance map, contact map
Hong Sun, Ahmet Sacan, Hakan Ferhatosmanoglu, Yusu Wang, "Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 1, pp. 249-261, January/February 2012, doi:10.1109/TCBB.2011.67
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