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Issue No.03 - May/June (2011 vol.8)
pp: 819-831
Jayendra Gnanaskandan Venkateswaran , University of Florida, Gainesville
Bin Song , University of Florida, Gainesville
Tamer Kahveci , University of Florida, Gainesville
Christopher Jermaine , University of Florida, Gainesville
Finding structural similarities in distantly related proteins can reveal functional relationships that can not be identified using sequence comparison. Given two proteins A and B and threshold \epsilon Å, we develop an algorithm, TRiplet-based Iterative ALignment (TRIAL) for computing the transformation of B that maximizes the number of aligned residues such that the root mean square deviation (RMSD) of the alignment is at most \epsilon Å. Our algorithm is designed with the specific goal of effectively handling proteins with low similarity in primary structure, where existing algorithms perform particularly poorly. Experiments show that our method outperforms existing methods. TRIAL alignment brings the secondary structures of distantly related proteins to similar orientations. It also finds larger number of secondary structure matches at lower RMSD values and increased overall alignment lengths. Its classification accuracy is up to 63 percent better than other methods, including CE and DALI. TRIAL successfully aligns 83 percent of the residues from the smaller protein in reasonable time while other methods align only 29 to 65 percent of the residues for the same set of proteins.
Protein structure, tertiary structure, alignment.
Jayendra Gnanaskandan Venkateswaran, Bin Song, Tamer Kahveci, Christopher Jermaine, "TRIAL: A Tool for Finding Distant Structural Similarities", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 3, pp. 819-831, May/June 2011, doi:10.1109/TCBB.2009.28
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