CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011 vol.8 Issue No.01 - January-February
Issue No.01 - January-February (2011 vol.8)
Dukka B. KC , University of North Carolina at Charlotte, Charlotte
Dennis R. Livesay , Univeristy of North Carolina at Charlotte, Charlotte
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.60
Prediction of protein functional sites from sequence-derived data remains an open bioinformatics problem. We have developed a phylogenetic motif (PM) functional site prediction approach that identifies functional sites from alignment fragments that parallel the evolutionary patterns of the family. In our approach, PMs are identified by comparing tree topologies of each alignment fragment to that of the complete phylogeny. Herein, we bypass the phylogenetic reconstruction step and identify PMs directly from distance matrix comparisons. In order to optimize the new algorithm, we consider three different distance matrices and 13 different matrix similarity scores. We assess the performance of the various approaches on a structurally nonredundant data set that includes three types of functional site definitions. Without exception, the predictive power of the original approach outperforms the distance matrix variants. While the distance matrix methods fail to improve upon the original approach, our results are important because they clearly demonstrate that the improved predictive power is based on the topological comparisons. Meaning that phylogenetic trees are a straightforward, yet powerful way to improve functional site prediction accuracy. While complementary studies have shown that topology improves predictions of protein-protein interactions, this report represents the first demonstration that trees improve functional site predictions as well.
Phylogenetic motif, functional site prediction, phylogenetic tree, distance matrix.
Dukka B. KC, Dennis R. Livesay, "Topology Improves Phylogenetic Motif Functional Site Predictions", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 1, pp. 226-233, January-February 2011, doi:10.1109/TCBB.2009.60