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Advances in Bioinformatics
Volume 2011, Article ID 506583, 15 pages
Research Article

Predicting Flavonoid UGT Regioselectivity

1Technology Division, Momentx Corporation, Plano, TX 75024-3106, USA
2Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614, USA
3Department of Biological Sciences, East Tennessee State University, Johnson City, TN 37614, USA
4Department of Computer and Information Sciences, East Tennessee State University, Johnson City, TN 37614, USA

Received 15 September 2010; Revised 6 March 2011; Accepted 18 April 2011

Academic Editor: Alvis Brazma

Copyright © 2011 Rhydon Jackson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities.