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Predicting Flavonoid UGT Regioselectivity
Advances in Bioinformatics (2011)
  • Arthur R. Jackson
  • Debra Knisley, East Tennessee State University
  • Cecilia McIntosh, East Tennessee State University
  • Phillip Pfeiffer, East Tennessee State University
Abstract
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.
Publication Date
2011
DOI
10.1155/2011/506583
Publisher Statement
Copyright © 2011 Rhydon Jackson et al. The following open access article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.  

Citation Information
Jackson, A. R., Knisley, D., McIntosh, C., & Pfeiffer, P. (2011). Predicting Flavonoid UGT Regioselectivity. Advances in Bioinformatics, 2011(Article ID 506583), 1-15. http://dx.doi.org/10.1155/2011/506583
Creative Commons license
Creative Commons License
This work is licensed under a Creative Commons CC_BY International License.