Document Type
Article
Publication Date
9-16-2011
Description
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Citation Information
Jackson, Rhydon; Knisley, Debra; McIntosh, Cecilia; and Pfeiffer, Phillip. 2011. Predicting Flavonoid UGT Regioselectivity. Advances in Bioinformatics. Vol.2011 https://doi.org/10.1155/2011/506583 ISSN: 1687-8027
Copyright Statement
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.