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Predicting Flavonoid UGT Regioselectivity

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 e...

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Detalles Bibliográficos
Autores principales: Jackson, Rhydon, Knisley, Debra, McIntosh, Cecilia, Pfeiffer, Phillip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130495/
https://www.ncbi.nlm.nih.gov/pubmed/21747849
http://dx.doi.org/10.1155/2011/506583
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author Jackson, Rhydon
Knisley, Debra
McIntosh, Cecilia
Pfeiffer, Phillip
author_facet Jackson, Rhydon
Knisley, Debra
McIntosh, Cecilia
Pfeiffer, Phillip
author_sort Jackson, Rhydon
collection PubMed
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.
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spelling pubmed-31304952011-07-11 Predicting Flavonoid UGT Regioselectivity Jackson, Rhydon Knisley, Debra McIntosh, Cecilia Pfeiffer, Phillip Adv Bioinformatics Research Article 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. Hindawi Publishing Corporation 2011 2011-06-30 /pmc/articles/PMC3130495/ /pubmed/21747849 http://dx.doi.org/10.1155/2011/506583 Text en Copyright © 2011 Rhydon Jackson et al. https://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Research Article
Jackson, Rhydon
Knisley, Debra
McIntosh, Cecilia
Pfeiffer, Phillip
Predicting Flavonoid UGT Regioselectivity
title Predicting Flavonoid UGT Regioselectivity
title_full Predicting Flavonoid UGT Regioselectivity
title_fullStr Predicting Flavonoid UGT Regioselectivity
title_full_unstemmed Predicting Flavonoid UGT Regioselectivity
title_short Predicting Flavonoid UGT Regioselectivity
title_sort predicting flavonoid ugt regioselectivity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130495/
https://www.ncbi.nlm.nih.gov/pubmed/21747849
http://dx.doi.org/10.1155/2011/506583
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