Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.

Carbohydrate binding modules (CBMs) are found in polysaccharide-targeting enzymes and increase catalytic efficiency. Because only a relatively small number of CBM structures have been solved, computational modeling represents an alternative approach in conjunction with experimental assessment of CBM...

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Main Authors: Wei-Yao Chou, Tun-Wen Pai, Ting-Ying Jiang, Wei-I Chou, Chuan-Yi Tang, Margaret Dah-Tsyr Chang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2011-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3178555?pdf=render
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spelling doaj-c555373923414f86bfcb6325965387142020-11-24T21:26:37ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0169e2481410.1371/journal.pone.0024814Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.Wei-Yao ChouTun-Wen PaiTing-Ying JiangWei-I ChouChuan-Yi TangMargaret Dah-Tsyr ChangCarbohydrate binding modules (CBMs) are found in polysaccharide-targeting enzymes and increase catalytic efficiency. Because only a relatively small number of CBM structures have been solved, computational modeling represents an alternative approach in conjunction with experimental assessment of CBM functionality and ligand-binding properties. An accurate target-template sequence alignment is the crucial step during homology modeling. However, low sequence identities between target/template sequences can be a major bottleneck. We therefore incorporated the predicted hydrophilic aromatic residues (HARs) and secondary structure elements into our feature-incorporated alignment (FIA) algorithm to increase CBM alignment accuracy. An alignment performance comparison for FIA and six others was made, and the greatest average sequence identities and similarities were achieved by FIA. In addition, structure models were built for 817 representative CBMs. Our models possessed the smallest average surface-potential z scores. Besides, a large true positive value for liagnd-binding aromatic residue prediction was obtained by HAR identification. Finally, the pre-simulated CBM structures have been deposited in the Database of Simulated CBM structures (DS-CBMs). The web service is publicly available at http://dscbm.life.nthu.edu.tw/ and http://dscbm.cs.ntou.edu.tw/.http://europepmc.org/articles/PMC3178555?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Wei-Yao Chou
Tun-Wen Pai
Ting-Ying Jiang
Wei-I Chou
Chuan-Yi Tang
Margaret Dah-Tsyr Chang
spellingShingle Wei-Yao Chou
Tun-Wen Pai
Ting-Ying Jiang
Wei-I Chou
Chuan-Yi Tang
Margaret Dah-Tsyr Chang
Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.
PLoS ONE
author_facet Wei-Yao Chou
Tun-Wen Pai
Ting-Ying Jiang
Wei-I Chou
Chuan-Yi Tang
Margaret Dah-Tsyr Chang
author_sort Wei-Yao Chou
title Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.
title_short Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.
title_full Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.
title_fullStr Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.
title_full_unstemmed Hydrophilic aromatic residue and in silico structure for carbohydrate binding module.
title_sort hydrophilic aromatic residue and in silico structure for carbohydrate binding module.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2011-01-01
description Carbohydrate binding modules (CBMs) are found in polysaccharide-targeting enzymes and increase catalytic efficiency. Because only a relatively small number of CBM structures have been solved, computational modeling represents an alternative approach in conjunction with experimental assessment of CBM functionality and ligand-binding properties. An accurate target-template sequence alignment is the crucial step during homology modeling. However, low sequence identities between target/template sequences can be a major bottleneck. We therefore incorporated the predicted hydrophilic aromatic residues (HARs) and secondary structure elements into our feature-incorporated alignment (FIA) algorithm to increase CBM alignment accuracy. An alignment performance comparison for FIA and six others was made, and the greatest average sequence identities and similarities were achieved by FIA. In addition, structure models were built for 817 representative CBMs. Our models possessed the smallest average surface-potential z scores. Besides, a large true positive value for liagnd-binding aromatic residue prediction was obtained by HAR identification. Finally, the pre-simulated CBM structures have been deposited in the Database of Simulated CBM structures (DS-CBMs). The web service is publicly available at http://dscbm.life.nthu.edu.tw/ and http://dscbm.cs.ntou.edu.tw/.
url http://europepmc.org/articles/PMC3178555?pdf=render
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AT weiichou hydrophilicaromaticresidueandinsilicostructureforcarbohydratebindingmodule
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