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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40223

Title: Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate binding modules
Authors: Chou WY;Chou WI;Pai TW;Lin SC;Jiang TY;Tang CY;Chang MD.
Contributors: 國立臺灣海洋大學:資訊工程學系
Date: 2010
Issue Date: 2017-01-16T06:29:34Z
Publisher: Bioinformatics
Abstract: Abstract: Carbohydrate-binding modules (CBMs) share similar secondary and tertiary topology, but their primary sequence identity is low. Computational identification of ligand-binding residues allows biologists to better understand the protein-carbohydrate binding mechanism. In general, functional characterization can be alternatively solved by alignment-based manners. As alignment accuracy based on conventional methods is often sensitive to sequence identity, low sequence identity among query sequences makes it difficult to precisely locate small portions of relevant features. Therefore, we propose a feature-incorporated alignment (FIA) to flexibly align conserved signatures in CBMs. Then, an FIA-based target-template prediction model was further implemented to identify functional ligand-binding residues. RESULTS: Arabidopsis thaliana CBM45 and CBM53 were used to validate the FIA-based prediction model. The predicted ligand-binding residues residing on the surface in the hypothetical structures were verified to be ligand-binding residues. In the absence of 3D structural information, FIA demonstrated significant improvement in the estimation of sequence similarity and identity for a total of 808 sequences from 11 different CBM families as compared with six leading tools by Friedman rank test
Relation: 26(8)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40223
Appears in Collections:[資訊工程學系] 期刊論文

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