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Title: Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules
Authors: Wei-Yao Chou;Wei-I Chou;Tun-Wen Pai;Shu-Chuan Lin;Ting-Ying Jiang;Chuan-Yi Tang;Margaret Dah-Tsyr Chang
Contributors: NTOU:Department of Computer Science and Engineering
Date: 2010-02
Issue Date: 2011-10-21T02:34:09Z
Publisher: Bioinformatics
Abstract: Abstract:Motivation: 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 targettemplate 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 three dimensional 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), pp.1022-1028
Appears in Collections:[Department of Computer Science and Engineering] Periodical Articles

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