Journal Article

Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules

Wei-Yao Chou, Wei-I Chou, Tun-Wen Pai, Shu-Chuan Lin, Ting-Ying Jiang, Chuan-Yi Tang and Margaret Dah-Tsyr Chang

in Bioinformatics

Volume 26, issue 8, pages 1022-1028
Published in print April 2010 | ISSN: 1367-4803
Published online February 2010 | e-ISSN: 1460-2059 | DOI:
Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules

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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 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.


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5254 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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