Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules.
BACKGROUND
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.
Chou
Wei-Yao
WY
Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.
Chou
Wei-I
WI
Pai
Tun-Wen
TW
Lin
Shu-Chuan
SC
Jiang
Ting-Ying
TY
Tang
Chuan-Yi
CY
Chang
Margaret Dah-Tsyr
MD
D017360:Arabidopsis / Q000378:metabolism; D001665:Binding Sites; D002241:Carbohydrates / Q000737:chemistry*; D008024:Ligands; D008958:Models, Molecular; D011487:Protein Conformation; D016415:Sequence Alignment / Q000379:methods*; D013379:Substrate Specificity
D002241:Carbohydrates; D008024:Ligands