Hydrophilic Aromatic Residue and in silico Structure for Carbohydrate Binding Module

Abstract

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-simulate