A weighted q-gram method for glycan structure classification

<p>Abstract</p> <p>Background</p> <p>Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The <it>q</it>-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This <it>q</it>-gram method assumes that for a certain <it>q</it>, different <it>q</it>-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted <it>q</it>-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among <it>q</it>-grams. In contrast to the traditional <it>q</it>-gram method, our weighted <it>q</it>-gram method admits similarity among <it>q</it>-grams for a certain <it>q</it>. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans.</p> <p>Results</p> <p>Two glycan datasets were used to compare the weighted <it>q</it>-gram method and the original <it>q</it>-gram method. The results show that the incorporation of <it>q</it>-gram similarity improves the classification performance for all of the important glycan classes tested.</p> <p>Conclusion</p> <p>The results in this paper indicate that similarity among <it>q</it>-grams obtained from geometric structure, monosaccharides and glycosidic linkage contributes to the glycan function classification. This is a big step towards the understanding of glycan function based on their complex structures.</p

Crystallographic and Molecular Dynamics Analysis of Loop Motions Unmasking the Peptidoglycan-Binding Site in Stator Protein MotB of Flagellar Motor

Background: The C-terminal domain of MotB (MotB-C) shows high sequence similarity to outer membrane protein A and related peptidoglycan (PG)-binding proteins. It is believed to anchor the power-generating MotA/MotB stator unit of the bacterial flagellar motor to the peptidoglycan layer of the cell wall. We previously reported the first crystal structure of this domain and made a puzzling observation that all conserved residues that are thought to be essential for PG recognition are buried and inaccessible in the crystal structure. In this study, we tested a hypothesis that peptidoglycan binding is preceded by, or accompanied by, some structural reorganization that exposes the key conserved residues. Methodology/Principal Findings: We determined the structure of a new crystalline form (Form B) of Helicobacter pylori MotB-C. Comparisons with the existing Form A revealed conformational variations in the petal-like loops around the carbohydrate binding site near one end of the b-sheet. These variations are thought to reflect natural flexibility at this site required for insertion into the peptidoglycan mesh. In order to understand the nature of this flexibility we have performed molecular dynamics simulations of the MotB-C dimer. The results are consistent with the crystallographic data and provide evidence that the three loops move in a concerted fashion, exposing conserved MotB residues that have previously been implicated in binding of the peptide moiety of peptidoglycan. Conclusion/Significance: Our structural analysis provides a new insight into the mechanism by which MotB inserts into th

A Modular BAM Complex in the Outer Membrane of the α-Proteobacterium Caulobacter crescentus

Mitochondria are organelles derived from an intracellular α-proteobacterium. The biogenesis of mitochondria relies on the assembly of β-barrel proteins into the mitochondrial outer membrane, a process inherited from the bacterial ancestor. Caulobacter crescentus is an α-proteobacterium, and the BAM (β-barrel assembly machinery) complex was purified and characterized from this model organism. Like the mitochondrial sorting and assembly machinery complex, we find the BAM complex to be modular in nature. A ∼150 kDa core BAM complex containing BamA, BamB, BamD, and BamE associates with additional modules in the outer membrane. One of these modules, Pal, is a lipoprotein that provides a means for anchorage to the peptidoglycan layer of the cell wall. We suggest the modular design of the BAM complex facilitates access to substrates from the protein translocase in the inner membrane

Unexpected High Digestion Rate of Cooked Starch by the Ct-Maltase-Glucoamylase Small Intestine Mucosal α-Glucosidase Subunit

For starch digestion to glucose, two luminal α-amylases and four gut mucosal α-glucosidase subunits are employed. The aim of this research was to investigate, for the first time, direct digestion capability of individual mucosal α-glucosidases on cooked (gelatinized) starch. Gelatinized normal maize starch was digested with N- and C-terminal subunits of recombinant mammalian maltase-glucoamylase (MGAM) and sucrase-isomaltase (SI) of varying amounts and digestion periods. Without the aid of α-amylase, Ct-MGAM demonstrated an unexpected rapid and high digestion degree near 80%, while other subunits showed 20 to 30% digestion. These findings suggest that Ct-MGAM assists α-amylase in digesting starch molecules and potentially may compensate for developmental or pathological amylase deficiencies

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed