GlycomeDB—a unified database for carbohydrate structures

GlycomeDB integrates the structural and taxonomic data of all major public carbohydrate databases, as well as carbohydrates contained in the Protein Data Bank, which renders the database currently the most comprehensive and unified resource for carbohydrate structures worldwide. GlycomeDB retains the links to the original databases and is updated at weekly intervals with the newest structures available from the source databases. The complete database can be downloaded freely or accessed through a Web-interface (www.glycome-db.org) that provides flexible and powerful search functionalities

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

SuperSweet—a resource on natural and artificial sweetening agents

A vast number of sweet tasting molecules are known, encompassing small compounds, carbohydrates, d-amino acids and large proteins. Carbohydrates play a particularly big role in human diet. The replacement of sugars in food with artificial sweeteners is common and is a general approach to prevent cavities, obesity and associated diseases such as diabetes and hyperlipidemia. Knowledge about the molecular basis of taste may reveal new strategies to overcome diet-induced diseases. In this context, the design of safe, low-calorie sweeteners is particularly important. Here, we provide a comprehensive collection of carbohydrates, artificial sweeteners and other sweet tasting agents like proteins and peptides. Additionally, structural information and properties such as number of calories, therapeutic annotations and a sweetness-index are stored in SuperSweet. Currently, the database consists of more than 8000 sweet molecules. Moreover, the database provides a modeled 3D structure of the sweet taste receptor and binding poses of the small sweet molecules. These binding poses provide hints for the design of new sweeteners. A user-friendly graphical interface allows similarity searching, visualization of docked sweeteners into the receptor etc. A sweetener classification tree and browsing features allow quick requests to be made to the database. The database is freely available at: http://bioinformatics.charite.de/sweet/

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

[CarbBank and the complex carbohydrate structure database. Annual report 1994]

A brief status report and forthcoming changes to the CarbBank, a carbohydrate database, is provided