Data mining the protein data bank: automatic detection and assignment of carbohydrate structures.

Thomas Lütteke; Martin Frank; Claus-W von der Lieth

doi:10.1016/j.carres.2003.09.038

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Data mining the protein data bank: automatic detection and assignment of carbohydrate structures.

Thomas Lütteke·Martin Frank·Claus-W von der Lieth

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Abstract

Knowledge of the 3D structure of glycans is a prerequisite for a complete understanding of the biological processes glycoproteins are involved in. However, due to a lack of standardised nomenclature, carbohydrate compounds are difficult to locate within the Protein Data Bank (PDB). Using an algorithm that detects carbohydrate structures only requiring element types and atom coordinates, we were able to detect 1663 entries containing a total of 5647 carbohydrate chains. The majority of chains are found to be N-glycosidically bound. Noncovalently bound ligands are also frequent, while O-glycans form a minority. About 30% of all carbohydrate containing PDB entries comprise one or several errors. The automatic assignment of carbohydrate structures in PDB entries will improve the cross-linking of glycobiology resources with genomic and proteomic data collections, which will be an important issue of the upcoming glycomics projects. By aiding in detection of erroneous annotations and structures, the algorithm might also help to increase database quality.

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Algorithms Carbohydrates Computational Biology Databases, Protein Glycoproteins Glycosylation Internet Models, Molecular Journal Article Research Support, Non-U.S. Gov't

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Thomas Lütteke, Martin Frank, Claus-W von der Lieth. 2004-04-02. Data mining the protein data bank: automatic detection and assignment of carbohydrate structures.. https://doi.org/10.1016/j.carres.2003.09.038

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