Piotr Klukowski1, Mario Schubert2. 1. Department of Computer Science, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland. 2. Department of Biosciences, University of Salzburg, Salzburg, Austria.
Abstract
Motivation: A better understanding of oligosaccharides and their wide-ranging functions in almost every aspect of biology and medicine promises to uncover hidden layers of biology and will support the development of better therapies. Elucidating the chemical structure of an unknown oligosaccharide remains a challenge. Efficient tools are required for non-targeted glycomics. Chemical shifts are a rich source of information about the topology and configuration of biomolecules, whose potential is however not fully explored for oligosaccharides. We hypothesize that the chemical shifts of each monosaccharide are unique for each saccharide type with a certain linkage pattern, so that correlated data measured by NMR spectroscopy can be used to identify the chemical nature of a carbohydrate. Results: We present here an efficient search algorithm, GlycoNMRSearch, which matches either a subset or the entire set of chemical shifts of an unidentified monosaccharide spin system to all spin systems in an NMR database. The search output is much more precise than earlier search functions and highly similar matches suggest the chemical structure of the spin system within the oligosaccharide. Thus, searching for connected chemical shift correlations within all electronically available NMR data of oligosaccharides is a very efficient way of identifying the chemical structure of unknown oligosaccharides. With an improved database in the future, GlycoNMRSearch will be even more efficient deducing chemical structures of oligosaccharides and there is a high chance that it becomes an indispensable technique for glycomics. Availability and implementation: The search algorithm presented here, together with a graphical user interface, is available at http://glyconmrsearch.nmrhub.eu. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: A better understanding of oligosaccharides and their wide-ranging functions in almost every aspect of biology and medicine promises to uncover hidden layers of biology and will support the development of better therapies. Elucidating the chemical structure of an unknown oligosaccharide remains a challenge. Efficient tools are required for non-targeted glycomics. Chemical shifts are a rich source of information about the topology and configuration of biomolecules, whose potential is however not fully explored for oligosaccharides. We hypothesize that the chemical shifts of each monosaccharide are unique for each saccharide type with a certain linkage pattern, so that correlated data measured by NMR spectroscopy can be used to identify the chemical nature of a carbohydrate. Results: We present here an efficient search algorithm, GlycoNMRSearch, which matches either a subset or the entire set of chemical shifts of an unidentified monosaccharidespin system to all spin systems in an NMR database. The search output is much more precise than earlier search functions and highly similar matches suggest the chemical structure of the spin system within the oligosaccharide. Thus, searching for connected chemical shift correlations within all electronically available NMR data of oligosaccharides is a very efficient way of identifying the chemical structure of unknown oligosaccharides. With an improved database in the future, GlycoNMRSearch will be even more efficient deducing chemical structures of oligosaccharides and there is a high chance that it becomes an indispensable technique for glycomics. Availability and implementation: The search algorithm presented here, together with a graphical user interface, is available at http://glyconmrsearch.nmrhub.eu. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Luca Unione; Maria Pia Lenza; Ana Ardá; Pedro Urquiza; Ana Laín; Juan Manuel Falcón-Pérez; Jesús Jiménez-Barbero; Oscar Millet Journal: ACS Cent Sci Date: 2019-07-24 Impact factor: 14.553
Authors: Michael Böhm; Andreas Bohne-Lang; Martin Frank; Alexander Loss; Miguel A Rojas-Macias; Thomas Lütteke Journal: Nucleic Acids Res Date: 2019-01-08 Impact factor: 16.971