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Titel: GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES
VerfasserIn: Joeres, Roman
Bojar, Daniel
Kalinina, Olga
Sprache: Englisch
Titel: Journal of cheminformatics
Bandnummer: 15
Heft: 1
Verlag/Plattform: BioMed Central
Erscheinungsjahr: 2023
Freie Schlagwörter: Glycan
Glycobiology
IUPAC-condensed
SMILES
Grammar
DDC-Sachgruppe: 610 Medizin, Gesundheit
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: Glycans are important polysaccharides on cellular surfaces that are bound to glycoproteins and glycolipids. These are one of the most common post-translational modifications of proteins in eukaryotic cells. They play important roles in protein folding, cell-cell interactions, and other extracellular processes. Changes in glycan structures may influence the course of different diseases, such as infections or cancer. Glycans are commonly represented using the IUPAC-condensed notation. IUPAC-condensed is a textual representation of glycans operating on the same topological level as the Symbol Nomenclature for Glycans (SNFG) that assigns colored, geometrical shapes to the main monomers. These symbols are then connected in tree-like structures, visualizing the glycan structure on a topological level. Yet for a representation on the atomic level, notations such as SMILES should be used. To our knowledge, there is no easy-to-use, general, open-source, and offline tool to convert the IUPAC-condensed notation to SMILES. Here, we present the open-access Python package GlyLES for the generalizable generation of SMILES representations out of IUPAC-condensed representations. GlyLES uses a grammar to read in the monomer tree from the IUPAC-condensed notation. From this tree, the tool can compute the atomic structures of each monomer based on their IUPAC-condensed descriptions. In the last step, it merges all monomers into the atomic structure of a glycan in the SMILES notation. GlyLES is the first package that allows conversion from the IUPAC-condensed notation of glycans to SMILES strings. This may have multiple applications, including straightforward visualization, substructure search, molecular modeling and docking, and a new featurization strategy for machine-learning algorithms. GlyLES is available at https://github.com/kalininalab/GlyLES .
DOI der Erstveröffentlichung: 10.1186/s13321-023-00704-0
URL der Erstveröffentlichung: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00704-0
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-437275
hdl:20.500.11880/39159
http://dx.doi.org/10.22028/D291-43727
ISSN: 1758-2946
Datum des Eintrags: 11-Dez-2024
Fakultät: M - Medizinische Fakultät
Fachrichtung: M - Medizinische Biometrie, Epidemiologie und medizinische Informatik
Professur: M - Prof. Dr. Olga Kalinina
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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