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GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES

Overview of attention for article published in Journal of Cheminformatics, March 2023
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  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
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Title
GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES
Published in
Journal of Cheminformatics, March 2023
DOI 10.1186/s13321-023-00704-0
Pubmed ID
Authors

Roman Joeres, Daniel Bojar, Olga V. Kalinina

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 .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 22%
Unspecified 1 11%
Student > Ph. D. Student 1 11%
Librarian 1 11%
Unknown 4 44%
Readers by discipline Count As %
Unspecified 1 11%
Computer Science 1 11%
Social Sciences 1 11%
Chemistry 1 11%
Unknown 5 56%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 April 2023.
All research outputs
#8,187,876
of 24,541,341 outputs
Outputs from Journal of Cheminformatics
#617
of 912 outputs
Outputs of similar age
#142,454
of 405,984 outputs
Outputs of similar age from Journal of Cheminformatics
#20
of 35 outputs
Altmetric has tracked 24,541,341 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 912 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 405,984 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.