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Glycosylator: a Python framework for the rapid modeling of glycans

  • Lemmin, Thomas1, 2
  • Soto, Cinque3, 4
  • 1 ETH Zurich, DS3Lab, System Group, Department of Computer Sciences, Zurich, CH-8093, Switzerland , Zurich (Switzerland)
  • 2 Institute of Medical Virology, University of Zurich (UZH), Zurich, CH-8057, Switzerland , Zurich (Switzerland)
  • 3 Vanderbilt University Medical Center, Vanderbilt Vaccine Center, Nashville, TN, 37232, USA , Nashville (United States)
  • 4 Vanderbilt University Medical Center, Department of Pediatrics, Nashville, TN, 37232, USA , Nashville (United States)
Published Article
BMC Bioinformatics
Springer (Biomed Central Ltd.)
Publication Date
Oct 22, 2019
DOI: 10.1186/s12859-019-3097-6
Springer Nature


BackgroundCarbohydrates are a class of large and diverse biomolecules, ranging from a simple monosaccharide to large multi-branching glycan structures. The covalent linkage of a carbohydrate to the nitrogen atom of an asparagine, a process referred to as N-linked glycosylation, plays an important role in the physiology of many living organisms. Most software for glycan modeling on a personal desktop computer requires knowledge of molecular dynamics to interface with specialized programs such as CHARMM or AMBER. There are a number of popular web-based tools that are available for modeling glycans (e.g., GLYCAM-WEB (http:// or Glycosciences.db ( However, these web-based tools are generally limited to a few canonical glycan conformations and do not allow the user to incorporate glycan modeling into their protein structure modeling workflow.ResultsHere, we present Glycosylator, a Python framework for the identification, modeling and modification of glycans in protein structure that can be used directly in a Python script through its application programming interface (API) or through its graphical user interface (GUI). The GUI provides a straightforward two-dimensional (2D) rendering of a glycoprotein that allows for a quick visual inspection of the glycosylation state of all the sequons on a protein structure. Modeled glycans can be further refined by a genetic algorithm for removing clashes and sampling alternative conformations. Glycosylator can also identify specific three-dimensional (3D) glycans on a protein structure using a library of predefined templates.ConclusionsGlycosylator was used to generate models of glycosylated protein without steric clashes. Since the molecular topology is based on the CHARMM force field, new complex sugar moieties can be generated without modifying the internals of the code. Glycosylator provides more functionality for analyzing and modeling glycans than any other available software or webserver at present. Glycosylator will be a valuable tool for the glycoinformatics and biomolecular modeling communities.

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