Abstract
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://https://dev.glycam.org/gp/) or Glycosciences.db (http://www.glycosciences.de/)). 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.
Highlights
Carbohydrates are a class of large and diverse biomolecules, ranging from a simple monosaccharide to large multi-branching glycan structures
In silico modeling of glycoprotein is a tedious and time consuming process and tools, such as CarbBuilder [12], POLYS [13], doGlycans [14], SWEET-II [15], GLYCAM-Web [16], Glycan Reader [17, 18] and CHARMM-graphical user interface (GUI) glycan modeler [19] were developed to facilitate the modeling of glycans
Glycan Reader recognizes most types of glycans and their chemical modifications found in the Protein Data Bank (PDB), which are all available in the CHARMM force field [22]
Summary
Benchmark on viral glycoproteins We compared the performance of Glycosylator and doGlycans, another Python framework for modeling glycans using three representative viral envelope glycoproteins, each containing different numbers of glycosylation sites and overall glycan density. We observed that all glycoproteins modeled with Glycosylator had a lower potential energy and were devoid of any steric clashes and topological errors (Table 2). A simple minimization was insufficient for removing steric clashes from the HIV-1 Envelope trimer and Delta coronavirus spike protein structures using doGlycans. The density of sequons at the surface of these glycoproteins is high, requiring a more effective strategy for removing clashes, such as provided by Glycosylator’s Sampler Class. The steric clashes present in the structures produced with doGlycans lead topological errors, such as ring puckering after minimizations. A combination of mannose 5, mannose 9 and complex glycans was modeled ab initio or by extending existing glycans to produce a more biologically relevant glycoform of the HIV-1 Env trimer (Fig. 1, lower right triangle). The Sampler function in Glycosylator was used to remove all major clashes, such that the
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