Glycosylator

Glycosylator models and refines glycans in protein structures to support identification and analysis of N-linked glycosylation and complex multi-branching carbohydrate conformations.


Key Features:

  • Glycan modeling: Models glycans within protein structures ranging from monosaccharides to complex multi-branching glycan structures.
  • N-linked glycosylation support: Represents glycosylation at asparagine residues and across sequons for analysis of N-linked glycosylation.
  • Genetic algorithm refinement: Applies a genetic algorithm to refine modeled glycans, remove steric clashes, and explore alternative conformations.
  • Template-based identification: Identifies specific three-dimensional glycans on protein structures using a library of predefined templates.
  • Extensible polymer library: Provides a generic library design that allows incorporation of additional polymer types.
  • CHARMM topology basis: Uses the CHARMM force field as the molecular topology basis to enable generation of new complex sugar moieties without internal code changes.
  • Implementation: Implemented as a Python framework.

Scientific Applications:

  • Glycan identification in structures: Identification and modeling of glycans in protein structural models.
  • N-linked glycosylation analysis: Analysis of glycosylation at asparagine residues and across sequons.
  • Structural refinement: Refinement of glycan conformations to resolve steric clashes for structural and functional studies.
  • Generation of novel sugar moieties: Generation and parametrization of new complex sugar moieties using CHARMM-based topologies.
  • Glycoinformatics and biomolecular modeling: Support for research workflows in glycoinformatics and biomolecular modeling.

Methodology:

Implemented in Python, Glycosylator uses a library of predefined glycan templates and the CHARMM force field as its molecular topology basis and applies a genetic algorithm to refine modeled glycans and remove steric clashes; its generic library structure permits adding polymer types and generating new complex sugar moieties without changing internal code.

Topics

Details

Tool Type:
command-line tool
Programming Languages:
Python
Added:
1/9/2020
Last Updated:
12/3/2020

Operations

Publications

Lemmin T, Soto C. Glycosylator: a Python framework for the rapid modeling of glycans. BMC Bioinformatics. 2019;20(1). doi:10.1186/s12859-019-3097-6. PMID:31640540. PMCID:PMC6806574.

PMID: 31640540
PMCID: PMC6806574
Funding: - Swiss National Foundation for Science: P3P3PA_174356 - National Institutes of Health: U19 AI117905