GLYCO: a tool to quantify glycan shielding of glycosylated proteins
Author:
Lee Myungjin1ORCID,
Reveiz Mateo1,
Rawi Reda1ORCID,
Kwong Peter D12,
Chuang Gwo-Yu1
Affiliation:
1. Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
2. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
Abstract
Abstract
Motivation
Glycans play important roles in protein folding and cell–cell interactions—and, furthermore, glycosylation of protein antigens can dramatically impact immune responses. While there have been attempts to quantify the glycan shielding or coverage of a protein surface, none of the publicly available tools analyzes glycan shielding computationally at an atomistic level.
Results
Here, we developed an in silico approach, GLYCO (GLYcan COverage), to quantify the glycan shielding of a protein surface. The software provides insights into glycan-dense/sparse regions of the entire protein surface or a subset of the protein surface. GLYCO calculates glycan shielding from a single coordinate file or from multiple coordinate files, for instance, as obtained from molecular dynamics simulations or by nuclear magnetic resonance spectroscopy structure determination, enabling analysis of glycan dynamics. Overall, GLYCO provides fundamental insights into the glycan shielding of glycosylated proteins.
Availability and implementation
GLYCO is freely available at GitHub (https://github.com/myungjinlee/GLYCO).
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
Intramural Research Program of the Vaccine Research Center
National Institute of Allergy and Infectious Diseases
Intramural AIDS Research Fellowship Program from the Office of AIDS Research
Office of Intramural Training & Education and the Office of Intramural Research
National Institutes of Health
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
2 articles.
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1. Artificial Neural Network-Based Optimization of Shielding Cylinder;2022 International Conference on Knowledge Engineering and Communication Systems (ICKES);2022-12-28
2. Cryo-EM structures of prefusion SIV envelope trimer;Nature Structural & Molecular Biology;2022-11