GLYCO: a tool to quantify glycan shielding of glycosylated proteins

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.

1 Introduction

Glycans have diverse biological roles such as intercellular signaling, glycoprotein folding, biological masks (Dumville and Fry, 2000; Roth et al., 1979; Schauer, 1985; Varki, 2017). In particular, glycan shielding can be a major factor in understanding protein–protein interactions as glycans can block protein binding and recognition. Especially in vaccine research, glycan shielding can substantially alter immune responses as it may markedly impede antibody–antigen recognition, whereas glycan holes generated by glycan removal can increase immunogenicity (Zhou et al., 2017). Therefore, quantification of glycan shielding is crucial to glycoprotein related research.

Several studies have attempted to quantify and visualize the glycan shielding of protein surfaces (Berndsen et al., 2020; Lemmin et al., 2017; Stewart-Jones et al., 2016; Wagh et al., 2018). In addition, a few tools and servers for glycan modeling and delineation of potential glycosylation sites have been developed such as GlyProt (Bohne-Lang and von der Lieth, 2005), Glycan Reader & Modeler in CHARMM-GUI (Park et al., 2019), and NetNGlyc 1.0 Server (Gupta and Brunak, 2002). However, they do not provide insight into the impact of glycosylation on the protein surface and quantification of glycan shielding through a computational approach at an atomistic level has not been achieved.

Here, we introduce GLYCO (GLYcan COverage), a software to quantify glycan shielding or coverage of the glycoprotein surface. We have applied GLYCO to calculate the correlation between antibody–antigen properties and glycan shielding (Lee et al., 2021) and to evaluate glycan-depleted areas, which antibodies preferentially target (Cerutti et al., 2021).

GLYCO counts the number of glycan atoms per entire surface protein residue or per user input residue. It works not only for a single Protein Data Bank (PDB; Berman et al., 2000) file but also for multiple frames, e.g. from molecular dynamics (MD) simulations or nuclear magnetic resonance spectroscopy structure determination. Overall, GLYCO is a useful tool for identifying glycan shielding over protein surfaces and can be applied to many research areas where glycosylated proteins are involved.

2 Materials and methods

GLYCO quantifies glycan shielding of glycoprotein surfaces using four steps (Fig.  1A). Only heavy atoms (e.g. non-hydrogen atoms) of protein and glycan atoms are used in the analyses. The composition of glycan is not of importance to the calculation; thus, all types of glycans can be quantified by GLYCO (see Supplementary Data for workflow of GLYCO).

Fig. 1.

Description of GLYCO, a glycan quantification protocol. (A) Schematics for calculation of glycan atoms covering protein surface atoms, the core algorithm for GLYCO. (B) Visualization of GLYCO output comparing with an actual glycoprotein. The example input is N-linked Mannose-5 BG505 N160K HIV-1 Env Trimer (left panel). The calculated glycan shielding of a single PDB is displayed in the middle panel and that of the multiple PDBs is in the right panel. The multiple frames are obtained from MD simulations, and the results were averaged over 300 frames (see Supplementary Data for more details)

2.1 Step 1: extract protein surface residues

GLYCO executes FreeSASA (Mitternacht, 2016) to extract protein surface residues for analysis. The cutoff to define surface has been set to 30 Ų with 1.4 Å as a probe radius by default; however, these can be altered by users.

2.2 Step 2: select glycans within a distance cutoff

GLYCO measures the distance from the atoms of protein surface residues that were selected from Step 1 to all glycan atoms. The glycan atoms beyond the defined distance cutoff are excluded from the calculations. The cutoff could be defined as the length of a glycan (as Fig.  1B using the longest length of glycans of the protein) but users can define it freely.

2.3 Step 3: exclude glycan atoms that have protein atoms in between

GLYCO defines glycan shielding as the number of glycan atoms covering a protein surface atom. Therefore, GLYCO excludes glycan atoms that are blocked by intervening protein regions, that do not actually provide shielding effect to the atom of the protein surface. The program generates vector equations of lines ($\mathbf{r}$) between the vector of protein surface atoms ($\mathbf{a}$) and the vector of selected glycan atoms ($\mathbf{b}$)

$$\mathbf{r}=\mathbf{a}+t\left(\mathbf{b}-\mathbf{a}\right),$$ (1)

where $t \in [0, 1]$. Each discrete point along the vector line generates a cubic box from $-1$Å to $+1$Å on $x,\mathrm{ }y,\mathrm{ }z$ and checks if any protein atom is within the box. If any protein atoms are in the box, the associated glycan atom is excluded from the calculation.

2.4 Step 4: count selected glycans per protein atom

The final step is to count the selected glycan heavy atoms associated with each protein surface atom within the distance cutoff. Steps 1–4 are repeated to generate glycan shielding for all protein surface atoms while removing redundant counts of glycan atoms in each protein residue.

3 Visualization

GLYCO outputs a PDB file that has glycan shielding values saved in the B-factor column (see Supplementary Data). Various molecular visualization programs such as PyMOL (DeLano, 2002) can display the glycan shielding based on the B-factors enabling for users to identify dense and sparse regions of glycan shielding (Fig.  1B, middle and right) compared to the actual glycosylated protein (Fig.  1B, left).

4 Conclusions

In this study, we developed GLYCO to quantify the glycan shielding of glycoproteins. This software calculates glycan shielding for the entire protein surface or a set of user-defined residues. The results can be visualized using protein structure visualization software. Considering the impact of glycans on proteins, such as the effect of viral proteins or immunogens, we believe that GLYCO will be a useful tool to quantify glycan shielding.