Get Started with glyenzy • glyenzy

Think of glycan biosynthesis as nature’s most sophisticated assembly line 🏭. Glycosyltransferases work like specialized robots, each with a very particular job: some love attaching GlcNAc residues, others are obsessed with Gal, and each follows strict rules about where and how to make connections. It’s like LEGO building!

While you might build a LEGO house solo on a weekend, cells need an entire crew of enzymatic specialists working in perfect coordination. The result? Thousands of unique glycan structures, each crafted with precision.

Enter glyenzy 🧬 – your computational toolkit for exploring this fascinating world. Whether you want to trace how a glycan came to be or predict what new structures might emerge from a biochemical reaction, glyenzy has you covered.

library(glyrepr)
library(glyenzy)
library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#> 
#>     decompose, spectrum
#> The following object is masked from 'package:base':
#> 
#>     union

Quick heads-up: glyenzy stands on the shoulders of giants – specifically glyrepr, glyparse, and glymotif. You don’t need to master these packages to get started, but they’re worth exploring for advanced glycan wizardry ✨. Also, this vignette assumes you’re comfortable with IUPAC-condensed notation. New to it? No worries – check out this friendly tutorial first!

Your First Taste of Glycan Engineering 🚀

Let’s dive right in with a hands-on example! We’ll start with a charming O-glycan that’s perfect for demonstration:

Notice how each glycosidic bond is labeled with its responsible enzyme? That’s the beauty of glycan biosynthesis – every connection has a story! (We’ll skip the rightmost peptide bond for now.)

Here’s our star glycan in IUPAC-condensed notation:

glycan <- "Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc(a1-"

Time to meet your new best friend: get_involved_enzymes() 👋 This clever function reveals all the enzymes that might have had a hand in building your glycan:

get_involved_enzymes(glycan)
#> [1] "B4GALT1" "B4GALT2" "B4GALT3" "B4GALT4" "B4GALT5" "C1GALT1" "GCNT1"  
#> [8] "GCNT3"   "GCNT4"

Cool, right? Not only does it spot the enzymes from our diagram, but it also whispers about B4GALT1/2/3/4 potentially being involved. (Don’t worry, we’ll unpack this mystery soon!)

But wait, there’s more! What if we want to see what happens when we add a new enzyme to the mix? Let’s give ST3GAL2 a chance to work its magic:

apply_enzyme(glycan, "ST3GAL2")
#> <glycan_structure[1]>
#> [1] Neu5Ac(a2-3)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc(a1-
#> # Unique structures: 1

Fascinating! 🎯 The function returns a glyrepr::glycan_structure() vector, but the real story is what happened: a shiny new sialic acid got attached to the β1-3 Gal, while the β1-4 Gal was completely ignored. Why? Because ST3GAL2 is incredibly picky – it only recognizes “Gal(β1-3)GalNAc(α1-” as its substrate.

Getting the hang of it? Great! Let’s explore what else glyenzy can do for you.

Detective Work: Tracing Glycan Origins 🔍

Ever wondered about a glycan’s backstory? glyenzy is your molecular detective, ready to solve three key mysteries:

Who did it? Which glycosyltransferases and glycoside hydrolases were involved?
How many times? How often did each enzyme swing into action?
What’s the timeline? In what order did these biochemical events unfold?

Behind the scenes, glyenzy has catalogued the reaction rules of 62 enzymes in its molecular database. Want to peek at any enzyme’s profile? Just use enzyme("MGAT3") and prepare to be amazed by the biochemical details!

Combined with the sophisticated motif-matching algorithms from glymotif, we can reconstruct any glycan’s complete biography.

You’ve already met get_involved_enzymes() – perfect for getting the full cast of characters. But sometimes you want more targeted intel:

is_synthesized_by() answers “Was enzyme X involved?” with a simple yes/no
count_enzyme_steps() tells you exactly how many times an enzyme got busy

This intel is gold for multiomics analysis – imagine linking glycan structures directly to enzyme expression levels! 📊

Ready for the grand finale? Meet rebuild_biosynthesis() – the function that reconstructs a glycan’s complete life story:

path <- rebuild_biosynthesis(glycan)
path
#> IGRAPH 2137362 DN-- 4 9 -- 
#> + attr: name (v/c), enzyme (e/c), step (e/n)
#> + edges from 2137362 (vertex names):
#> [1] GalNAc(a1-                       ->Gal(b1-3)GalNAc(a1-                       
#> [2] Gal(b1-3)GalNAc(a1-              ->Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-         
#> [3] Gal(b1-3)GalNAc(a1-              ->Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-         
#> [4] Gal(b1-3)GalNAc(a1-              ->Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-         
#> [5] Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-->Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc(a1-
#> [6] Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-->Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc(a1-
#> [7] Gal(b1-3)[GlcNAc(b1-6)]GalNAc(a1-->Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc(a1-
#> + ... omitted several edges

What you get back is a directed igraph object – think of it as a molecular family tree! 🌳 Each vertex represents a glycan structure, and each edge represents an enzymatic step. If more than one enzyme is involved in an enzymatic step, multiple edges are created between the two vertices.

Let’s try this with a more complex N-glycan:

glycan <- "GlcNAc(b1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-"
path <- rebuild_biosynthesis(glycan)
plot(
  path,
  layout = layout_as_tree(path),
  vertex.label = NA,
  vertex.size = 10,
  edge.arrow.size = 0.3,
  margin = 0
)

Note: The plotting could use some TLC – we’re working on prettier visualizations! 🎨

Crystal Ball Mode: Predicting Glycan Futures 🔮

Now for the really fun part – playing molecular fortune teller! glyenzy can predict what new glycans emerge when you mix specific enzymes with existing structures. Let’s start simple and work our way up to some serious biochemical wizardry.

# The humble GalNAc core of O-glycan
glycan <- "GalNAc(a1-"

What happens if we introduce C1GALT1 to this lonely GalNAc?

apply_enzyme(glycan, "C1GALT1")
#> <glycan_structure[1]>
#> [1] Gal(b1-3)GalNAc(a1-
#> # Unique structures: 1

Boom! 💥 We just witnessed the birth of the famous Core 1 O-GalNAc glycan!

Here’s how apply_enzyme() works: give it a glycan and an enzyme, and it returns all possible products. When multiple outcomes are possible, you’ll get them all bundled in a glyrepr::glycan_structure() vector.

# A bi-antennary agalactosylated N-glycan (fancy name for "no galactose yet")
glycan <- "GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-"
apply_enzyme(glycan, "B4GALT1")
#> <glycan_structure[2]>
#> [1] Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [2] Gal(b1-4)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> # Unique structures: 2

Perfect! Both antennae can be galactosylated, giving us two distinct products. This is biochemical branching in action! 🌿

The Primordial Soup Experiment 🧪

Ready for some serious fun? Let’s create a molecular primordial soup! Toss in some glycan substrates, add a cocktail of enzymes, and watch the magic unfold over multiple reaction steps.

Use spawn_glycans() for the full experience, or spawn_glycans_step() if you prefer to watch the drama unfold step by step:

# Our trusty bi-antennary N-glycan meets three enzyme friends
spawn_glycans(glycan, c("B4GALT1", "ST3GAL3", "MGAT3"))
#> <glycan_structure[32]>
#> [1] GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [2] Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [3] Gal(b1-4)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [4] GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-4)][GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [5] Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [6] Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-4)][GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [7] GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-4)][GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [8] Gal(b1-4)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][GlcNAc(b1-4)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [9] Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> [10] Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc(b1-
#> ... (22 more not shown)
#> # Unique structures: 32

What a party! 🎉 Here’s what each enzyme brought to the table:

B4GALT1: Adds β1-4 Gal to those lonely GlcNAc branches
ST3GAL3: Decorates with α2-3 sialic acid (the fancy finishing touch)
MGAT3: Drops in a bisecting GlcNAc right at the core mannose

The result? A spectacular collection of 32 different glycans, including our original starting structure!

The Fine Print: What You Need to Know ⚠️

Before you go wild with glyenzy, here are some important caveats to keep in mind:

Species and Scope

glyenzy is currently a human-centric package, focusing specifically on N-glycans and O-GalNAc glycans. If you’re working with GAGs, glycolipids, or glycans from plants or insects, the results might not be accurate. We’re basically specialists, not generalists! 🎯

The “Better Safe Than Sorry” Approach

Our algorithms are intentionally inclusive – they assume that all possible isoenzymes capable of catalyzing a reaction might be involved. This means you should interpret results with a grain of salt.

For instance, when glyenzy spots the motif “Neu5Ac(α2-3)Gal(β1-”, it’ll flag both ST3GAL3 and ST3GAL4 as potential culprits. In reality, tissue specificity and other factors might mean only one is actually active. Think of it as getting a list of suspects rather than the definitive perpetrator! 🕵️

Concrete Residues Only

glyenzy is picky about precision – it only works with concrete residues like “Glc” and “GalNAc”, not generic ones like “Hex” or “HexNAc”. If your data uses generic terms, you’ll need to be more specific! 🎯

Substituents: Not Yet Supported

Modifications like sulfation and phosphorylation aren’t supported yet, and they might actually break the algorithms. If your glycans are decorated with these extras, use glyrepr::remove_substituents() to get clean, analysis-ready structures.

Complete Structures Required

Incomplete or partially degraded glycan structures can lead glyenzy astray. Make sure your input represents the full, intact glycan for reliable results! ✅

Where We Start the Story

glyenzy has specific starting points for its biosynthetic narratives:

N-glycans: Begin with the Glc₃Man₉GlcNAc₂ precursor (post-OST transfer)
O-glycans: Start with GalNAc(α1- (post-GALNT addition)

This means we don’t track the earlier steps – ALGs building the N-glycan precursor, OST transferring it to asparagine, or GALNTs adding the initial GalNAc. We pick up the story from there! 📖

Behind the Scenes: The Technical Magic ⚙️

Curious about what makes glyenzy tick? Here’s the tech stack:

glyrepr: The foundation for representing glycan structures
glyparse: The linguistic expert that decodes IUPAC-condensed notation
glymotif: The pattern-matching wizard that finds structural motifs

All enzymes live as enzyme() objects (technically glyenzy_enzyme S3 class instances). Want to peek under the hood of any enzyme? Just call enzyme("YOUR_FAVORITE_ENZYME") and prepare for a detailed biochemical profile! 🔬