glycowork 0.8.1

Package for processing and analyzing glycans

glycowork

Glycans are a fundamental biological sequence, similar to DNA, RNA, or proteins. Glycans are complex carbohydrates that can form branched structures comprising monosaccharides and linkages as constituents. Despite being conspicuously absent from most research, glycans are ubiquitous in biology. They decorate most proteins and lipids and direct the stability and functions of biomolecules, cells, and organisms. This also makes glycans relevant to every human disease.

The analysis of glycans is made difficult by their nonlinearity and their astounding diversity, given the large number of monosaccharides and types of linkages. Glycowork is a Python package designed to process and analyze glycan sequences, with a special emphasis on glycan-focused data science and machine learning. Next to various functions to work with glycans, glycowork also contains glycan data that can be used for model training, motif comparisons, etc. Glycowork uses human-readable IUPAC-condensed (e.g., Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc) as a user-facing string representation and monosaccharide-level graphs as a powerful behind-the-scenes architecture.

If you use glycowork or any of our datasets in your project, please cite Thomes et al., 2021.

The full documentation for glycowork can be found here: https://bojarlab.github.io/glycowork/

If you want to contribute to glycowork, the best place to start is to read our contribution guidelines.

Install

via pip:
pip install glycowork
import glycowork

alternative:
pip install git+https://github.com/BojarLab/glycowork.git
import glycowork

Note that we have optional extra installs for specialized use (even further instructions can be found in the Examples tab), such as:
deep learning
pip install glycowork[ml]
drawing glycan images with GlycoDraw (see install instructions in the Examples tab)
pip install glycowork[draw]
analyzing atomic/chemical properties of glycans
pip install glycowork[chem]
everything
pip install glycowork[all]

Data & Models

Glycowork currently contains the following main datasets that are freely available to everyone:

• df_glycan
  • contains ~50,000 unique glycan sequences, including labels such as ~35,000 species associations, ~14,000 tissue associations, and ~1,000 disease associations
• glycan_binding
  • contains >550,000 protein-glycan binding interactions, from 1,392 unique glycan-binding proteins

Additionally, we store these trained deep learning models for easy usage, which can be retrieved with the prep_model function:

• LectinOracle
  • can be used to predict glycan-binding specificity of a protein, given its ESM-1b representation; from Lundstrom et al., 2021
• LectinOracle_flex
  • operates the same as LectinOracle but can directly use the raw protein sequence as input (no ESM-1b representation required)
• SweetNet
  • a graph convolutional neural network trained to predict species from glycan, can be used to generate learned glycan representations; from Burkholz et al., 2021
• NSequonPred
  • given the ESM-1b representation of an N-sequon (+/- 20 AA), this model can predict whether the sequon will be glycosylated

How to use

Glycowork currently contains four main modules:

• glycan_data
  • stores several glycan datasets and contains helper functions
• ml
  • here are all the functions for training and using machine learning models, including train-test-split, getting glycan representations, etc.
• motif
  • contains functions for processing & drawing glycan sequences, identifying motifs and features, and analyzing them
• network
  • contains functions for constructing and analyzing glycan networks (e.g., biosynthetic networks)

Below are some examples of what you can do with glycowork; be sure to check out the other examples in the full documentation for everything that’s there. A non-exhaustive list includes:

• using trained AI models for prediction
• training your own AI models
• motif enrichment analyses
• annotating motifs in glycans
• drawing publication-quality glycan figures
• finding out whether & where glycans are describing the same sequence
• m/z to composition to structure to motif mappings
• mass calculation
• visualizing motif distribution / glycan similarities / sequence properties
• constructing and analyzing biosynthetic networks

#drawing publication-quality glycan figures
from glycowork.motif.draw import GlycoDraw
GlycoDraw("Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Neu5Gc(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)][GlcNAc(b1-4)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc")

#get motifs, graph features, and sequence features of a set of glycan sequences to train models or analyze glycan properties
glycans = ['Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc',
           'Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
           'GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN1P']
from glycowork.motif.annotate import annotate_dataset
out = annotate_dataset(glycans, feature_set = ['known', 'graph', 'exhaustive']).head()

	LewisX	LewisY	SialylLewisX	SulfoSialylLewisX	LewisA	LewisB	SialylLewisA	SulfoLewisA	H_type2	H_type1	A_antigen	B_antigen	Galili_antigen	GloboH	Gb5	Gb4	Gb3	3SGb3	8DSGb3	3SGb4	8DSGb4	6DSGb4	3SGb5	8DSGb5	6DSGb5	6DSGb5_2	6SGb3	8DSGb3_2	6SGb4	8DSGb4_2	6SGb5	8DSGb5_2	66DSGb5	Forssman_antigen	iGb3	I_antigen	i_antigen	PI_antigen	Chitobiose	Trimannosylcore	Internal_LacNAc_type1	Terminal_LacNAc_type1	Internal_LacNAc_type2	Terminal_LacNAc_type2	Internal_LacdiNAc_type1	Terminal_LacdiNAc_type1	Internal_LacdiNAc_type2	Terminal_LacdiNAc_type2	bisectingGlcNAc	VIM	PolyLacNAc	Ganglio_Series	Lacto_Series(LewisC)	NeoLacto_Series	betaGlucan	KeratanSulfate	Hyluronan	Mollu_series	Arthro_series	Cellulose_like	Chondroitin_4S	GPI_anchor	Isoglobo_series	LewisD	Globo_series	Sda	SDA	Muco_series	Heparin	Peptidoglycan	Dermatansulfate	CAD	Lactosylceramide	Lactotriaosylceramide	LexLex	GM3	H_type3	GM2	GM1	cisGM1	VIM2	GD3	GD1a	GD2	GD1b	SDLex	Nglycolyl_GM2	Fuc_LN3	GT1b	GD1	GD1a_2	LcGg4	GT3	Disialyl_T_antigen	GT1a	GT2	GT1c	2Fuc_GM1	GQ1c	O_linked_mannose	GT1aa	GQ1b	HNK1	GQ1ba	O_mannose_Lex	2Fuc_GD1b	Sialopentaosylceramide	Sulfogangliotetraosylceramide	B-GM1	GQ1aa	bisSulfo-Lewis x	para-Forssman	core_fucose	core_fucose(a1-3)	GP1c	B-GD1b	GP1ca	Isoglobotetraosylceramide	polySia	high_mannose	Gala_series	LPS_core	Nglycan_complex	Nglycan_complex2	Oglycan_core1	Oglycan_core2	Oglycan_core3	Oglycan_core4	Oglycan_core5	Oglycan_core6	Oglycan_core7	Xylogalacturonan	Sialosylparagloboside	LDNF	OFuc	Arabinogalactan_type2	EGF_repeat	Nglycan_hybrid	Arabinan	Xyloglucan	Acharan_Sulfate	M3FX	M3X	1-6betaGalactan	Arabinogalactan_type1	Galactomannan	Tetraantennary_Nglycan	Mucin_elongated_core2	Fucoidan	Alginate	FG	XX	Difucosylated_core	GalFuc_core	diameter	branching	nbrLeaves	avgDeg	varDeg	maxDeg	nbrDeg4	max_deg_leaves	mean_deg_leaves	deg_assort	betweeness	betwVar	betwMax	eigenMax	eigenMin	eigenAvg	eigenVar	closeMax	closeMin	closeAvg	closeVar	flowMax	flowAvg	flowVar	flow_edgeMax	flow_edgeMin	flow_edgeAvg	flow_edgeVar	loadMax	loadAvg	loadVar	harmMax	harmMin	harmAvg	harmVar	secorderMax	secorderMin	secorderAvg	secorderVar	size_corona	size_core	nbr_node_types	egap	entropyStation	N	dens	Fuc	GalNAc	GlcN	GlcN1P	GlcN4P	GlcNAc	GlcNAcA	Kdo	Man	Xyl	Man(a1-3)Man	Man(a1-6)Man	Xyl(b1-2)Man	Man(b1-4)GlcNAc	GlcNAc(b1-4)GlcNAc	Fuc(a1-3)GlcNAc	Man(a1-2)Man	GalNAc(a1-4)GlcNAcA	GlcNAcA(a1-4)Kdo	GlcN(b1-7)Kdo	Kdo(a2-5)Kdo	Kdo(a2-4)Kdo	Kdo(a2-6)GlcN4P	GlcN4P(b1-6)GlcN1P	Man(a1-?)Man	Kdo(a2-?)Kdo
Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	8.0	1.0	4.0	1.846154	0.591716	4.0	1.0	4.0	4.0	-3.448276e-02	0.240093	0.051241	0.727273	0.337083	0.251423	0.276471	0.000487	0.400000	0.181818	0.288591	0.003994	0.727273	0.240093	0.051241	0.318182	0.090909	0.179293	0.006460	0.727273	0.240093	0.051241	6.950000	3.253571	4.820330	0.908780	66.603303	26.305893	44.589784	127.751200	4.0	13.0	13.0	0.057103	-2.075921	13.0	12.0	1	0	2	0	0	2	0	0	3	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	2	0
Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10.0	1.0	3.0	1.866667	0.248889	3.0	0.0	3.0	3.0	-2.331468e-15	0.263004	0.037282	0.703297	0.288267	0.234925	0.257853	0.000179	0.341463	0.157303	0.238951	0.003059	0.703297	0.263004	0.037282	0.296703	0.076923	0.182104	0.005067	0.703297	0.263004	0.037282	6.616667	3.407937	4.921958	0.799295	70.823725	26.381812	48.985176	174.452553	3.0	15.0	15.0	0.031066	-2.658737	15.0	14.0	0	0	2	0	0	2	0	0	6	0	2	1	0	1	1	0	2	0	0	0	0	0	0	0	5	0
GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN1P	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10.0	2.0	4.0	1.866667	0.382222	3.0	0.0	4.0	4.0	-1.449275e-02	0.239560	0.044684	0.615385	0.287575	0.234359	0.257668	0.000274	0.350000	0.172840	0.255611	0.003247	0.615385	0.239560	0.044684	0.307692	0.076923	0.169545	0.006240	0.615385	0.239560	0.044684	6.616667	3.563492	5.083122	0.950051	66.992537	28.248894	47.236515	150.711681	4.0	15.0	15.0	0.013600	-2.688625	15.0	14.0	0	1	4	1	1	1	1	3	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	0	2

#using graphs, you can easily check whether two glycans are the same - even if they use different bracket notations!
from glycowork.motif.graph import compare_glycans
print(compare_glycans('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc',
                     'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'))
print(compare_glycans('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc',
                     'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc'))

#or you could find the terminal structures of a glycan
from glycowork.motif.annotate import get_terminal_structures
print("\nTerminal structures:")
print(get_terminal_structures('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'))

True
False

Terminal structures:
['Man(a1-3)', 'Man(a1-6)', 'Fuc(a1-6)']

#given a composition, find matching glycan structures in SugarBase; specific for glycan classes and taxonomy
from glycowork.motif.tokenization import compositions_to_structures
print(compositions_to_structures([{'Hex':3, 'HexNAc':4}], glycan_class = 'N'))

#or we could calculate the mass of this composition
from glycowork.motif.tokenization import composition_to_mass
print("\nMass of the composition Hex3HexNAc4")
print(composition_to_mass({'Hex':3, 'HexNAc':4}))

0 compositions could not be matched. Run with verbose = True to see which compositions.
                                               glycan  abundance
0   GalNAc(b1-4)GlcNAc(b1-2)Man(a1-3)[Man(a1-6)]Ma...          0
1   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Ma...          0
2   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][G...          0
3   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)[GlcNAc(b1-4...          0
4   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-4)][Man(a1-6)]...          0
5   GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-6)Man(a1-6)]Ma...          0
6   GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-6)Man(a1-6)]Ma...          0
7   GlcNAc(b1-2)Man(a1-6)[Man(a1-3)][GlcNAc(b1-4)]...          0
8   Man(a1-3)[GlcNAc(b1-4)][GlcNAc(b1-2)Man(a1-6)]...          0
9   GlcNAc(b1-2)[GlcNAc(b1-4)]Man(a1-3)[GlcNAc(b1-...          0
10  GlcNAc(b1-2)[GlcNAc(b1-4)]Man(a1-3)[Man(a1-6)]...          0
11  GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Ma...          0
12  GlcNAc(?1-?)Man(a1-3)[GlcNAc(?1-?)Man(a1-6)]Ma...          0
13  HexNAc(?1-?)GlcNAc(b1-2)Man(a1-3)[Man(a1-6)]Ma...          0
14  Man(a1-3)[GlcNAc(b1-2)[GlcNAc(b1-6)]Man(a1-6)]...          0
15  GalNAc(b1-4)GlcNAc(b1-2)Man(a1-6)[Man(a1-3)]Ma...          0

Mass of the composition Hex3HexNAc4
1316.4865545999999