Identifying glycan motifs using a novel subtree mining approach

Glycosaminoglycan microarrays for studying glycosaminoglycan-protein systems

More than 3000 proteins are now known to bind to glycosaminoglycans (GAGs). Yet, GAG-protein systems are rather poorly understood in terms of selectivity of recognition, molecular mechanism of action, and translational promise. High-throughput screening (HTS) technologies are critically needed for studying GAG biology and developing GAG-based therapeutics. Microarrays, developed within the past two decades, have now improved to the point of being the preferred tool in the HTS of biomolecules. GAG microarrays, in which GAG sequences are immobilized on slides, while similar to other microarrays, have their own sets of challenges and considerations. GAG microarrays are rapidly becoming the first choice in studying GAG-protein systems. Here, we review different modalities and applications of GAG microarrays presented to date. We discuss advantages and disadvantages of this technology, explain covalent and non-covalent immobilization strategies using different chemically reactive groups, and present various assay formats for qualitative and quantitative interpretations, including selectivity screening, binding affinity studies, competitive binding studies etc. We also highlight recent advances in implementing this technology, cataloging of data, and project its future promise. Overall, the technology of GAG microarray exhibits enormous potential of evolving into more than a mere screening tool for studying GAG - protein systems.

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.

Decoding glycomics with a suite of methods for differential expression analysis

Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.

Simple and practical sialoglycan encoding system reveals vast diversity in nature and identifies a universal sialoglycan-recognizing probe derived from AB5 toxin B subunits

Vertebrate sialic acids (Sias) display much diversity in modifications, linkages, and underlying glycans. Slide microarrays allow high-throughput explorations of sialoglycan-protein interactions. A microarray presenting ~150 structurally defined sialyltrisaccharides with various Sias linkages and modifications still poses challenges in planning, data sorting, visualization, and analysis. To address these issues, we devised a simple 9-digit code for sialyltrisaccharides with terminal Sias and underlying two monosaccharides assigned from the nonreducing end, with 3 digits assigning a monosaccharide, its modifications, and linkage. Calculations based on the encoding system reveal >113,000 likely linear sialyltrisaccharides in nature. Notably, a biantennary N-glycan with 2 terminal sialyltrisaccharides could thus have >1010 potential combinations and a triantennary N-glycan with 3 terminal sequences, >1015 potential combinations. While all possibilities likely do not exist in nature, sialoglycans encode enormous diversity. While glycomic approaches are used to probe such diverse sialomes, naturally occurring bacterial AB5 toxin B subunits are simpler tools to track the dynamic sialome in biological systems. Sialoglycan microarray was utilized to compare sialoglycan-recognizing bacterial toxin B subunits. Unlike the poor correlation between B subunits and species phylogeny, there is stronger correlation with Sia-epitope preferences. Further supporting this pattern, we report a B subunit (YenB) from Yersinia enterocolitica (broad host range) recognizing almost all sialoglycans in the microarray, including 4-O-acetylated-Sias not recognized by a Yersinia pestis orthologue (YpeB). Differential Sia-binding patterns were also observed with phylogenetically related B subunits from Escherichia coli (SubB), Salmonella Typhi (PltB), Salmonella Typhimurium (ArtB), extra-intestinal E.coli (EcPltB), Vibrio cholera (CtxB), and cholera family homologue of E. coli (EcxB).

A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities

Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving; however, the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well-defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use a combination of machine learning algorithms and expert annotation to define lectin specificity for this important probe set. Our analysis uses comprehensive glycan microarray analysis of commercially available lectins we obtained using version 5.0 of the Consortium for Functional Glycomics glycan microarray (CFGv5). This data set was made public in 2011. We report the creation of this data set and its use in large-scale evaluation of lectin-glycan binding behaviors. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides and linkages. Combining machine learning with manual annotation, we create a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

GlyNet: a multi-task neural network for predicting protein-glycan interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan–protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet's output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein–glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology.

GlyNet, a neural net model of glycan-protein binding strengths. Given a glycan it outputs binding to each of several protein samples. Reproducing glycan array data, it extrapolates the binding of untested glycans against the protein samples.

Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning

The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input. To address this, we developed a chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules. Our work enables quantitative chemistry-informed decision-making and iterative design in the macromolecular chemical space.

LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction

Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host-microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

OUP accepted manuscript

Glycan arrays continue to be the primary resource for determining the glycan-binding specificity of proteins. The volume and diversity of glycan-array data are increasing, but no common method and resource exist to analyze, integrate, and use the available data. To meet this need, we developed a resource of analyzed glycan-array data called CarboGrove. Using the ability to process and interpret data from any type of glycan array, we populated the database with the results from 35 types of glycan arrays, 13 glycan families, 5 experimental methods, and 19 laboratories or companies. In meta-analyses of glycan-binding proteins, we observed glycan-binding specificities that were not uncovered from single sources. In addition, we confirmed the ability to efficiently optimize selections of glycan-binding proteins to be used in experiments for discriminating between closely related motifs. Through descriptive reports and a programmatically accessible Application Programming Interface, CarboGrove yields unprecedented access to the wealth of glycan-array data being produced and powerful capabilities for both experimentalists and bioinformaticians.

Dealing with the Ambiguity of Glycan Substructure Search

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion.

Glycowork: A Python package for glycan data science and machine learning

While glycans are crucial for biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include these diverse carbohydrates into workflows. Here, we present glycowork, an open-source Python package designed for glycan-related data science and machine learning by end users. Glycowork includes functions to, for instance, automatically annotate glycan motifs and analyze their distributions via heatmaps and statistical enrichment. We also provide visualization methods, routines to interact with stored databases, trained machine learning models and learned glycan representations. We envision that glycowork can extract further insights from glycan datasets and demonstrate this with workflows that analyze glycan motifs in various biological contexts. Glycowork can be freely accessed at https://github.com/BojarLab/glycowork/.

Profiling the glycome of Cardicola forsteri, a blood fluke parasitic to bluefin tuna

Infections by blood flukes (Cardicola spp.) are considered the most significant health issue for ranched bluefin tuna, a major aquaculture industry in Japan and Australia. The host-parasite interfaces of trematodes, namely their teguments, are particularly rich in carbohydrates, which function both in evasion and modulation of the host immune system, while some are primary antigenic targets. In this study, histochemistry and mass spectrometry techniques were used to profile the glycans of Cardicola forsteri. Fluorescent lectin staining of adult flukes indicates the presence of oligomannose (Concanavalin A-reactive) and fucosylated (Pisum sativum agglutinin-reactive) N-glycans. Additionally, reactivity of succinylated wheat germ agglutinin (s-WGA) was localised to several internal organs of the digestive and monoecious reproductive systems. Glycan structures were further investigated with tandem mass spectrometry, which revealed structures indicated by lectin reactivity. While O-glycans from these adult specimens were not detectable by mass spectrometry, several oligomannose, paucimannosidic, and complex-type N-glycans were identified, including some carrying hexuronic acid and many carrying core xylose. This is, to our knowledge, the first glycomic characterisation of a marine platyhelminth, with broader implications for research into other trematodes.

Mass Spectrometry Imaging for Glycome in the Brain

Glycans are diverse structured biomolecules that play crucial roles in various biological processes. Glycosylation, an enzymatic system through which various glycans are bound to proteins and lipids, is the most common and functionally crucial post-translational modification process. It is known to be associated with brain development, signal transduction, molecular trafficking, neurodegenerative disorders, psychopathologies, and brain cancers. Glycans in glycoproteins and glycolipids expressed in brain cells are involved in neuronal development, biological processes, and central nervous system maintenance. The composition and expression of glycans are known to change during those physiological processes. Therefore, imaging of glycans and the glycoconjugates in the brain regions has become a “hot” topic nowadays. Imaging techniques using lectins, antibodies, and chemical reporters are traditionally used for glycan detection. However, those techniques offer limited glycome detection. Mass spectrometry imaging (MSI) is an evolving field that combines mass spectrometry with histology allowing spatial and label-free visualization of molecules in the brain. In the last decades, several studies have employed MSI for glycome imaging in brain tissues. The current state of MSI uses on-tissue enzymatic digestion or chemical reaction to facilitate successful glycome imaging. Here, we reviewed the available literature that applied MSI techniques for glycome visualization and characterization in the brain. We also described the general methodologies for glycome MSI and discussed its potential use in the three-dimensional MSI in the brain.

Combined Analysis of Multiple Glycan-Array Datasets: New Explorations of Protein-Glycan Interactions

Glycan arrays are indispensable for learning about the specificities of glycan-binding proteins. Despite the abundance of available data, the current analysis methods do not have the ability to interpret and use the variety of data types and to integrate information across datasets. Here, we evaluated whether a novel, automated algorithm for glycan-array analysis could meet that need. We developed a regression-tree algorithm with simultaneous motif optimization and packaged it in software called MotifFinder. We applied the software to analyze data from eight different glycan-array platforms with widely divergent characteristics and observed an accurate analysis of each dataset. We then evaluated the feasibility and value of the combined analyses of multiple datasets. In an integrated analysis of datasets covering multiple lectin concentrations, the software determined approximate binding constants for distinct motifs and identified major differences between the motifs that were not apparent from single-concentration analyses. Furthermore, an integrated analysis of data sources with complementary sets of glycans produced broader views of lectin specificity than produced by the analysis of just one data source. MotifFinder, therefore, enables the optimal use of the expanding resource of the glycan-array data and promises to advance the studies of protein-glycan interactions.

Using graph convolutional neural networks to learn a representation for glycans

As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes—from protein folding to viral cell entry—yet are still not well understood. There are few computational methods to link glycan sequences to functions, and they do not fully leverage all available information about glycans. SweetNet is a graph convolutional neural network that uses graph representation learning to facilitate a computational understanding of glycobiology. SweetNet explicitly incorporates the nonlinear nature of glycans and establishes a framework to map any glycan sequence to a representation. We show that SweetNet outperforms other computational methods in predicting glycan properties on all reported tasks. More importantly, we show that glycan representations, learned by SweetNet, are predictive of organismal phenotypic and environmental properties. Finally, we use glycan-focused machine learning to predict viral glycan binding, which can be used to discover viral receptors.

Burkholz et al. develop an analysis platform for glycans, using graph convolutional neural networks, that considers the branched nature of these carbohydrates. They demonstrate that glycan-focused machine learning can be employed for various purposes, such as to cluster species according to their glycomic similarity or to identify viral receptors.

Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication

Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.

Molecular and structural basis for Lewis glycan recognition by a cancer-targeting antibody

Immunotherapy has been successful in treating many tumour types. The development of additional tumour-antigen binding monoclonal antibodies (mAbs) will help expand the range of immunotherapeutic targets. Lewis histo-blood group and related glycans are overexpressed on many carcinomas, including those of the colon, lung, breast, prostate and ovary, and can therefore be selectively targeted by mAbs. Here we examine the molecular and structural basis for recognition of extended Lea and Lex containing glycans by a chimeric mAb. Both the murine (FG88.2) IgG3 and a chimeric (ch88.2) IgG1 mAb variants showed reactivity to colorectal cancer cells leading to significantly reduced cell viability. We determined the X-ray structure of the unliganded ch88.2 fragment antigen-binding (Fab) containing two Fabs in the unit cell. A combination of molecular docking, glycan grafting and molecular dynamics simulations predicts two distinct subsites for recognition of Lea and Lex trisaccharides. While light chain residues were exclusively used for Lea binding, recognition of Lex involved both light and heavy chain residues. An extended groove is predicted to accommodate the Lea-Lex hexasaccharide with adjoining subsites for each trisaccharide. The molecular and structural details of the ch88.2 mAb presented here provide insight into its cross-reactivity for various Lea and Lex containing glycans. Furthermore, the predicted interactions with extended epitopes likely explains the selectivity of this antibody for targeting Lewis-positive tumours.

Tools for generating and analyzing glycan microarray data

Glycans are one of the major biological polymers found in the mammalian body. They play a vital role in a number of physiologic and pathologic conditions. Glycan microarrays allow a plethora of information to be obtained on protein–glycan binding interactions. In this review, we describe the intricacies of the generation of glycan microarray data and the experimental methods for studying binding. We highlight the importance of this knowledge before moving on to the data analysis. We then highlight a number of tools for the analysis of glycan microarray data such as data repositories, data visualization and manual analysis tools, automated analysis tools and structural informatics tools.