Development and application of an algorithm to compute weighted multiple glycan alignments

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.

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/.

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.

Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy

Cancer immunotherapy has revolutionized treatment and led to an unprecedented wave of immuno-oncology research during the past two decades. In 2018, two pioneer immunotherapy innovators, Tasuku Honjo and James P. Allison, were awarded the Nobel Prize for their landmark cancer immunotherapy work regarding “cancer therapy by inhibition of negative immune regulation” –CTLA4 and PD-1 immune checkpoints. However, the challenge in the coming decade is to develop cancer immunotherapies that can more consistently treat various patients and cancer types. Overcoming this challenge requires a systemic understanding of the underlying interactions between immune cells, tumor cells, and immunotherapeutics. The role of aberrant glycosylation in this process, and how it influences tumor immunity and immunotherapy is beginning to emerge. Herein, we review current knowledge of miRNA-mediated regulatory mechanisms of glycosylation machinery, and how these carbohydrate moieties impact immune cell and tumor cell interactions. We discuss these insights in the context of clinical findings and provide an outlook on modulating the regulation of glycosylation to offer new therapeutic opportunities. Finally, in the coming age of systems glycobiology, we highlight how emerging technologies in systems glycobiology are enabling deeper insights into cancer immuno-oncology, helping identify novel drug targets and key biomarkers of cancer, and facilitating the rational design of glyco-immunotherapies. These hold great promise clinically in the immuno-oncology field.

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.

Identifying glycan motifs using a novel subtree mining approach

Background

Glycans are complex sugar chains, crucial to many biological processes. By participating in binding interactions with proteins, glycans often play key roles in host–pathogen interactions. The specificities of glycan-binding proteins, such as lectins and antibodies, are governed by motifs within larger glycan structures, and improved characterisations of these determinants would aid research into human diseases. Identification of motifs has previously been approached as a frequent subtree mining problem, and we extend these approaches with a glycan notation that allows recognition of terminal motifs.

Results

In this work, we customised a frequent subtree mining approach by altering the glycan notation to include information on terminal connections. This allows specific identification of terminal residues as potential motifs, better capturing the complexity of glycan-binding interactions. We achieved this by including additional nodes in a graph representation of the glycan structure to indicate the presence or absence of a linkage at particular backbone carbon positions. Combining this frequent subtree mining approach with a state-of-the-art feature selection algorithm termed minimum-redundancy, maximum-relevance (mRMR), we have generated a classification pipeline that is trained on data from a glycan microarray. When applied to a set of commonly used lectins, the identified motifs were consistent with known binding determinants. Furthermore, logistic regression classifiers trained using these motifs performed well across most lectins examined, with a median AUC value of 0.89.

Conclusions

We present here a new subtree mining approach for the classification of glycan binding and identification of potential binding motifs. The Carbohydrate Classification Accounting for Restricted Linkages (CCARL) method will assist in the interpretation of glycan microarray experiments and will aid in the discovery of novel binding motifs for further experimental characterisation.

Advances in Tools to Determine the Glycan-Binding Specificities of Lectins and Antibodies

Proteins that bind carbohydrate structures can serve as tools to quantify or localize specific glycans in biological specimens. Such proteins, including lectins and glycan-binding antibodies, are particularly valuable if accurate information is available about the glycans that a protein binds. Glycan arrays have been transformational for uncovering rich information about the nuances and complexities of glycan-binding specificity. A challenge, however, has been the analysis of the data. Because protein-glycan interactions are so complex, simplistic modes of analyzing the data and describing glycan-binding specificities have proven inadequate in many cases. This review surveys the methods for handling high-content data on protein-glycan interactions. We contrast the approaches that have been demonstrated and provide an overview of the resources that are available. We also give an outlook on the promising experimental technologies for generating new insights into protein-glycan interactions, as well as a perspective on the limitations that currently face the field.

Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees

This chapter describes the Multiple Carbohydrate Alignment with Weights (MCAW) tool, which is available as a part of the RINGS (Resource for INformatics of Glycomes at Soka) website. It implements a combination of KCaM (Aoki, Yamaguchi, Ueda, et al., Nucl Acids Res 32:W267-W272, 2004), a pairwise glycan alignment algorithm, and ClustalW (Thompson, Higgins, Gibson, Nucleic Acids Res 22:4673-80, 1994), a weighted multiple protein sequence alignment algorithm. This tool computes the multiple glycan alignment by first computing a guide tree to determine the order by which to progressively add glycans to the multiple alignment. The dynamic programming algorithm results in a glycan profile of the alignment glycans, containing "monosaccharide positions" indicating the ratio of monosaccharides and their glycosidic bonds that are aligned at the corresponding position. This tool has been used to analyze databases of glycan array experimental data, incorporating weights to reflect the biological significance of certain glycans over others. As a result, it has been shown that the alignments obtained are biologically relevant, matching the results as found in the literature.