Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses

Infection and the Glycome─New Insights into Host Response

Glycans play critical roles in the host-pathogen interactions leading to infection. However, we still understand very little about the dynamic nature of glycosylation in response to infection and its function in modulating host immunity. Many of the host proteins involved in immune defense are glycoproteins. Furthermore, the innate immune system recognizes glycans. The glycoform of a protein can impact proteolytic stability, receptor interactions, serum half-life, and other aspects. New, cutting-edge chemical biology tools are shedding light on the interplay between infection and the host glycome. In this review, we highlight new work on the importance of dynamic glycosylation of host proteins in the innate and adaptive immune pathways in response to infection. These include recent findings on altered glycoprofiles of mucins, complement components, and antibodies.

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.

Serum antibody screening using glycan arrays

Humans and other animals produce a diverse collection of antibodies, many of which bind to carbohydrate chains, referred to as glycans. These anti-glycan antibodies are a critical part of our immune systems' defenses. Whether induced by vaccination or natural exposure to a pathogen, anti-glycan antibodies can provide protection against infections and cancers. Alternatively, when an immune response goes awry, antibodies that recognize self-glycans can mediate autoimmune diseases. In any case, serum anti-glycan antibodies provide a rich source of information about a patient's overall health, vaccination history, and disease status. Glycan microarrays provide a high-throughput platform to rapidly interrogate serum anti-glycan antibodies and identify new biomarkers for a variety of conditions. In addition, glycan microarrays enable detailed analysis of the immune system's response to vaccines and other treatments. Herein we review applications of glycan microarray technology for serum anti-glycan antibody profiling.

False-Positive Glycopeptide Identification via In-FAIMS Fragmentation

High-field asymmetric waveform ion mobility spectrometry (FAIMS) separates glycopeptides in the gas phase prior to mass spectrometry (MS) analysis, thus offering the potential to analyze glycopeptides without prior enrichment. Several studies have demonstrated the ability of FAIMS to enhance glycopeptide detection but have primarily focused on N-glycosylation. Here, we evaluated FAIMS for O-glycoprotein and mucin-domain glycoprotein analysis using samples of varying complexity. We demonstrated that FAIMS was useful in increasingly complex samples as it allowed for the identification of more glycosylated species. However, during our analyses, we observed a phenomenon called "in FAIMS fragmentation" (IFF) akin to in source fragmentation but occurring during FAIMS separation. FAIMS experiments showed a 2- to 5-fold increase in spectral matches from IFF compared with control experiments. These results were also replicated in previously published data, indicating that this is likely a systemic occurrence when using FAIMS. Our study highlights that although there are potential benefits to using FAIMS separation, caution must be exercised in data analysis because of prevalent IFF, which may limit its applicability in the broader field of O-glycoproteomics.

Application of bioanalytical and computational methods in decoding the roles of glycans in host-pathogen interactions

Host-pathogen interactions (HPIs) are complex processes that require tight regulation. A common regulatory mechanism of HPIs is through glycans of either host cells or pathogens. Due to their diverse sequences, complex structures, and conformations, studies of glycans require highly sensitive and powerful tools. Recent improvements in technology have enabled the application of many bioanalytical techniques and modeling methods to investigate glycans and their mechanisms in HPIs. This mini-review highlights how these advances have been used to understand the role glycans play in HPIs in the past 2 years.

Recapitulating the Lateral Organization of Membrane Receptors at the Nanoscale

Many cell membrane functions emerge from the lateral presentation of membrane receptors. The link between the nanoscale organization of the receptors and ligand binding remains, however, mostly unclear. In this work, we applied surface molecular imprinting and utilized the phase behavior of lipid bilayers to create platforms that recapitulate the lateral organization of membrane receptors at the nanoscale. We used liposomes decorated with amphiphilic boronic acids that commonly serve as synthetic saccharide receptors and generated three lateral modes of receptor presentation─random distribution, nanoclustering, and receptor crowding─and studied their interaction with saccharides. In comparison to liposomes with randomly dispersed receptors, surface-imprinted liposomes resulted in more than a 5-fold increase in avidity. Quantifying the binding affinity and cooperativity proved that the boost was mediated by the formation of the nanoclusters rather than a local increase in the receptor concentration. In contrast, receptor crowding, despite the presence of increased local receptor concentrations, prevented multivalent oligosaccharide binding due to steric effects. The findings demonstrate the significance of nanometric aspects of receptor presentation and generation of multivalent ligands including artificial lectins for the sensitive and specific detection of glycans.

Synthesis and biomedical applications of mucin mimic materials

Mucin glycoproteins are the major component of mucus and coat epithelial cell surfaces forming the glycocalyx. The glycocalyx and mucus are involved in the transport of nutrients, drugs, gases, and pathogens toward the cell surface. Mucins are also involved in diverse diseases such as cystic fibrosis and cancer. Due to inherent heterogeneity in native mucin structure, many synthetic materials have been designed to probe mucin chemistry, biology, and physics. Such materials include various glycopolymers, low molecular weight glycopeptides, glycopolypeptides, polysaccharides, and polysaccharide-protein conjugates. This review highlights advances in the area of design and synthesis of mucin mimic materials, and their biomedical applications in glycan binding, epithelial models of infection, therapeutic delivery, vaccine formulation, and beyond.

Measuring the multifaceted roles of mucin-domain glycoproteins in cancer

Mucin-domain glycoproteins are highly O-glycosylated cell surface and secreted proteins that serve as both biochemical and biophysical modulators. Aberrant expression and glycosylation of mucins are known hallmarks in numerous malignancies, yet mucin-domain glycoproteins remain enigmatic in the broad landscape of cancer glycobiology. Here we review the multifaceted roles of mucins in cancer through the lens of the analytical and biochemical methods used to study them. We also describe a collection of emerging tools that are specifically equipped to characterize mucin-domain glycoproteins in complex biological backgrounds. These approaches are poised to further elucidate how mucin biology can be understood and subsequently targeted for the next generation of cancer therapeutics.

Current strategies for characterization of mucin-domain glycoproteins

Glycosylation, and especially O-linked glycosylation, remains a critical blind spot in the understanding of post-translational modifications. Due to their nature as proteins defined by a large density and abundance of O-glycosylation, mucins present extra challenges in the analysis of their structure and function. However, recent breakthroughs in multiple areas of research have rendered mucin-domain glycoproteins more accessible to current characterization techniques. In particular, the adaptation of mucinases to glycoproteomic workflows, the manipulation of cellular glycosylation pathways, and the advances in synthetic methods to more closely mimic mucin domains have introduced new and exciting avenues to study mucin glycoproteins. Here, we summarize recent developments in understanding the structure and biological function of mucin domains and their associated glycans, from glycoproteomic tools and visualization methods to synthetic glycopeptide mimetics.

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.