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

Lucas, Taryn M.; Gupta, Chitrak; Altman, Meghan O.; Sanchez, Emi; Naticchia, Matthew R.; Gagneux, Pascal; Singharoy, Abhishek; Godula, Kamil

doi:10.1016/j.chempr.2021.09.015

Cited by 14 publications

(11 citation statements)

References 55 publications

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“…Recent research has shed light on the differences between binding conditions on glycan arrays and physiological presentation of glycans on cells and tissues, [ 70 , 71 , 72 , 73 ] including issues such as crowding, diversity, and linker properties. Additionally, most glycan arrays currently are biased towards mammalian‐like glycans.…”

Section: Discussionmentioning

confidence: 99%

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

Lundstrøm

Korhonen

Lisacek³

et al. 2021

Advanced Science

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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.

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Section: Discussionmentioning

confidence: 99%

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

Lundstrøm

Korhonen

Lisacek³

et al. 2021

Advanced Science

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“…Glycosylation is a post-translational modification (PTM) implicated in viral infection, 1,2 T cell activation and differentiation, 3 reproductive health, 4 and cardiovascular disease. 5 Changes in glycosylation macrohetereogeneity (site occupancy) and microheterogeneity (distinct glycoforms on one site) are known to be relevant in receptor pattern recognition by influenza A viruses, 6 interaction of plasma proteins, 7 cancer, 8,9 and protein/glycan interactions, 10 among many others. This highlights the need to accurately detect changes in glycan localization and composition.…”

Section: Introductionmentioning

confidence: 99%

A systematic comparison of current bioinformatic tools for glycoproteomics data

Rangel-Angarita

Mahoney

Ince

et al. 2022

Preprint

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Glycosylation is one of the most common post-translational modifications and generates an enormous amount of proteomic diversity; changes in glycosylation are associated with nearly all disease states. Intact glycoproteomics seeks to determine the site-localization and composition of glycans along a protein backbone via mass spectrometry. Following data acquisition, raw files are analyzed using search algorithms to define peptide sequence, glycan composition, and site localization. Glycoproteomics is rapidly expanding, creating the pressing need to establish bioinformatic community standards. Recently, several new search algorithms were released, many of which vary in terms of search strategy, localization system, score cutoffs, and glycan databases, thus warranting a comprehensive comparison of these new programs along with existing programs. Here, we analyzed three common samples: an enriched cell lysate, a mixture of 6 glycoproteins, and a mucin-domain glycoprotein. All raw files were searched with comparable parameters among software and the results were extensively manually validated to compare accuracy and completion of the output. Our results highlight the continued need for manual validation of glycopeptide spectral matches, especially for O-glycopeptides. Despite this, O-Pair outperformed all other programs in correct identification of O-glycopeptides and its localization system proved to be useful. On the other hand, Byonic and pGlyco performed best for N-glycoproteomics; the former was best for proteome-wide searches, but the latter identified more N-glycosites in less complex samples. Overall, we summarize the strengths, weaknesses, and potential improvements for these search algorithms.

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“…We also tested the interaction of another oligosaccharide, raffinose, and found no binding. The fact that even in the presence of 25% BA, no binding was observed indicates that indeed the packing of BAs can affect their presentation and may make the receptors inaccessible within the crowded domains. , To verify that the receptor crowding is a result of phase separation due to the immiscibility of DOPC and PCDA-PEG-BA, we performed a FRET experiment, which is commonly used to examine lipid phase separation and nanodomain formation. , A FRET pair, CF-PE as donor and Rh-PE as acceptor dye, with the Förster distance of ∼6.0–6.9 nm was included in the liposome formulation. FRET was measured at temperatures above and below the phase transition temperature of PCDA (∼60 °C).…”

Section: Resultsmentioning

confidence: 99%

Recapitulating the Lateral Organization of Membrane Receptors at the Nanoscale

et al. 2023

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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.

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

Cited by 14 publications

References 55 publications

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

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

A systematic comparison of current bioinformatic tools for glycoproteomics data

Recapitulating the Lateral Organization of Membrane Receptors at the Nanoscale

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