YesU from Bacillus subtilis preferentially binds fucosylated glycans

Tiralongo, Joe; Cooper, Oren; Litfin, Thomas; Yang, Yuedong; King, Rebecca M.; Zhan, Jian; Zhao, Huiying; Bovin, Nicolai V.; Day, Christopher J.; Zhou, Yaoqi

doi:10.1038/s41598-018-31241-8

Cited by 8 publications

(6 citation statements)

References 50 publications

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“…For ADL, LectinOracle correctly inferred the activity of its chitin-binding domain [36] and predicted motifs such as chitotriose as the top binders (Figure S1E, Supporting Information). YesU has been characterized to prefer fucosylated glycans [37] and LectinOracle also predicted fucosylated glycan motifs to be strongly enriched for binding (Figure S1F, Supporting Information), including fucosylated motifs such as Lewis X. These case studies involving lectins outside our data set emphasize that LectinOracle can be used generally to further probe the glycan-binding specificity of lectins, both already characterized as well as uncharacterized.…”

Section: Developing a Deep Learning-based Model To Predict Lectin-glycan Interactionsmentioning

confidence: 55%

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: Developing a Deep Learning-based Model To Predict Lectin-glycan Interactionsmentioning

confidence: 55%

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

Lundstrøm

Korhonen

Lisacek³

et al. 2021

Advanced Science

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show abstract

“…For ADL, LectinOracle correctly inferred the activity of its chitin-binding domain [32] and predicted motifs such as chitotriose as the top binders (Figure S1E). YesU has been characterized to prefer fucosylated glycans [33] and LectinOracle also predicted fucosylated glycan motifs to be strongly enriched for binding (Figure S1F), including fucosylated motifs such as Lewis X. These case studies involving lectins outside our dataset emphasize that LectinOracle can be used generally to further probe the glycan-binding specificity of lectins, both already characterized as well as uncharacterized.…”

Section: Protein Armmentioning

confidence: 59%

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

Lundstrøm

Korhonen

Lisacek

et al. 2021

Preprint

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Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins 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, we present LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction. Using a curated dataset of 564,647 unique protein-glycan interactions, we show that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. We further identify clusters of lectins with related binding specificity that are not clustered based on sequence similarity. Using a range of specialized glycan arrays, we show that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. We further demonstrate that LectinOracle can analyze whole lectomes and their role in host-microbe interactions. We envision that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

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“…This work demonstrated that the APTES-GOPTS ratio can be used to control the surface potential and subsequent cell adhesion. 56 The current GOPTS-generated glycan array was compared to our in-house glycan arrays printed on SE3 slides 39,49 revealing comparable glycosylamine deposition. A statistical comparison of binding and nonbinding events revealed 78.5% homology between the arrays (Supporting Information Figures S1 and S2).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Glycan arrays consisting of 172 diverse glycans (DextraLabs) in the absence of spacers were taken from existing glycan libraries. ,, Glycans were amine functionalized as previously published by Day et al Glycan arrays were fabricated as previously published by Tiralongo et al In brief, glycosylamines were suspended in 1:1 DMF/dimethyl sulfoxide (DMSO) at a concentration of 500 μM and printed onto 3C-SiC GOPTS slides, 3C-SiC APTES slides, and SuperEpoxy3 glass slides (SE3) (ArrayIt) using a SpotBot Extreme array spotter (ArrayIT) in a six-pin subarray print per glass slide format. All glycans were printed in replicates of four, including four AlexaFlour 555/647 and FITC control spots, per subarray using 946MP4 pins and a contact time of 1 s at 50% relative humidity, with pins being reloaded after every 12 spots.…”

Section: Methodsmentioning

confidence: 99%

Functional Microarray Platform with Self-Assembled Monolayers on 3C-Silicon Carbide

et al. 2020

Self Cite

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Currently available bio-platforms such as microarrays and surface plasmon resonators are unable to combine high throughput multiplexing with label-free detection. As such, emerging Micro-Electro-Mechanical Systems (MEMS) and microplasmonics platforms offer the potential for high-resolution, high-throughput label-free sensing of biological and chemical analytes. Therefore, the search for materials capable of combining multiplexing and label free quantitation is of great significance. Recently, interest in silicon carbide (SiC) as a suitable material in numerous biomedical applications has increased due to its well explored chemical inertness, mechanical strength, bio-and hemo-compatibility and the presence of carbon that enables transfer free growth of graphene. SiC is also multifunctional as both a wide band gap semiconductor and an efficient low-loss plasmonics material, and thus is ideal for augmenting current biotransducers in biosensors. Additionally, the cubic variant, 3C-SiC, is an extremely promising material for MEMS, being a suitable platform for the easy micromachining of microcantilevers, it is capable of realizing the potential of real time miniaturized multiplexed assays. However, to realize the full potential of SiC for label free, multiplexed quantitation of biological interactions the generation of an appropriately functionalized and versatile organic monolayer suitable for the immobilization of biomolecules is critical. Herein, we address the use of various silane self-assembled monolayers (SAMs) for covalent functionalization of monocrystalline 3C-SiC films as a novel platform for the generation of functionalized microarray surfaces using high-throughput glycan arrays as the model system. We also demonstrate the ability to robotically print high throughput arrays on free standing SiC microstructures. The implementation of a SiC-based label free glycan array will provide proof-of-principle that could be extended to the immobilization of other biomolecules in a similar SiC-based array format, thus making potentially significant advances to the way biological interactions are studied.

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YesU from Bacillus subtilis preferentially binds fucosylated glycans

Cited by 8 publications

References 50 publications

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

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

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

Functional Microarray Platform with Self-Assembled Monolayers on 3C-Silicon Carbide

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