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

Lundstrøm, Jon; Korhonen, Emma; Lisacek, Frédérique; Bojar, Daniel

doi:10.1101/2021.08.30.458147

Cited by 5 publications

(6 citation statements)

References 58 publications

(61 reference statements)

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“…Future work could investigate potential anti-pathogen functionality of the described structures, in cases where species-specific pathogens are known. Computational methods, predicting binding to pathogen glycan-binding proteins 52 , and screening methods, such as glycan arrays 53 , could shed light on functions of motifs such as the herein discovered MO LacdiNAc, considering its abovementioned connection to Helicobacter pylori 32 .…”

Section: Discussionmentioning

confidence: 99%

Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc

Jin

Lundstrøm

Korhonen

et al. 2023

Preprint

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Breast milk is abundant with functionalized milk oligosaccharides (MOs), to nourish and protect the neonate. Yet we lack a comprehensive understanding of the repertoire and evolution of MOs across Mammalia. We report ≈400 MO-species associations (>100 novel structures) from milk glycomics of nine mostly understudied species: alpaca, beluga whale, black rhinoceros, bottlenose dolphin, impala, L'Hoest's monkey, pygmy hippopotamus, domestic sheep, and striped dolphin. This revealed the hitherto unknown existence of the LacdiNAc motif (GalNAcβ1-4GlcNAc) in MOs of all species except alpaca, sheep, and striped dolphin, indicating widespread occurrence of this potentially antimicrobial motif in MOs. We also characterize glucuronic acid-containing MOs in the milk of impala, dolphins, sheep, and rhinoceros, previously only reported in cows. We demonstrate that these GlcA-MOs exhibit potent immunomodulatory effects. Our study extends the number of known MOs by >15%. Combined with >1,900 curated MO-species associations, we characterize MO motif distributions, presenting an exhaustive overview of MO biodiversity.

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Section: Discussionmentioning

confidence: 99%

Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc

Jin

Lundstrøm

Korhonen

et al. 2023

Preprint

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“…This can aid lectin categorization, annotation, and utility for researchers working with these lectins. This concept has recently been taken further with LectinOracle, a deep learning algorithm that utilizes protein and glycan sequences to predict lectin specificity. To annotate the specificities of glycan binding proteins more directly from glycan microarray data, multiple algorithms including frequent subtree mining , and motif based approaches have been developed. , Yet the high diversity and nonlinearity of glycans has stymied the large-scale evaluation of subtle, interpretable binding motifs in glycan array data to date.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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 welldefined, making it difficult to leverage their full potential for glycan analysis. Herein, we use glycan microarray analysis of 116 commercially available lectins, including different preparations of the same lectin, to extract the specific glycan features required for lectin binding. Data was obtained using the Consortium for Functional Glycomics microarray (CFG v5.0) containing 611 glycans. We use a combination of machine learning algorithms to define lectin specificity, mapping inputs (glycan sequences) to outputs (lectin-glycan binding) for a large-scale evaluation of lectin-glycan binding behaviours. 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. Using a combination of machine learning and manual annotation of the data, we created 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.

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“…Anti-pathogen functions of MOs usually hinge on terminal motifs (sialylation, fucosylation, etc.) 5,6 that can serve as binding epitopes, indicating the importance of the motif repertoire for MO function 3 .…”

Section: Introductionmentioning

confidence: 99%

Mammalian Milk Glycomes: Connecting the Dots between Evolutionary Conservation and Biosynthetic Pathways

Thomès

Karlsson

Lundstrøm

et al. 2023

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Milk oligosaccharides (MOs) are among the most abundant constituents of breast milk and are essential for health and development. Biosynthesized from monosaccharides into complex sequences, MOs differ considerably between taxonomic groups. Even human MO biosynthesis is insufficiently understood, hampering evolutionary and functional analyses. Using a comprehensive resource of all published MOs from greater than 100 mammals, we develop a nonparametric pipeline for generating and analyzing MO biosynthetic networks, which readily generalizes to other glycan classes. We then use evolutionary relationships and inferred intermediates of these networks to discover (i) distributional glycome biases, (ii) biosynthetic restrictions, such as reaction path dependence, and (iii) conserved biosynthetic modules. This allows us to prune and pinpoint biosynthetic pathways despite missing information. Machine learning and network analysis cluster species by their milk glycome, identifying characteristic sequence relationships and evolutionary gains/losses of motifs, MOs, and biosynthetic modules. These resources and analyses will advance our understanding of glycan biosynthesis and the evolution of breast milk.

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

Cited by 5 publications

References 58 publications

Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc

Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc

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

Mammalian Milk Glycomes: Connecting the Dots between Evolutionary Conservation and Biosynthetic Pathways

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