ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- CLIMBS: Assessing Carbohydrate-Protein Interactions through a Graph Neural Network Classifier Using Synthetic Negative Data.Journal of chemical information and modeling · 2026Article
- Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Engineered OAA lectins as selective and sensitive high mannose glycan targeting tools.bioRxiv : the preprint server for biology · 2026Article
- Understanding glycan structure and function through artificial intelligence.BBA advances · 2026Review
- GlyContact analyzes glycan 3D structures at scale.Nature communications · 2025Article
- Atom-level machine learning of protein-glycan interactions and cross-chiral recognition in glycobiology.Science advances · 2025Article
- Evaluation of De Novo Deep Learning Models on the Protein-Sugar Interactome.bioRxiv : the preprint server for biology · 2025Article
- Biochemical Applications of Microbial Rare Glycan Biosynthesis, Recognition, and Sequencing.Biochemistry · 2025Review
- Bridging worlds: connecting glycan representations with glycoinformatics via Universal Input and a canonicalized nomenclature.Bioinformatics advances · 2025Article
- Article
- Tools for structural lectinomics: From structures to lectomes.BBA advances · 2025Article
- LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.bioRxiv : the preprint server for biology · 2024Article
- HumanLectome, an update of UniLectin for the annotation and prediction of human lectins.Nucleic acids research · 2024Article
- Computational toolbox for the analysis of protein-glycan interactions.Beilstein journal of organic chemistry · 2024Review
- Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions.Frontiers in molecular biosciences · 2024Article
- Decoding glycomics with a suite of methods for differential expression analysis.Cell reports methods · 2023Article
- GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES.Journal of cheminformatics · 2023Article
- Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level.bioRxiv : the preprint server for biology · 2023Article
- (Glycan Binding) Activity-Based Protein Profiling in Cells Enabled by Mass Spectrometry-Based Proteomics.Israel journal of chemistry · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.