ArticleFrontiers in bioinformatics2023
Structure-based neural network protein-carbohydrate interaction predictions at the residue level.
Article in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Predicting protein-carbohydrate binding sites: a deep learning approach integrating protein language model embeddings and structural features.Briefings in bioinformatics · 2026Article
- Evaluation of De Novo Deep Learning Models on the Protein-Sugar Interactome.bioRxiv : the preprint server for biology · 2025Article
- CAZyme3D: A Database of 3D Structures for Carbohydrate-active Enzymes.Journal of molecular biology · 2025Article
- Tools for structural lectinomics: From structures to lectomes.BBA advances · 2025Article
- The Human Ganglioside Interactome in Live Cells Revealed Using Clickable Photoaffinity Ganglioside Probes.Journal of the American Chemical Society · 2024Article
- PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces.Journal of chemical theory and computation · 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
- Editorial: Structural modeling and computational analyses of immune system molecules.Frontiers in immunology · 2023Article
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Abstract
Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.
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