ArticleNature communications2024
Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Characterization ofMicrobiology spectrum · 2026Article
- Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 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
- An update for AlphaFold3 versus experimental structures: assessing the precision of small molecule binding in GPCRs.Acta pharmacologica Sinica · 2025Article
- Modeling glycans with AlphaFold 3: capabilities, caveats, and limitations.Glycobiology · 2025Article
- Towards a comprehensive view of the pocketome universe-biological implications and algorithmic challenges.PLoS computational biology · 2025Article
- Analysing protein complexes in plant science: insights and limitation with AlphaFold 3.Botanical studies · 2025Review
- Aligning large language models and geometric deep models for protein representation.Patterns (New York, N.Y.) · 2025Article
- Sugar-sensing swodkoreceptors and swodkocrine signaling.Animal models and experimental medicine · 2025Article
- Deep Learning for Predicting Biomolecular Binding Sites of Proteins.Research (Washington, D.C.) · 2025Review
- Article
- Tools for structural lectinomics: From structures to lectomes.BBA advances · 2025Article
- Advances and Challenges in Scoring Functions for RNA-Protein Complex Structure Prediction.Biomolecules · 2024Review
- Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.Nature communications · 2024Article
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.
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Registered trials
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.