ArticleJournal of chemical theory and computation2024
PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Predicting protein-carbohydrate binding sites: a deep learning approach integrating protein language model embeddings and structural features.Briefings in bioinformatics · 2026Article
- An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein.PloS one · 2026Article
- Structural characterization of an extracellular contractile injection system from Photorhabdus luminescens in extended and contracted states.Nature communications · 2025Article
- Evaluation of De Novo Deep Learning Models on the Protein-Sugar Interactome.bioRxiv : the preprint server for biology · 2025Article
- Towards a comprehensive view of the pocketome universe-biological implications and algorithmic challenges.PLoS computational biology · 2025Article
- A new age in structural S-layer biology: Experimental and in silico milestones.The Journal of biological chemistry · 2025Review
- Tools for structural lectinomics: From structures to lectomes.BBA advances · 2025Article
- Context-aware geometric deep learning for protein sequence design.Nature communications · 2024Article
- Computational toolbox for the analysis of protein-glycan interactions.Beilstein journal of organic chemistry · 2024Review
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3 authors.
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Abstract
The Protein Structure Transformer (PeSTo), a geometric transformer, has exhibited exceptional performance in predicting protein-protein binding interfaces and distinguishing interfaces with nucleic acids, lipids, small molecules, and ions. In this study, we introduce PeSTo-Carbs, an extension of PeSTo specifically engineered to predict protein-carbohydrate binding interfaces. We evaluate the performance of this approach using independent test sets and compare them with those of previous methods. Furthermore, we highlight the model's capability to specialize in predicting interfaces involving cyclodextrins, a biologically and pharmaceutically significant class of carbohydrates. Our method consistently achieves remarkable accuracy despite the scarcity of available structural data for cyclodextrins.
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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.