ArticleNucleic acids research2023
GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- DyProL: Dynamic Ensemble Representation Learning for Protein-Nucleic Acid Binding Site Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A protein surface-aware multimodal framework for residue-level metal-binding site recognition.Cell reports methods · 2026Article
- Decoding Protein-Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2026Article
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SurfFold: a unified model for protein inverse folding by integrating surface and structural information.Bioinformatics (Oxford, England) · 2026Article
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- PLiSAGE: enhancing protein-ligand interaction prediction with multimodal surface and geometry encoding.Bioinformatics (Oxford, England) · 2025Article
- SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions.Genome biology · 2025Article
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model.Briefings in bioinformatics · 2025Article
- INAB: identify nucleic acid binding domain via cross-modal protein language models and multiscale computation.Briefings in bioinformatics · 2025Article
- LABind: identifying protein binding ligand-aware sites via learning interactions between ligand and protein.Nature communications · 2025Article
- Hybrid protein-ligand binding residue prediction with protein language models: does the structure matter?Bioinformatics (Oxford, England) · 2025Article
- Deep Learning for Predicting Biomolecular Binding Sites of Proteins.Research (Washington, D.C.) · 2025Review
- An Online Server for Geometry-Aware Protein Function Annotations Through Predicted Structure.Methods in molecular biology (Clifton, N.J.) · 2025Article
- SpatConv Enables the Accurate Prediction of Protein Binding Sites by a Pretrained Protein Language Model and an Interpretable Bio-spatial Convolution.Research (Washington, D.C.) · 2025Article
- Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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2 authors.
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Abstract
Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.
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