ArticleNucleic acids research2021
GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
83 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
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- Article
- ProRB: a structure-free unified framework for joint prediction and design of protein-RNA interactions.Nucleic acids research · 2026Article
- DyProL: Dynamic Ensemble Representation Learning for Protein-Nucleic Acid Binding Site Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Protein-DNA Binding Sites Prediction via Integrating Pretrained Large Language Models and Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- xBind: an integrated webserver for large language model-enabled cross-molecular protein binding site prediction.Nucleic acids research · 2026Article
- DeepPANB: integrating protein language model with PaiNN equivariant graph neural networks for prediction of protein-nucleic acid binding sites.Briefings in bioinformatics · 2026Article
- Protein-nucleic acid binding site prediction using interpretable Kolmogorov-Arnold networks with hypergraph representation learning.Bioinformatics (Oxford, England) · 2026Article
- RGTBind: RBF-gate graph transformer with spatially biased attention for protein-DNA binding-site prediction.Journal of molecular modeling · 2026Article
- A protocol for single-sequence protein-RNA complex structure prediction using ProRNA3D-single.STAR protocols · 2026Article
- A protein surface-aware multimodal framework for residue-level metal-binding site recognition.Cell reports methods · 2026Article
- Predicting protein-nucleic acid interactions via protein language models with biophysical and evolutionary priors.iScience · 2026Article
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- An interpretable molecular framework for predicting cancer driver missense mutations.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
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.Frontiers in artificial intelligence · 2026Article
- Antimicrobial peptide prediction based on contrastive learning and gated convolutional neural network.Scientific reports · 2025Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.
Indexed as
Identifiers
What OpenQuestion holds
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.