ArticleNucleic acids research2017
DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.
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Who cites it
101 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
- VAEBAC: a representation-learning framework for proteome-scale prediction of amyloidogenic proteins and functional stratification of nucleic-acid-binding proteins.Bioinformatics (Oxford, England) · 2026Article
- 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
- 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
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- Protein-nucleic acid binding site prediction using interpretable Kolmogorov-Arnold networks with hypergraph representation learning.Bioinformatics (Oxford, England) · 2026Article
- Predicting protein-nucleic acid interactions via protein language models with biophysical and evolutionary priors.iScience · 2026Article
- Transcriptomic analysis of Rnq1 loss and prionization reveals alterations in translation pathways and energy metabolism.Scientific reports · 2026Article
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- Prediction of Intrinsically Disordered Regions That Bind RNA with DisoRDPbind.Methods in molecular biology (Clifton, N.J.) · 2026Article
- PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.Frontiers in artificial intelligence · 2026Article
- A novel prediction method for protein-DNA binding sites based on protein language model fusion features with SE-connection pyramidal network and ensemble learning.BMC genomics · 2025Article
- Challenges in Applying DNA-Binding Protein Predictors to Biological Research.International journal of molecular sciences · 2025Article
- INAB: identify nucleic acid binding domain via cross-modal protein language models and multiscale computation.Briefings in bioinformatics · 2025Article
- Predicting nucleic acid binding sites by attention map-guided graph convolutional network with protein language embeddings and physicochemical information.Briefings in bioinformatics · 2025Article
- Article
- Unravelling the conundrum of nucleolar NR2F1 localization using antibody-based approaches in vitro and in vivo.Communications biology · 2025Article
- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
- Centromeric localization of αKNL2 and CENP-C proteins in plants depends on their centromere-targeting domain and DNA-binding regions.Nucleic acids research · 2025Article
41 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Protein-DNA and protein-RNA interactions are part of many diverse and essential cellular functions and yet most of them remain to be discovered and characterized. Recent research shows that sequence-based predictors of DNA-binding residues accurately find these residues but also cross-predict many RNA-binding residues as DNA-binding, and vice versa. Most of these methods are also relatively slow, prohibiting applications on the whole-genome scale. We describe a novel sequence-based method, DRNApred, which accurately and in high-throughput predicts and discriminates between DNA- and RNA-binding residues. DRNApred was designed using a new dataset with both DNA- and RNA-binding proteins, regression that penalizes cross-predictions, and a novel two-layered architecture. DRNApred outperforms state-of-the-art predictors of DNA- or RNA-binding residues on a benchmark test dataset by substantially reducing the cross predictions and predicting arguably higher quality false positives that are located nearby the native binding residues. Moreover, it also more accurately predicts the DNA- and RNA-binding proteins. Application on the human proteome confirms that DRNApred reduces the cross predictions among the native nucleic acid binders. Also, novel putative DNA/RNA-binding proteins that it predicts share similar subcellular locations and residue charge profiles with the known native binding proteins. Webserver of DRNApred is freely available at http://biomine.cs.vcu.edu/servers/DRNApred/.
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