ArticleNucleic acids research2024
HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- The Zinc Finger Ran-Binding Protein 3 (ZRANB3): An Advanced Perspective.International journal of molecular sciences · 2026Review
- Challenges in Applying DNA-Binding Protein Predictors to Biological Research.International journal of molecular sciences · 2025Article
- Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025Article
- Evidence for vitellogenin DNA-binding in honey bees.Protein science : a publication of the Protein Society · 2025Article
- Predicting nucleic acid binding sites by attention map-guided graph convolutional network with protein language embeddings and physicochemical information.Briefings in bioinformatics · 2025Article
- MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model.Briefings in bioinformatics · 2025Article
- Uncovering the Molecular Interactions Underlying MBD2 and MBD3 Phase Separation.The journal of physical chemistry. B · 2025Article
- Explainable Deep Multilevel Attention Learning for Predicting Protein Carbonylation Sites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Prediction of Intrinsic Disorder Functions with DEPICTER2.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.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
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins.Biomolecules · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Current predictors of DNA-binding residues (DBRs) from protein sequences belong to two distinct groups, those trained on binding annotations extracted from structured protein-DNA complexes (structure-trained) vs. intrinsically disordered proteins (disorder-trained). We complete the first empirical analysis of predictive performance across the structure- and disorder-annotated proteins for a representative collection of ten predictors. Majority of the structure-trained tools perform well on the structure-annotated proteins while doing relatively poorly on the disorder-annotated proteins, and vice versa. Several methods make accurate predictions for the structure-annotated proteins or the disorder-annotated proteins, but none performs highly accurately for both annotation types. Moreover, most predictors make excessive cross-predictions for the disorder-annotated proteins, where residues that interact with non-DNA ligand types are predicted as DBRs. Motivated by these results, we design, validate and deploy an innovative meta-model, hybridDBRpred, that uses deep transformer network to combine predictions generated by three best current predictors. HybridDBRpred provides accurate predictions and low levels of cross-predictions across the two annotation types, and is statistically more accurate than each of the ten tools and baseline meta-predictors that rely on averaging and logistic regression. We deploy hybridDBRpred as a convenient web server at http://biomine.cs.vcu.edu/servers/hybridDBRpred/ and provide the corresponding source code at https://github.com/jianzhang-xynu/hybridDBRpred.
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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.