ArticleNucleic acids research2014
Identifying RNA-binding residues based on evolutionary conserved structural and energetic features.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
- Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026Article
- EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.The protein journal · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Role of Optimization in RNA-Protein-Binding Prediction.Current issues in molecular biology · 2024Article
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
- Computational tools to study RNA-protein complexes.Frontiers in molecular biosciences · 2022Review
- A Max-Margin Model for Predicting Residue-Base Contacts in Protein-RNA Interactions.Life (Basel, Switzerland) · 2021Article
- GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues.Nucleic acids research · 2021Article
- How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms.Molecular cell · 2020Review
- A Graph Approach to Mining Biological Patterns in the Binding Interfaces.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Protein-RNA interactions: structural biology and computational modeling techniques.Biophysical reviews · 2016Review
- RBscore&NBench: a high-level web server for nucleic acid binding residues prediction with a large-scale benchmarking database.Nucleic acids research · 2016Article
- Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.BMC bioinformatics · 2016Article
- SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.PloS one · 2015Article
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing numbers of protein structures are solved each year, but many of these structures belong to proteins whose sequences are homologous to sequences in the Protein Data Bank. Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to 'novel' protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the 'unknown' RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw.
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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.