ArticlePloS one2014
Prediction of RNA binding residues: an extensive analysis based on structure and function to select the best predictor.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 22 citations in OpenAlex.
- RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes.Computational and structural biotechnology journal · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms.Molecular cell · 2020Review
- Deciphering RNA-Recognition Patterns of Intrinsically Disordered Proteins.International journal of molecular sciences · 2018Article
- Sequence-Based Prediction of RNA-Binding Residues in Proteins.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Advances in the characterization of RNA-binding proteins.Wiley interdisciplinary reviews. RNA · 2016Review
- Article
- PDBparam: Online Resource for Computing Structural Parameters of Proteins.Bioinformatics and biology insights · 2016Review
- Computational Prediction of RNA-Binding Proteins and Binding Sites.International journal of molecular sciences · 2015Review
- Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score.Nucleic acids research · 2015Article
- Structure based approach for understanding organism specific recognition of protein-RNA complexes.Biology direct · 2015Article
- Article
- SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.PloS one · 2015Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-RNA complexes play key roles in several cellular processes by the interactions of amino acids with RNA. To understand the recognition mechanism, it is important to identify the specific amino acids involved in RNA binding. Various computational methods have been developed for predicting RNA binding residues from protein sequence. However, their performances mainly depend on the training dataset, feature selection for developing a model and learning capacity of the model. Hence, it is important to reveal the correspondence between the performance of methods and properties of RNA-binding proteins (RBPs). In this work, we have collected all available RNA binding residues prediction methods and revealed their performances on unbiased, stringent and diverse datasets for RBPs with less than 25% sequence identity based on structural class, fold, superfamily, family, protein function, RNA type, RNA strand and RNA conformation. The best methods for each type of RBPs and the type of RBPs, which require further refinement in prediction, have been brought out. We also analyzed the performance of these methods for the disordered regions, structures which are not included in the training dataset and recently solved structures. The reliability of prediction is better than randomly choosing any method or combination of methods. This approach would be a valuable resource for biologists to choose the best method based on the type of RBPs for designing their experiments and the tool is freely accessible online at www.iitm.ac.in/bioinfo/RNA-protein/.
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