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RBPBind: Quantitative Prediction of Protein-RNA Interactions
Jeff Gaither1, Yi-Hsuan Lin2, Ralf Bundschuh3
1Institute for Genomic Medicine, Nationwide Children's Hospital, 700 Children's Dr, Columbus, OH 43205, USA. Electronic address: https://twitter.com/JeffBGaither.
Journal of Molecular Biology
|June 6, 2022
Summary
RBPBind predicts RNA-protein interactions by integrating RNA secondary structure with sequence specificity. This tool quantifies binding affinity and location for single-stranded RNA binding proteins, improving prediction accuracy.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Hundreds of RNA binding proteins (RBPs) regulate RNA lifecycle through interactions.
- Understanding RBP-RNA binding strength and location is crucial for cellular processes.
Purpose of the Study:
- To introduce RBPBind, a web tool for quantitative prediction of RBP-RNA interactions.
- To incorporate RNA secondary structure effects into binding affinity predictions.
Main Methods:
- RBPBind uses sequence specificity from RNAcompete data and Vienna RNA package for calculations.
- It computes binding curves, effective binding constants, and nucleotide-level binding probabilities.
- The tool integrates protein binding and RNA secondary structure predictions.
Main Results:
- RBPBind accurately predicts binding affinities by considering RNA secondary structure.
- Validation with HuR protein and literature data shows improved agreement between predicted and experimental affinities.
- The integration of sequence affinities into secondary structure prediction enhances accuracy.
Conclusions:
- RBPBind offers a user-friendly platform for predicting RBP-RNA binding affinities and locations.
- The tool relies solely on RNA sequence for reliable predictions.
- This resource aids in understanding RBP-RNA interactions in various biological contexts.
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