RBPBind

RBPBind predicts quantitative interactions between single-stranded RNA-binding proteins and target RNAs by integrating sequence-specific binding data with RNA secondary structure to compute binding curves, effective binding constants, and positional binding probabilities.


Key Features:

  • Quantitative Prediction: Calculates binding curves and effective binding constants for specified RNA sequences and selected RBPs from a predefined set.
  • RNA Secondary Structure Consideration: Fully accounts for the influence of RNA secondary structure on binding affinity in all predictions.
  • Probability Computation: Computes the probability that a protein molecule will bind each nucleotide along an RNA sequence at specified protein concentrations.
  • Sequence Specificity Parameterization: Integrates sequence specificity parameters from RNAcompete experiments into the Vienna RNA package recursions to jointly model sequence affinity and RNA folding.
  • Validation and Reliability: Methodology validated against experimentally determined binding affinities for the HuR protein across diverse RNAs, showing improved correlation when incorporating sequence affinities into structure-aware predictions.

Scientific Applications:

  • Binding site and affinity prediction: Provides quantitative binding affinities and positional binding probabilities from sequence data to identify likely RBP binding sites.
  • Gene regulation and post-transcriptional studies: Supports analyses of RBP-mediated gene regulation and post-transcriptional control by linking binding predictions to sequence context and secondary structure.
  • Comparison with experimental data: Facilitates comparison between computational predictions and experimentally measured binding affinities for RBPs such as HuR.

Methodology:

Integrates RNAcompete-derived sequence specificity parameters into the Vienna RNA package recursions to account for RNA secondary structure while computing binding curves, effective binding constants, and positional binding probabilities at specified protein concentrations.

Topics

Details

Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Added:
9/3/2022
Last Updated:
11/24/2024

Operations

Publications

Gaither J, Lin Y, Bundschuh R. RBPBind: Quantitative Prediction of Protein-RNA Interactions. Journal of Molecular Biology. 2022;434(11):167515. doi:10.1016/j.jmb.2022.167515. PMID:35662470.

PMID: 35662470
Funding: - National Science Foundation: DMR-1410172, DMR-1719316, DMS-0931642