dsRBPBind

dsRBPBind models and quantifies the effect of RNA secondary structure on the binding affinity of double-stranded RNA-binding proteins (dsRBPs).

Key Features:

• Quantitative binding-affinity model: Calculates the effective affinity of dsRBPs for any given RNA sequence by accounting for the principal dissociation constant and the full ensemble of possible RNA secondary structures.
• ViennaRNA integration: Uses the ViennaRNA folding package to obtain secondary-structure ensembles and folding predictions, with the integration operating at O(N^3) time complexity.
• Validation with experimental data: Model predictions have been compared to experimentally determined binding affinities and stoichiometries, specifically for the transactivation response element RNA-binding protein (TRBP).
• Correlation with miRNA processing: Predicted TRBP binding affinities for pre-miRNA-like constructs show significant correlation with experimentally measured processing rates.
• Sensitivity to alternative structures: Demonstrates that alternative RNA secondary structures can alter dsRBP binding affinity by several orders of magnitude.
• Parameterized model: Explicitly represents model parameters including footprint size, concentration, cooperativity, principal dissociation constant, and overlap.

Scientific Applications:

• RNA interference studies: Assess how RNA secondary-structure variation affects dsRBP binding relevant to RNAi mechanisms.
• mRNA elongation and gene regulation: Explore dsRBP interactions with mRNA to inform regulatory pathway analyses.
• A-to-I editing: Investigate roles of dsRBPs in adenosine-to-inosine RNA editing processes.
• Splicing: Evaluate dsRBP contributions to splicing regulation via structure-dependent binding.
• Host defense and viral RNA recognition: Analyze how dsRBPs recognize and bind viral RNAs in host defense contexts.
• miRNA biogenesis: Relate predicted dsRBP affinities, particularly for TRBP, to processing rates of pre-miRNA-like constructs.

Methodology:

Computes effective affinities by aggregating principal binding-affinity contributions across the ensemble of RNA secondary structures predicted by the ViennaRNA package; the integration runs with O(N^3) time complexity.