HotSPRing

HotSPRing predicts binding hot spots at protein–RNA recognition sites in RNA-binding proteins by integrating evolutionary conservation from structural alignments with structural and physicochemical attributes of protein–RNA interfaces.

Key Features:

• Evolutionary Conservation Analysis: Derives evolutionary conservation from structural alignment of polypeptide sequences and reports that residues at RNA binding sites are more conserved than solvent-exposed residues across structural classes.
• Residue Interaction Specificity: Distinguishes interactions with duplex RNA grooves and finds residues interacting with the major groove are more conserved than those engaging the minor groove.
• Multi-Interface Residue Identification: Identifies residues that participate in both protein–protein and protein–RNA interfaces within multi-polypeptide complexes and reports higher conservation for these residues.
• Water Preservation Site Analysis: Evaluates conservation at water preservation sites and reports these residues are more conserved than residues at hydrated or dehydrated sites.
• Predictive Modeling with Random Forests: Uses a Random Forests model built on structural and physicochemical attributes to predict binding hot spots and successfully predicts 80% of instances of experimental ΔΔG values within specific classes.

Scientific Applications:

• Molecular mechanism elucidation: Facilitates analysis of molecular mechanisms underlying protein–RNA interactions by mapping conserved and functionally important residues.
• RBP design and optimization: Guides the design and optimization of RNA-binding proteins (RBPs), including engineering recognition sites with tailored affinities.
• Drug discovery: Informs identification of hot spots relevant to therapeutic targeting of protein–RNA interfaces.
• Synthetic biology and genetic engineering: Supports design of RBPs for synthetic biology and genetic engineering applications where altered affinity or specificity is required.
• Understanding complex formation: Provides insights into evolutionary conservation patterns and interaction specificities that inform RNA–protein complex formation and stability.

Methodology:

Computes evolutionary conservation from structural alignment of polypeptide sequences, analyzes structural and physicochemical attributes at protein–RNA interfaces, integrates these datasets, and applies a Random Forests model to predict binding hot spots and compare predictions to experimental ΔΔG values.