DR.Bind

DR.Bind predicts DNA-binding residues in proteins using structural information by integrating electrostatics, evolutionary conservation, and geometric considerations.

Key Features:

• Input Requirements: Accepts protein structures in PDB format as input.
• Prediction Methodology: Identifies clusters of conserved, solvent-accessible residues that exhibit electrostatic stabilization upon mutation to negatively charged amino acids (Asp or Glu) without using machine learning or extensive parameterization.
• Underlying Principles: Combines electrostatic analysis, evolutionary conservation, and geometric plausibility to locate potential DNA-binding sites.
• Outputs: Produces a downloadable text file of predicted residues, a 3D visualization highlighting predicted sites, and a PyMol script for further analysis.
• Performance: Calibration on 83 non-redundant DNA-bound and 55 DNA-free protein structures reported 90% accuracy and 47% precision for DNA-bound proteins, and 88% accuracy and 42% precision for DNA-free proteins.

Scientific Applications:

• Structural genomics: Predicts DNA-binding residues in novel or uncharacterized protein structures lacking prior complex data.
• Protein annotation: Assists annotation of potential DNA-binding sites in solved protein structures.
• Comparative analysis: Supports distinction between DNA-bound and DNA-free proteins through predicted binding-site patterns and reported calibration metrics.

Methodology:

Computationally identifies clusters of conserved, solvent-accessible residues, assesses electrostatic stabilization upon mutation to Asp or Glu, and evaluates geometric plausibility; approach explicitly excludes machine learning and extensive parameterization.