ECONTACT

ECONTACT identifies nonadditive energetic cross-talks between amino acid residues within protein-ligand binding pockets by analysing per-residue energy contributions across multiple complexes to inform determinants of ligand binding affinity.

Key Features:

• Per-residue energy decomposition: Decomposes protein–ligand interaction energies into individual amino acid residue contributions across multiple structural datasets.
• Principal component analysis (PCA): Uses decomposed per-residue energy terms as variables for PCA to detect correlated energetic patterns among residues.
• Molecular dynamics integration: Integrates molecular dynamics simulations to reveal correlative motions indicative of nonadditive effects on binding.
• Rescoring method: Applies a rescoring method alongside dynamics to enhance detection of residue cross-talks.
• Joint probability density function (jPDF): Models key determinants of ligand binding using a joint probability density function to pinpoint significant cross-talk interactions.
• Multi-complex analysis: Operates on multiple protein–ligand complexes, demonstrated across eight datasets.
• Cross-talk identification: Identified 16 residue cross-talks across datasets.
• Empirical association: Associated 10 of the 16 identified cross-talks with empirical data from site-directed mutagenesis, free energy calculations, and dynamics simulations.
• Virtual screening performance: Generated models that improved discrimination between known inhibitors and decoy molecules in virtual screening tests.
• Experimental corroboration: Reported the top two identified cross-talks as corroborated by experimental findings.
• No reliance on experimental affinities: Operates without using experimental binding affinity measurements.

Scientific Applications:

• Mapping nonadditive interactions: Identifies nonadditive energetic contributions among residues that influence ligand binding affinity.
• Mutagenesis target prioritization: Guides interpretation and prioritization of site-directed mutagenesis experiments through association with predicted cross-talks.
• Interpretation of free energy and dynamics: Aids interpretation of free energy calculations and dynamics simulation outcomes by linking them to residue cross-talks.
• Virtual screening enhancement: Improves virtual screening models for discriminating inhibitors from decoys by incorporating identified energetic cross-talks.
• Structural determinant modeling: Uses jPDF-based models to represent key energetic determinants of ligand binding within binding pockets.

Methodology:

Per-residue energy decomposition across multiple protein–ligand complexes; principal component analysis using decomposed residue energy terms as variables; integration of molecular dynamics simulations and a rescoring method to detect correlative motions; modeling of key determinants with a joint probability density function (jPDF).