EnCPdock

EnCPdock computes equilibrium thermodynamic parameters (∆Gbinding, Kd) and analyzes shape and electrostatic complementarity to quantify binding energetics in protein–protein interactions.

Key Features:

• AI-Predicted Binding Free Energy: Employs artificial intelligence to predict ∆Gbinding by combining complementarity (shape and electrostatic) with other high-level structural descriptors.
• Complementarity Plotting: Visualizes protein–protein complexes on a two-dimensional complementarity plot based on shape (Sc) and electrostatic (EC) complementarities.
• Molecular Graphics of Interfacial Atomic Contact Network: Generates molecular graphics of the interfacial atomic contact network to represent atomic contacts at protein interfaces.
• Feature Trend Analysis and Probability Estimates: Computes individual feature trends and relative probability estimates (Prfmax) from highest observed frequencies of feature-scores to inform interface modification.

Scientific Applications:

• Structural Biology: Characterizes interface complementarity and binding energetics in PPIs using ∆Gbinding, Kd, Sc, and EC metrics.
• Protein Engineering: Informs mutational design and interface optimization by evaluating feature trends and Prfmax for targeted structural changes.
• Drug Design: Supports design of PPI modulators by estimating binding energetics and identifying interfacial atomic contact networks relevant to ligand or inhibitor binding.

Methodology:

Integrates physics-based and knowledge-based methodologies with artificial intelligence to directly compute equilibrium parameters without relying on intermediate structural descriptors, uses Sc and EC complementarity metrics and two-dimensional complementarity plotting, computes Prfmax from feature-score frequency distributions, and generates interfacial atomic contact network graphics.