Simultaneous identification of multiple binding sites in proteins: A statistical mechanics approach

TL;DR

This paper introduces the HDPBL model, an advanced statistical mechanics framework that predicts the organization of water, ions, and hydrophobic molecules around proteins, aiding in identifying multiple binding sites simultaneously.

Contribution

The paper extends the Poisson-Boltzmann model by incorporating self-orienting dipoles, hydrophobic interactions, and steric constraints, enabling simultaneous detection of diverse protein binding sites.

Findings

Successfully detects pockets binding hydrophobic ligands

Characterizes environments of membrane proteins

Validates model against known protein structures

Abstract

We present an extension of the Poisson-Boltzmann model in which the solute of interest is immersed in an assembly of self-orienting Langevin water dipoles, anions, cations, and hydrophobic molecules, all of variable densities. Interactions between charges are controlled by electrostatics, while hydrophobic interactions are modeled with a Yukawa potential. We impose steric constraints by assuming that the system is represented on a cubic lattice. We also assume incompressibility, i.e. all sites of the lattice are occupied. This model, which we refer to as the Hydrophobic Dipolar Poisson Boltzmann Langevin (HDPBL) model, leads to a system of two equations whose solutions give the water dipole, salt, and hydrophobic molecule densities, all of them in the presence of the others in a self-consistent way. We use those to study the organization of the ions, co-solvent and solvent molecules around proteins. In particular, peaks of densities are expected to reveal, simultaneously, the presence of compatible binding sites of different kinds on a protein. We have tested and validated the ability of HDPBL to detect pockets in proteins that bind to hydrophobic ligands, polar ligands and charged small probes as well as to characterize the environment of membrane proteins.