Simultaneous Identification of Multiple Binding Sites in Proteins: A Statistical Mechanics Approach.

Patrice Koehl,Marc Delarue,Henri Orland

doi:10.1021/acs.jpcb.1c02658

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, cosolvent, 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 binding sites of lipids for membrane proteins.

Highlights

Proteins are unique among biomolecules in that their function is modulated by a wide variery of small molecules that use different types of interactions in their binding sites
We have tested the ability of Hydrophobic Dipolar Poisson Boltzmann Langevin (HDPBL) to detect pockets that bind to hydrophobic ligands as well as to characterize the environment of membrane proteins
HDPBL provides the densities of anions, cations, and water dipole around the solute

Summary

Introduction

Proteins are unique among biomolecules in that their function is modulated by a wide variery of small molecules that use different types of interactions in their binding sites These binding sites can bind natural substrates, as observed for example in enzyme active sites, as well as in allosteric regulatory sites. Many experimental methods have been developed for this purpose, based on NMR,[1] X-ray crystallography,[2] as well as other techniques.[3] Those techniques, are time-consuming and often expensive This has led to parallel developments in computational structural biology with the same goals of identifying druggable binding sites in proteins.[4,5,6] The corresponding methods are usually fast and easy to implement. In this paper we propose a complementary technique based on statistical mechanics for identifying simultaneously one or several binding sites of different kinds in a given protein

Methods

Results

Conclusion