Grid-based state space exploration for molecular binding

TL;DR

This paper introduces a low-dimensional grid-based model for molecular binding that discretizes relative positions and orientations, enabling efficient energy evaluation and analysis, and demonstrating promising results on molecular systems.

Contribution

First implementation of a low-dimensional grid-based model for molecular binding, including algorithms for discretizing orientations and a Python package for energy calculations.

Findings

Successfully modeled water dimer and protein-chloride interactions

Electrostatic energy contributions are well-resolved by grid point calculations

The approach complements molecular dynamics by providing efficient energy analysis

Abstract

Binding processes are difficult to sample with molecular-dynamics (MD) simulations. In particular, the state space exploration is often incomplete. Evaluating the molecular interaction energy on a grid circumvents this problem but is heavily limited by state space dimensionality. Here, we make the first steps towards a low-dimensional grid-based model of molecular binding. We discretise the state space of relative positions and orientations of the two molecules under the rigid body assumption.The corresponding program is published as the Python package molgri. For the rotational component of the grids, we test algorithms based on Euler angles, polyhedra and quaternions, of which the polyhedra-based are the most uniform. The program outputs a sequence of molecular structures that can be easily processed by standard MD programs to calculate grid point energies. We demonstrate the grid-based approach on two molecular systems: a water dimer and a coiled-coil protein interacting with a chloride anion. For the second system we relax the rigid-body assumption and improve the accuracy of the grid point energies by an energy minimisation. In both cases, oriented bonding patterns and energies confirm expectations from chemical intuition and MD simulations. We also demonstrate how analysis of energy contributions on a grid can be performed and demonstrate that electrostatically-driven association is sufficiently resolved by point-energy calculations. Overall, grid-based models of molecular binding are potentially a powerful complement to molecular sampling approaches, and we see the potential to expand the method to quantum chemistry and flexible docking applications.