The role of water in host-guest interaction

TL;DR

This paper develops a machine learning-based collective variable to accurately model water's role in host-guest binding, improving binding energy calculations and aligning well with experimental data.

Contribution

The study introduces a novel water-describing collective variable combining machine learning and physical intuition for enhanced sampling in binding simulations.

Findings

Achieved highly accurate binding energies matching experiments.

Provided detailed analysis of water's role in binding processes.

Demonstrated the effectiveness of the new collective variable in SAMPL5 systems.

Abstract

One of the main applications of atomistic computer simulations is the calculation of ligand binding energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in modern simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.