The role of water in host-guest interaction

One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in 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 host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail. Computational approaches to predict water's role in host-ligand binding attract a great deal of attention. Here the authors use a metadynamics enhanced sampling method and machine learning to compute binding energies for host-guest systems from the SAMPL5 challenge and provide details of water structural changes.

Automated summary

Atomistic computer simulations are commonly used for calculating ligand binding free energies, and the accuracy depends on the quality of the force field and sampling thoroughness. In this study, machine learning was combined with physical intuition to develop a non-local and efficient water-describing collective variable, resulting in highly accurate binding free energies for host-guest systems. The role of water during the binding process was analyzed in detail, highlighting the importance of computational approaches in predicting water's role in host-ligand binding.