Abstract
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.
Highlights
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies
We will frame our discussion in the context of Metadynamics (MetaD)[10,11,12] or, more precisely, of its most recent evolution, the on-the-fly probability-enhanced sampling method (OPES)[13]
In order to succeed in our endeavour, we rely on a combination of physical considerations and modern machine learning (ML)
Summary
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. 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. Atomistic simulations have been widely used[3,4,5,6,7] to calculate key parameters like ligand affinity and residence time, and to gain a microscopic understanding of how protein–ligand binding works The accuracy of these simulations depends on two key aspects: the quality of the model used to describe the interatomic interactions and the thoroughness of the statistical sampling[8,9]. In order to succeed in our endeavour, we rely on a combination of physical considerations and modern machine learning (ML)
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