Blinded predictions of standard binding free energies: lessons learned from the SAMPL6 challenge

<p>In the context of the SAMPL6 challenges, series of blinded predictions of standard binding free energies were made with the SOMD software for a dataset of 27 host-guest systems featuring two octa-acids hosts (<i>OA </i>and <i>TEMOA) </i>and a cucuribituril ring (<i>CB</i>8)<i> </i>host. Three different models were used, <i>ModelA </i>computes the free energy of binding based on a double annihilation technique; <i>ModelB</i> additionally takes into account long-range dispersion and standard state corrections; <i>ModelC</i> additionally introduces an empirical correction term derived from a regression analysis of SAMPL5 predictions previously made with SOMD. The performance of each model was evaluated with two different setups; <i>buffer </i>explicitly matches the ionic strength from the binding assays, whereas <i>no-buffer</i> merely neutralizes the host-guest net charge with counter-ions. <i>ModelC/no-buffer</i> shows the lowest mean-unsigned error for the overall dataset (MUE 1.29 < 1.39 < 1.50 kcal mol^-1, 95% CI), while explicit modelling of the buffer improves significantly results for the CB8 host only. Correlation with experimental data ranges from excellent for the host TEMOA (R² 0.91 < 0.94 < 0.96), to poor for <i>CB8 </i>(R² 0.04 < 0.12 < 0.23). Further investigations indicate a pronounced dependence of the binding free energies on the modelled ionic strength, and variable reproducibility of the binding free energies between different simulation packages. </p

The role of water in host-guest interaction

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