Accurate Determination of Cavitand Binding Free Energies via Unrestrained Advanced Sampling

Calculating Binding Free Energies in Model Host-Guest Systems with Unrestrained Advanced Sampling

Host-guest interactions are important to the design of pharmaceuticals and, more broadly, to soft materials as they can enable targeted, strong, and specific interactions between molecules. The binding process between the host and guest may be classified as a "rare event" when viewing the system at atomic scales, such as those explored in molecular dynamics simulations. To obtain equilibrium binding conformations and dissociation constants from these simulations, it is essential to resolve these rare events. Advanced sampling methods such as the adaptive biasing force (ABF) promote the occurrence of less probable configurations in a system, therefore facilitating the sampling of essential collective variables that characterize the host-guest interactions. Here, we present the application of ABF to a rod-cavitand coarse-grained model of host-guest systems to acquire the potential of mean force. We show that the employment of ABF enables the computation of the configurational and thermodynamic properties of bound and unbound states, including the free energy landscape. Moreover, we identify important dynamic bottlenecks that limit sampling and discuss how these may be addressed in more general systems.

Transfer Learning Facilitates the Prediction of Polymer-Surface Adhesion Strength

Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [Shi et al. ACS Applied Materials & Interfaces 2022, 14, 37161-37169.], ML models were applied to predict the adhesive free energy of polymer-surface interactions with high accuracy from the knowledge of the sequence data, demonstrating successes in inverse-design of polymer sequence for known surface compositions. While the method was shown to be successful in designing polymers for a known surface, extensive data sets were needed for each specific surface in order to train the surrogate models. Ideally, one should be able to infer information about similar surfaces without having to regenerate a full complement of adhesion data for each new case. In the current work, we demonstrate a transfer learning (TL) technique using a deep neural network to improve the accuracy of ML models trained on small data sets by pretraining on a larger database from a related system and fine-tuning the weights of all layers with a small amount of additional data. The shared knowledge from the pretrained model facilitates the prediction accuracy significantly on small data sets. We also explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks. While applied to a relatively simple coarse-grained (CG) polymer model, the general lessons of this study apply to detailed modeling studies and the broader problems of inverse materials design.

Thermodynamics of Li+-Crown Ether Interactions in Aqueous Solvent

Lithium ion-based batteries are ubiquitous in modern technology due to applications in personal electronics and high-capacity storage for electric vehicles. Concerns about lithium supply and battery waste have prompted interest in lithium recycling methods. The crown ether 12-crown-4 has been studied for its abilities to form stable complexes with lithium ions (Li⁺). In this paper, molecular dynamics simulations are applied to examine the binding properties of a 12-crown-4-Li⁺ system in aqueous solution. It was found that 12-crown-4 did not form stable complexes with Li⁺ in aqueous solution due to the binding geometry which was prone to interference by surrounding water molecules. In addition, the binding properties of sodium ions (Na⁺) to 12-crown-4 are examined for comparison. Subsequently, calculations were performed with the crown ethers 15-crown-5 and 18-crown-6 to study their complexation with Li⁺ as well as Na⁺. It was determined that binding was unfavorable for both types of ions for all three crown ethers tested, though 15-crown-5 and 18-crown-6 showed a marginally greater affinity for Li⁺ than 12-crown-4. Metastable minima present in the potential of mean force for Na⁺ render binding marginally more likely there. We discuss these results in the context of membrane-based applications of crown ethers for Li⁺ separations.

Advances in the calculation of binding free energies

In recent years, calculations of binding affinities from molecular simulations seem to have matured significantly. While the number of applications of such methods in drug design and biotechnology increases, the number of truly new methodological developments decreases. This review provides an overview of the current status of the field as reflected in recent publications. The focus is on the challenges that remain when using endstate, alchemical and pathway methods. For endstate methods this is the calculation of entropic contributions. For alchemical methods there are unsolved problems associated with the solvation of the active site, sampling slow degrees of freedom and when modifying the net charge. For pathway methods achieving sufficient sampling remains challenging. New trends are also highlighted, including the use of pathway methods for the quantification of protein-protein interactions.