Layered nested Markov chain Monte Carlo

A General Picture of Cucurbit[8]uril Host–Guest Binding: Recalibrating Bonded Interactions

Atomic-level understanding of the dynamic feature of host–guest interactions remains a central challenge in supramolecular chemistry. The remarkable guest binding behavior of the Cucurbiturils family of supramolecular containers makes them promising drug carriers. Among Cucurbit[n]urils, Cucurbit[8]uril (CB8) has an intermediate portal size and cavity volume. It can exploit almost all host–guest recognition motifs formed by this host family. In our previous work, an extensive computational investigation of the binding of seven commonly abused and structurally diverse drugs to the CB8 host was performed, and a general dynamic binding picture of CB8-guest interactions was obtained. Further, two widely used fixed-charge models for drug-like molecules were investigated and compared in great detail, aiming at providing guidelines in choosing an appropriate charge scheme in host-guest modelling. Iterative refitting of atomic charges leads to improved binding thermodynamics and the best root-mean-squared deviation from the experimental reference is 2.6 kcal/mol. In this work, we focus on a thorough evaluation of the remaining parts of classical force fields, i.e., the bonded interactions. The widely used general Amber force fields are assessed and refitted with generalized force-matching to improve the intra-molecular conformational preference, and thus the description of inter-molecular host–guest interactions. The interaction pattern and binding thermodynamics show a significant dependence on the modelling parameters. The refitted system-specific parameter set improves the consistency of the modelling results and the experimental reference significantly. Finally, combining the previous charge-scheme comparison and the current force-field refitting, we provide general guidelines for the theoretical modelling of host–guest binding.

A General Picture of Cucurbit[8]uril Host-Guest Binding

Describing, understanding, and designing complex interaction networks within macromolecular systems remain challenging in modern chemical research. Host-guest systems, despite their relative simplicity in both the structural feature and interaction patterns, still pose problems in theoretical modeling. The barrel-shaped supramolecular container cucurbit[8]uril (CB8) shows promising functionalities in various areas, e.g., catalysis and molecular recognition. It can stably coordinate a series of structurally diverse guests with high affinities. In this work, we examine the binding of seven commonly abused drugs to the CB8 host, aiming at providing a general picture of CB8-guest binding. Extensive sampling of the configurational space of these host-guest systems is performed, and the binding pathway and interaction patterns of CB8-guest complexes are investigated. A thorough comparison of widely used fixed-charge models for drug-like molecules is presented. Iterative refitting of the atomic charges suggests significant conformation dependence of charge generation. The initial model generated at the original conformation could be inaccurate for new conformations explored during conformational search, and the newly fitted charge set improves the prediction-experiment correlation significantly. Our investigations of the configurational space of CB8-drug complexes suggest that the host-guest interactions are more complex than expected. Despite the structural simplicities of these molecules, the conformational fluctuations of the host and the guest molecules and orientations of functional groups lead to the existence of an ensemble of binding modes. The insights of the binding thermodynamics, performance of fixed-charge models, and binding patterns of the CB8-guest systems are useful for studying and elucidating the binding mechanism of other host-guest complexes.

Generation of Quantum Configurational Ensembles Using Approximate Potentials

Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance. High-level methods, such as ab initio MD, provide reliable structural information but are still too computationally expensive to allow for extensive sampling. Therefore, to overcome these limitations, we present a multilevel MC method that is capable of generating quantum configurational ensembles while keeping the computational cost at a minimum. We show that FF reparametrization is an efficient route to generate FFs that reproduce QM results more closely, which, in turn, can be used as low-cost models to achieve the gold standard QM accuracy. We demonstrate that the MC acceptance rate is strongly correlated with various phase space overlap measurements and that it constitutes a robust metric to evaluate the similarity between the MM and QM levels of theory. As a more advanced application, we present a self-parametrizing version of the algorithm, which combines sampling and FF parametrization in one scheme, and apply the methodology to generate the QM/MM distribution of a ligand in aqueous solution.

First principles reactive simulation for equation of state prediction

The high cost of density functional theory (DFT) has hitherto limited the ab initio prediction of the equation of state (EOS). In this article, we employ a combination of large scale computing, advanced simulation techniques, and smart data science strategies to provide an unprecedented ab initio performance analysis of the high explosive pentaerythritol tetranitrate (PETN). Comparison to both experiment and thermochemical predictions reveals important quantitative limitations of DFT for EOS prediction and thus the assessment of high explosives. In particular, we find that DFT predicts the energy of PETN detonation products to be systematically too high relative to the unreacted neat crystalline material, resulting in an underprediction of the detonation velocity, pressure, and temperature at the Chapman-Jouguet state. The energetic bias can be partially accounted for by high-level electronic structure calculations of the product molecules. We also demonstrate a modeling strategy for mapping chemical composition across a wide parameter space with limited numerical data, the results of which suggest additional molecular species to consider in thermochemical modeling.