Lewis pair polymerization employing N-Heterocyclic olefins (NHOs) and simple metal halides as co-catalysts has emerged as a useful tool to polymerize diverse lactones. To elucidate some of the mechanistic aspects that remain unclear to date and to better understand the impact of the metal species, computational methods have been applied. Several key aspects have been considered: (1) the formation of NHO-metal halide adducts has been evaluated for eight different NHOs and three different Lewis acids, (2) the coordination of four lactones to MgCl2 was studied and (3) the deprotonation of an initiator (butanol) was investigated in the presence and absence of metal halide for one specific Lewis pair. It was found that the propensity for adduct formation can be influenced, perhaps even designed, by varying both organic and metallic components. Apart from the NHO backbone, the substituents on the exocyclic, olefinic carbon have emerged as interesting tuning site. The tendency to form adducts is ZnCl2 > MgCl2 > LiCl. If lactones coordinate to MgCl2, the most likely binding mode is via the carbonyl oxygen. A chelating coordination cannot be ruled out and seems to gain importance upon increasing ring-size of the lactone. For a representative NHO, it is demonstrated that in a metal-free setting an initiating alcohol cannot be deprotonated, while in the presence of MgCl2 the same process is exothermic with a low barrier.
We present the software package transformato for the setup of large-scale relative binding free energy calculations. Transformato is written in Python as an open source project (https://github.com/wiederm/transformato); in contrast to comparable tools, it is not closely tied to a particular molecular dynamics engine to carry out the underlying simulations. Instead of alchemically transforming a ligand L1 directly into another L2, the two ligands are mutated to a common core. Thus, while dummy atoms are required at intermediate states, in particular at the common core state, none are present at the physical endstates. To validate the method, we calculated 76 relative binding free energy differences ΔΔGL1→L2bind for five protein–ligand systems. The overall root mean squared error to experimental binding free energies is 1.17 kcal/mol with a Pearson correlation coefficient of 0.73. For selected cases, we checked that the relative binding free energy differences between pairs of ligands do not depend on the choice of the intermediate common core structure. Additionally, we report results with and without hydrogen mass reweighting. The code currently supports OpenMM, CHARMM, and CHARMM/OpenMM directly. Since the program logic to choose and construct alchemical transformation paths is separated from the generation of input and topology/parameter files, extending transformato to support additional molecular dynamics engines is straightforward.
To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have limited range of applicability and are significantly slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. In this study, we systematically investigate the accuracy, robustness, and efficiency of free energy calculations between a MM reference and a neural network target potential (ANI-2x) using free energy perturbation (FEP) and non-equilibrium (NEQ) switching simulation in vacuum. We investigate the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution. We compare the results to multistage equilibrium free energy calculations (MFES) for an established dataset. Our results demonstrate that free energy calculations between NNPs and MM potentials should not be performed using FEP but require NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between MM and NNPs are highly efficient, robust, and trivial to implement.
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