The QM/MM method has become a useful tool to investigate various properties of complex systems. We previously introduced the Layered Interacting Chemical Models (LICHEM) package to enable QM/MM simulations with advanced potentials by combining various (unmodified) QM and MM codes (JCC, 27, 1019). LICHEM provides several capabilities such as the ability to use polarizable force fields, such as AMOEBA, for the MM environment. Here, we describe an updated version of LICHEM (v1.1), which includes several new functionalities including a new method to account for long-range electrostatic effects in QM/mm (QM/MM-LREC), a new implementation for QM/MM with the Gaussian Electrostatic Model (GEM), and new capabilities for path optimizations using the quadratic string model (QSM) coupled with restrained MM environment optimization.

The use of advanced polarizable potentials in quantum mechanical/molecular mechanical (QM/MM) simulations has been shown to improve the overall accuracy of the calculation. We have developed a density-based potential called the Gaussian electrostatic model (GEM), which has been shown to provide very accurate environments for QM wave functions in QM/MM. In this contribution we present a new implementation of QM/GEM that extends our implementation to include all components (Coulomb, exchange-repulsion, polarization, and dispersion) for the total intermolecular interaction energy in QM/MM calculations, except for the charge-transfer term. The accuracy of the method is tested using a subset of water dimers from the water dimer potential energy surface reported by Babin et al. ( J. Chem. Theory Comput. 2013 9, 5395-5403). Additionally, results of the new implementation are contrasted with results obtained with the classical AMOEBA potential. Our results indicate that GEM provides an accurate MM environment with average root-mean-square error <0.15 kcal/mol for every intermolecular interaction energy component compared with SAPT2+3/aug-cc-pVTZ reference calculations.

The potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry. However, the proper choice of the computational approach based on computational cost and reliability of calculated energies is a dilemma, especially for large molecules. This dilemma is proved to be even more problematic for studies that require hundreds and thousands of calculations, such as drug discovery. On the other hand, driven by their pattern recognition capabilities, neural networks started to gain popularity in the computational chemistry community. During the last decade, many neural network potentials have been developed to predict a variety of chemical information of different systems. Neural network potentials are proved to predict chemical properties with accuracy comparable to quantum mechanical approaches but with the cost approaching molecular mechanics calculations. As a result, the development of more reliable, transferable, and extensible neural network potentials became an attractive field of study for researchers. In this review, we outlined an overview of the status of current neural network potentials and strategies to improve their accuracy. We provide recent examples of studies that prove the applicability of these potentials. We also discuss the capabilities and shortcomings of the current models and the challenges and future aspects of their development and applications. It is expected that this review would provide guidance for the development of neural network potentials and the exploitation of their applicability. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Interactions Software > Molecular Modeling

The behavior of proteins is closely related to the protonation states of the residues. Therefore, prediction and measurement of pKa are essential to understand the basic functions of proteins. In...

The inactivation mechanism of γ-aminobutyric acid aminotransferase (GABA-AT) in the presence of γ-vinyl-aminobutyric acid, an anti-epilepsy drug, has been studied by means of theoretical calculations. Density functional theory methods have been applied to compare the three experimentally proposed inactivation mechanisms (Silverman et al., J. Biol. Chem., 2004, 279, 363). All the calculations were performed at the B3LYP/6-31+G(d,p) level of theory. Single point solvent calculations were carried out in water, by means of an integral equation formalism-polarizable continuum model (IEFPCM) at the B3LYP/6-31+G(d,p) level of theory. The present calculations provide an insight into the mechanistic preferences of the inactivation reaction of GABA-AT. The results also allow us to elucidate the key factors behind the mechanistic preferences. The computations also confirm the importance of explicit water molecules around the reacting center in the proton transfer steps.