Journal of Chemical Theory and Computation最新文献

A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and ab initio computation methods are widely used to evaluate PA; however, both methods are resource-intensive and time-consuming. Therefore, there is a critical need for efficient methods to estimate PA, enabling the rapid identification of the most favorable protonation site in complex organic molecules with multiple proton binding sites. In this work, we developed a fast and accurate method for PA prediction by using multiple descriptors in combination with machine learning (ML) models. Using a comprehensive set of 186 descriptors, our model demonstrated strong predictive performance, with an R² of 0.96 and a MAE of 2.47 kcal/mol, comparable to experimental uncertainty. Furthermore, we designed quantum circuits as feature encoders for a classical neural network. To evaluate the effectiveness of this hybrid quantum-classical model, we compared its performance with traditional ML models using a reduced feature set derived from the full set. A correlation analysis showed that the quantum-encoded representations have a stronger positive correlation with the target values than the original features do. As a result, the hybrid model outperformed its classical counterpart and achieved consistent performance comparable to traditional ML models with the same reduced feature set on both a noiseless simulator and real quantum hardware, highlighting the potential of quantum machine learning for accurate and efficient PA predictions.

The unitary coupled cluster (UCC) approach has been one of the most popular wavefunction parametrizations for the variational quantum eigensolver due to the relative ease of optimization compared to hardware-efficient ansätze. In this contribution, we explore the energy landscape of the unitary coupled cluster singles and doubles (UCCSD) wavefunction for two commonly employed benchmark systems, lithium hydride and the nitrogen dimer. We investigate the organization of the solution space in terms of local minima and show how it changes as the number and order of operators of the UCC ansatz are varied. Surprisingly, we find that in all cases, the UCCSD energy has numerous low-lying minima connected by high energy transition states. Additionally, the energy spread of the minima that lie in the same band as the global minimum may exceed chemical accuracy, making the location of the true global minimum especially challenging.

Enzymes play crucial roles in all biological systems by catalyzing a myriad of chemical reactions. These reactions range from simple one-step processes to intricate multistep cascades. Predicting mechanistically appropriate binding modes along a reaction pathway for substrate, product, and all reaction intermediates and transition states is a daunting task. To address this challenge, special docking programs like EnzyDock have been developed. Yet, running such docking simulations is complicated due to the nature of multistep enzyme processes. This work presents CHARMM-GUI EnzyDocker, a web-based cyberinfrastructure designed to streamline the preparation and running of EnzyDock docking simulations. The development of EnzyDocker has been achieved through integration of existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Ligand Designer, and QM/MM Interfacer. In addition, new functionalities have been developed to facilitate a one-stop preparation of multistate and multiscale docking systems and enable interactive and intuitive ligand modifications and flexible protein residues selections. A simple setup related to multiligand docking is automatized through intuitive user interfaces. EnzyDocker offers support for standard classical docking and QM/MM docking with CHARMM built-in semiempirical engines. Automated consensus restraints for incorporating experimental knowledge into the docking are facilitated via a maximum common substructure algorithm. To illustrate the robustness of EnzyDocker, we conducted docking simulations of three enzyme systems: dihydrofolate reductase, SARS-CoV-2 M^pro, and the diterpene synthase CotB2. In addition, we have created four tutorial videos about these systems, which can be found at https://www.charmm-gui.org/demo/enzydock. EnzyDocker is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for multistate docking for enzymes.

Quantum computing offers promising new avenues for tackling the long-standing challenge of simulating the quantum dynamics of complex chemical systems, particularly open quantum systems coupled to external baths. However, simulating such nonunitary dynamics on quantum computers is challenging since quantum circuits are specifically designed to carry out unitary transformations. Furthermore, chemical systems are often strongly coupled to the surrounding environment, rendering the dynamics non-Markovian and beyond the scope of Markovian quantum master equations like Lindblad or Redfield. In this work, we introduce a quantum algorithm designed to simulate non-Markovian dynamics of open quantum systems. Our approach enables the implementation of arbitrary quantum master equations on noisy intermediate-scale quantum (NISQ) computers. We illustrate the method as applied in conjunction with the numerically exact hierarchical equations of motion (HEOM) method. The effectiveness of the resulting quantum HEOM algorithm is demonstrated as applied to simulations of the non-Lindbladian electronic energy and charge transfer dynamics in models of the carotenoid-porphyrin-C₆₀ molecular triad dissolved in tetrahydrofuran and the Fenna-Matthews-Olson complex.

The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these potential energy functions integrate physics-based and machine-learned many-body terms trained on electronic structure data calculated at the "gold standard" coupled-cluster level of theory. Recent advancements in MBX have focused on optimizing its performance, resulting in the release of MBX v1.2. While the inherently many-body nature of MB-pol and MB-nrg ensures high accuracy, it poses computational challenges. MBX v1.2 addresses these challenges with significant performance improvements, including enhanced parallelism that fully harnesses the power of modern multicore CPUs. These advancements enable simulations on nanosecond time scales for condensed-phase systems, significantly expanding the scope of high-accuracy, predictive simulations of complex molecular systems powered by data-driven many-body potential energy functions.

The magnetite/water interface is commonly found in nature and plays a crucial role in various technological applications. However, our understanding of its structural and dynamical properties at the molecular scale remains still limited. In this study, we developed an efficient Behler-Parrinello neural network potential (NNP) for the magnetite/water system, paying particular attention to the accurate generation of reference data with density functional theory. Using this NNP, we performed extensive molecular dynamics simulations of the magnetite (001) surface across a wide range of water coverages, from single molecules to bulk water. Our simulations revealed several new ground states of low coverage water on the Subsurface Cation Vacancy (SCV) model and yielded a density profile of water at the surface that exhibits marked layering. By calculating mean square displacements, we obtained quantitative information on the diffusion of water molecules on the SCV for different coverages, revealing significant anisotropy. Additionally, our simulations provided qualitative insights into the dissociation mechanisms of water molecules at the surface.

Starting from a general molecular Hamiltonian expressed in the basis of adiabatic electronic and nuclear position states, where a compact and complete expression for the nonadiabatic derivative coupling (NDC) Hamiltonian term is obtained, we provide a general analysis of the Fermi's golden rule (FGR) rate expression for nonadiabatic transitions between adiabatic states. We then consider a quasi-adiabatic approximation that uses crude adiabatic states and NDC couplings, both evaluated at the minimum potential energy configuration of the initial adiabatic state, for the definition of the zeroth and first-order terms of the Hamiltonian. Although the application of this approximation is rather limited, it allows deriving a general FGR rate expression without further approximation while accounting for non-Condon contribution to the FGR rate arising from momentum operators of NDC terms and its coupling with vibronic displacements. For a generic and widely used model where all nuclear degrees of freedom and environmental effects are represented as linearly coupled harmonic oscillators, we derive a closed-form FGR rate expression that requires only Fourier transform. The resulting rate expression includes quadratic contributions of NDC terms and their couplings to Franck-Condon modes, which require evaluation of two additional bath spectral densities in addition to the conventional one that appears in a typical FGR rate theory based on the Condon approximation. Model calculations for the case where nuclear vibrations consist of both a sharp high-frequency mode and an Ohmic bath spectral density illustrate new features and implications of the rate expression. We then apply our theoretical expression to the nonradiative decay from the first excited singlet state of azulene, which illustrates the utility and implications of our theoretical results.

Noncovalent interactions govern many chemical and biological phenomena and are crucial in protein-protein interactions, enzyme catalysis, and DNA folding. The size of these macromolecules and their various conformations demand computationally inexpensive force fields that can accurately mimic the quantum chemical nature of the atomic noncovalent interactions. Accurate force fields, coupled with increasingly longer molecular dynamics simulations, may empower us to predict conformational changes associated with the biochemical function of proteins. Here, we aim to derive nonbonded protein force field parameters from the partitioned electron density of amino acids, the fundamental units of proteins, via the atoms-in-molecules (AIM) approach. The AIM parameters are validated using a database of charged, aromatic, and hydrophilic side-chain interactions in 610 conformations, primarily involving π-π interactions, as recently reported by one of us (Carter-Fenk et al., 2023). Electrostatic and van der Waals interaction energies calculated with nonbonded force field parameters from different AIM methodologies were compared to first-principles interaction energies from absolute localized molecular orbital-energy decomposition analysis (ALMO-EDA) at the ωB97XV/def2-TZVPD level. Our findings show that electrostatic interactions between side chains are accurately reproduced by atomic charges from the minimal basis iterative stockholder (MBIS) scheme with mean absolute errors of 4-7 kJ/mol. Meanwhile, C₆ coefficients from the MBIS AIM method effectively predict dispersion interactions with a mean error of -2 kJ/mol and a maximal error of -5 kJ/mol. As an outlook to use AIM methods in the development of protein force fields, we present the constrained AIM method that allows one to fix backbone parameters during the optimization of side-chain interactions. Backbone dihedral parameters have been optimized to reproduce secondary structure elements in proteins, and not altering them maintains compatibility with conventional protein force fields while improving the description of side-chain interactions. Our validated AIM methods allow for the depiction of noncovalent, long-range interactions in proteins using cost-effective force fields that achieve chemical precision.

Molecular dynamics (MD) simulations are a powerful tool for studying matter at the atomic scale. However, to simulate solids, an initial atomic structure is crucial for the successful execution of MD simulations but can be difficult to prepare due to insufficient atomistic information. At the same time, wide-angle X-ray scattering (WAXS) measurements can determine the radial distribution function (RDF) of atomic structures. However, the interpretation of RDFs is often challenging. Here, we present an algorithm that can bias MD simulations with RDFs by combining the information on the MD atomic interaction potential and the RDF under the principle of maximum relative entropy. We show that this algorithm can be used to adjust the RDF of one liquid model, e.g., the TIP3P water model, to reproduce the RDF and improve the angular distribution function (ADF) of another model, such as the TIP4P/2005 water model. In addition, we demonstrate that the algorithm can initiate crystallization in liquid systems, leading to both stable and metastable crystalline states defined by the RDF, e.g., crystallization of water to ice and liquid TiO₂ to rutile or anatase. Finally, we discuss how this method can be useful for improving interaction models, studying crystallization processes, interpreting measured RDFs, or training machine-learned potentials.

In this contribution, we describe a novel approach, rooted in the time-dependent density functional theory, enabling to adapt range-separated hybrids (RSHs) to correctly describe excited states (ESs) of inter- and intramolecular charge transfer (CT) character. Contrary to previous works enforcing the fulfillment of Koopmans' theorem, here, the range-split parameter of RSHs is tuned so as to constrain it in the range of distances corresponding to the hole-electron separation occurring in target CT states for the molecule of interest. The procedure proposed, while not requiring a fit but only an estimate of the CT distances for all ESs of interest, is not based on empirical adjustment and enables finding a system-dependent range separation parameter optimal for the treatment of CT states while not deteriorating its performances with respect to low Hartree-Fock exchange global hybrids for the description of ESs of a more local character. The results obtained for a series of CT compounds show the very good accuracy of this adaptative tuning procedure of RSHs and its potential to explore the ES behavior of molecular systems.