Journal of Chemical Theory and Computation最新文献

The critical importance of water in sustaining life highlights the need for accurate water models in computer simulations, aiming to mimic biochemical processes experimentally. The polarizable Gaussian multipole (pGM) model, recently introduced for biomolecular simulations, improves the handling of complex biomolecular interactions. As an integral part of our initial exploration, we examined a minimalist fixed geometry three-center pGM water model using ab initio quantum mechanical calculations of water oligomers. However, our final model development was based on liquid-phase water properties, leveraging automated machine learning (AutoML) techniques for optimization. This allows the development of a framework to refine both van der Waals and electrostatic parameters of the pGM model, aiming to accurately reproduce specific properties such as the oxygen-oxygen radial distribution function, density, and dipole moment, all at 298 K and 1.0 bar pressure. The efficacy of the optimized three-center pGM water model, pGM3P-25, was assessed through simulations of a water box of 512 water molecules, showcasing marked enhancements in both accuracy and practical utility. Notably, the model accurately reproduces thermodynamic properties not explicitly included in training while significantly reducing the time and human effort required for optimization. It was found that pGM3P-25 can reproduce temperature-dependent properties such as density, self-diffusion constants, heat capacity, second virial coefficient, and dielectric constant, which are important in biomolecular simulations. This study underscores the potential of AutoML-driven frameworks to streamline parameter refinement for molecular dynamics simulations, paving the way for broader applications in computational chemistry and beyond.

Free energy perturbation (FEP) is a promising method for accurately predicting molecular interactions, widely applied in fields such as drug design, materials science, and catalysis. However, FEP calculations, particularly those in aqueous environments, often suffer from convergence issues due to insufficient sampling of water molecules. These challenges are particularly critical in solvation-related free energy calculations, such as small molecule-protein binding, interface interactions, and molecular adsorption on surfaces. To address these limitations, we present the divide-and-conquer absolute binding free energy (DC-ABFE) method. By partitioning the ligand or molecule into atomic groups and sequentially decoupling their van der Waals interactions, DC-ABFE improves water re-entry sampling, enhances phase-space overlap, and significantly enhances the convergence of free energy calculations. Our benchmark demonstrates that DC-ABFE achieves more reproducible and reliable binding free energy predictions compared to traditional FEP methods. DC-ABFE is applicable to a range of free energy calculations involving solvation effects. Additionally, this method establishes a stronger foundation for precise drug screening, offering a more robust tool for predicting binding affinities in drug discovery.

Electrostatic preorganization is an exciting mode to understand the catalytic function of enzymes, yet limited tools exist to computationally analyze it. In particular, no methods exist to interpret the geometry, dynamics, and fundamental components of 3D electric fields, E⃗(r), in protein active sites. To address this, we present PyCPET (Python Computation of Electric Field Topologies), a comprehensive, open-source toolbox to analyze E⃗(r) in enzymes. We designed it around computational efficiency and user friendliness with both CPU- and GPU-accelerated codes. Our aim is to provide a set of functions for rich, descriptive analysis of enzyme systems including dynamics, benchmarking, distribution of streamlines analysis in 3D E⃗(r), computation of point E⃗(r), principal component analysis, and 3D E⃗(r) visualization. Finally, we demonstrate its versatility by exploring the nature of electrostatic preorganization and dynamics in three cases: Cytochrome C, Co-substituted Liver Alcohol Dehydrogenase, and HIV Protease. These test systems, along with previous work, establish PyCPET as an essential toolkit for the in-depth analysis and visualization of electric fields in enzymes, unlocking new avenues for understanding electrostatic contributions to enzyme catalysis.

Ultrafast real-time dynamics are critical for understanding a broad range of physical processes. Real-time time-dependent density functional theory (rt-TDDFT) has emerged as a powerful computational tool for simulating these dynamics, offering insight into ultrafast processes and light-matter interactions. In periodic systems, the velocity gauge is essential because it preserves the system's periodicity under an external electric field. Numerical atomic orbitals (NAOs) are widely employed in rt-TDDFT codes due to their efficiency and localized nature. However, directly applying the velocity gauge within the NAO basis set neglects the position-dependent phase variations within atomic orbitals induced by the vector potential, leading to significant computational errors - particularly in current calculations. To resolve this issue, we develop a hybrid gauge that incorporates both the electric field and the vector potential, preserving the essential phase information in atomic orbitals and thereby eliminating these errors. Our benchmark results demonstrate that the hybrid gauge fully resolves the issues encountered with the velocity gauge in NAO-based calculations, providing accurate and reliable results. This algorithm offers a robust framework for future studies on ultrafast dynamics in periodic systems using NAO bases.

Accurate modeling of metal polarization is crucial for understanding molecular interactions at metal-liquid interfaces. In this paper, we present a novel computational method for incorporating the polarization of metallic electrons into classical molecular dynamics simulations. Our approach employs a kernel-based polarization model to describe the real-time polarization of the metal electron density on a three-dimensional grid, with parameters fitted to quantum mechanical calculations. We applied this model to investigate the water-Au(111) interface, analyzing the effects of varying levels of metal polarization: (1) no polarization, (2) full polarization, and (3) time-averaged polarization. The results showed that metal electron polarization enhanced the orientational fluctuations of water molecules, stabilized the O-down configuration near the metal surface, and increased the population of nondonor hydrogen-bond configurations. The time-averaged approximation reproduces some trends observed with full polarization but introduces a bias toward lay-down configurations, leading to an overestimation of double-donor configurations. Our grid-based polarization method offers a computational approach for simulating metal polarization effects, providing new methods to investigate the electrostatics and dynamics of metal-liquid interfaces.

Protein pK_a prediction is a key challenge in computational biology. In this study, we present pKALM, a novel deep learning-based method for high-throughput protein pK_a prediction. pKALM uses a protein language model (PLM) to capture the complex sequence-structure relationships of proteins. While traditionally considered a structure-based problem, our results show that a PLM pretrained on large-scale protein sequence databases can effectively learn this relationship and achieve state-of-the-art performance. pKALM accurately predicts the pK_a values of six residues (Asp, Glu, His, Lys, Cys, and Tyr) and two termini with high precision and efficiency. It performs well at predicting both exposed and buried residues, which often deviate from standard pK_a values measured in the solvent. We demonstrate a novel finding that predicted protein isoelectric points (pI) can be used to improve the accuracy of pK_a prediction. High-throughput pK_a prediction of the human proteome using pKALM achieves a speed of 4,965 pK_a predictions per second, which is several orders of magnitude faster than existing state-of-the-art methods. The case studies illustrate the efficacy of pKALM in estimating pK_a values and the constraints of the method. pKALM will thus be a valuable tool for researchers in the fields of biochemistry, biophysics, and drug design.

This study presents a transition path sampling (TPS) procedure to create an ensemble of trajectories describing a chemical transformation from a reactant to a product state, augmented with a computer vision technique. A 3D convolutional neural network (CNN) sorts the slices of the TPS trajectories into reactant or product state categories, which aids in automatically accepting or rejecting a newly generated trajectory. Furthermore, information about the geometrical configuration of each slice enables one to calculate the percentage of reactant and product states within a specific shooting range. These statistics are used to determine the most appropriate shooting range and, if needed, to improve a shooting acceptance rate. To test the automated 3D CNN TPS technique, we applied it to collect an ensemble of the transition paths for the rate-limiting step of the Morita-Bayliss-Hillman (MBH) reaction.

Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences, playing critical roles in cellular processes and disease pathways. Computational methods, particularly λ-Dynamics, offer a promising approach for predicting TF relative binding affinities. This study evaluates the effectiveness of different λ-Dynamics perturbation schemes in determining binding free energy changes (ΔΔG_b) of the WRKY transcription factor upon mutating its W-box binding site (GGTCAA) to a nonspecific sequence (GATAAA). Among the schemes tested, the single λ per base pair protocol demonstrated the fastest convergence and highest precision. Extending this protocol to additional mutants (GGTCCG and GGACAA) yielded ΔΔG_b values that successfully ranked binding affinities, showcasing its strong potential for high-throughput screening of DNA binding sites.

Creating analytic representations of multiple potential energy surfaces for modeling electronically nonadiabatic processes is a major challenge being addressed in various ways by the chemical dynamics community. In this work, we introduce a new method that can achieve convenient learning of multiple potential energy surfaces (PESs) and their gradients (negatives of the forces) for a polyatomic system. This new method, called compatibilization by deep neural network (CDNN), is demonstrated to be accurate and, even more importantly, to be automatic. The only required input is a database with geometries and potential energies. The method produces a matrix, called the compatible potential energy matrix (CPEM), that may be interpreted as the electronic Hamiltonian in an implicit nonadiabatic basis, and the analytic adiabatic potential energy surfaces and their gradients are obtained by diagonalization and automatic differentiation. We show that the CPEM, which is neither adiabatic nor necessarily diabatic, can be discovered automatically during the learning procedure by the special design of a CDNN architecture. We believe that the CDNN method will be very useful in practice for learning coupled PESs for polyatomic systems because it is accurate and fully automatic.

The Fermi potential, appearing in the basic equations of density functional theory (DFT), has been found to be an indispensable tool for the measurement of electron localization intensity in molecules and crystals. The regions of the most intensive electron localization appear there as negative wells, while the positive barriers of the Fermi potential prevent the electron concentration there to some extent. The shape of the Fermi potential distribution in covalent bonds reflects the bond order, while the structure of its components is able to provide valuable information about the bonding nature, e.g., helping to draw the line between covalent and noncovalent bonds. The accuracy of the Fermi potential's estimates of electron (de)localization stems from the ability of its components to preserve all the main features of the exchange-correlation hole behavior within the one-electron functions, while other popular descriptors can easily fail in this task. Such analysis is not restricted to DFT calculations; when applied to post-Hartree-Fock wave functions, it unravels details of how instantaneous Coulomb correlation prevents the overestimation of electron localization intensity in strongly correlated and ordinary systems. Generally, the slight decrease in localization intensity is achieved by the intensified response of electron correlation to variations in electron density, while in systems where instantaneous Coulomb correlation is particularly important, it also comes from the growth in the exchange-correlation hole mobility; the average hole depth increases in all cases.