Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications

Nonadiabatic Exciton Dynamics and Energy Gradients in the Framework of FMO-LC-TDDFTB

We introduce a novel methodology for simulating the excited-state dynamics of extensive molecular aggregates in the framework of the long-range corrected time-dependent density-functional tight-binding fragment molecular orbital method (FMO-LC-TDDFTB) combined with the mean-field Ehrenfest method. The electronic structure of the system is described in a quasi-diabatic basis composed of locally excited and charge-transfer states of all fragments. In order to carry out nonadiabatic molecular dynamics simulations, we derive and implement the excited-state gradients of the locally excited and charge-transfer states. Subsequently, the accuracy of the analytical excited-state gradients is evaluated. The applicability to the simulation of exciton transport in organic semiconductors is illustrated on a large cluster of anthracene molecules. Additionally, nonadiabatic molecular dynamics simulations of a model system of benzothieno-benzothiophene molecules highlight the method's utility in studying charge-transfer dynamics in organic materials. Our new methodology will facilitate the investigation of excitonic transfer in extensive biological systems, nanomaterials, and other complex molecular systems consisting of thousands of atoms.

Quantitative analysis of charge transfer plasmons in silver nanocluster dimers using semiempirical methods

Plasmonic metal nanoclusters are widely used in chemistry, nanotechnology, and biomedicine. In metal nanocluster dimers, coupling of the plasmons leads to the emergence of two distinct types of modes: (1) bonding dipole plasmons (BDP), which occurs when charge oscillates synchronously within each nanocluster, and (2) charge transfer plasmons (CTP), which occurs when charge oscillates between two conductively linked nanoclusters. Although TDDFT-based modeling has uncovered some trends in these modes, it is computationally expensive for large dimers, and quantitative analysis is challenging. Here, we demonstrate that the semiempirical quantum mechanical method INDO/CIS enables us to quantify the CTP character of each excited state efficiently. In end-to-end Ag nanowire dimers, the longitudinal states have CTP character that decreases with increasing gap distance and nanowire length. In side-by-side dimers, the transverse states have CTP character and generally larger than in the end-to-end dimers, particularly for the longer nanowires. In side-by-side dimers where one nanowire is shifted along the length of the other, the CTP character of the longitudinal states peaks when the dimer is shifted by two Ag-Ag bond lengths, while the transverse states show decreasing CTP character as displacement increases. In the larger Ag31+ nanorod dimers, CTP character follow a similar distance dependence to that seen in the small nanowire but have smaller overall CTP character than the nanowires. Our study demonstrates that INDO/CIS is capable of modeling metal nanocluster dimers at a low computational cost, making it possible to study larger dimers that are difficult to analyze using TDDFT.

QM/MM Free Energy Calculations of Long-Range Biological Protonation Dynamics by Adaptive and Focused Sampling

Water-mediated proton transfer reactions are central for catalytic processes in a wide range of biochemical systems, ranging from biological energy conversion to chemical transformations in the metabolism. Yet, the accurate computational treatment of such complex biochemical reactions is highly challenging and requires the application of multiscale methods, in particular hybrid quantum/classical (QM/MM) approaches combined with free energy simulations. Here, we combine the unique exploration power of new advanced sampling methods with density functional theory (DFT)-based QM/MM free energy methods for multiscale simulations of long-range protonation dynamics in biological systems. In this regard, we show that combining multiple walkers/well-tempered metadynamics with an extended system adaptive biasing force method (MWE) provides a powerful approach for exploration of water-mediated proton transfer reactions in complex biochemical systems. We compare and combine the MWE method also with QM/MM umbrella sampling and explore the sampling of the free energy landscape with both geometric (linear combination of proton transfer distances) and physical (center of excess charge) reaction coordinates and show how these affect the convergence of the potential of mean force (PMF) and the activation free energy. We find that the QM/MM-MWE method can efficiently explore both direct and water-mediated proton transfer pathways together with forward and reverse hole transfer mechanisms in the highly complex proton channel of respiratory Complex I, while the QM/MM-US approach shows a systematic convergence of selected long-range proton transfer pathways. In this regard, we show that the PMF along multiple proton transfer pathways is recovered by combining the strengths of both approaches in a QM/MM-MWE/focused US (FUS) scheme and reveals new mechanistic insight into the proton transfer principles of Complex I. Our findings provide a promising basis for the quantitative multiscale simulations of long-range proton transfer reactions in biological systems.

A neural network potential based on pairwise resolved atomic forces and energies

Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)-based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we have introduced the ML-based PairF-Net approach to neural network potentials, that adopts a pairwise interatomic scheme to predicting forces within a molecular system. Here, we further develop the PairF-Net model to intrinsically incorporate energy conservation and couple the model to a molecular mechanical (MM) environment within the OpenMM package. The updated PairF-Net model yields energy and force predictions and dynamical distributions in good agreement with the rMD17 dataset of ten small organic molecules in the gas-phase. We further show that these in vacuo ML models of small molecules can be applied to force predictions in aqueous solution via hybrid ML/MM simulations. We present a new benchmark dataset for these ten molecules in solution, obtained from QM/MM simulations, which we denote as rMD17-aq (https://zenodo.org/records/10048644); and assess the ability of PairF-Net to reproduce the molecular energy, atomic forces and dynamical distributions of these solution conformations via ML/MM simulations.

Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions

The computational demand posed by applying multi-Slater determinant trials in phaseless auxiliary-field quantum Monte Carlo methods (MSD-AFQMC) is particularly significant for molecules exhibiting strong correlations. Here, we propose using direct-product wave functions as trials for MSD-AFQMC, aiming to reduce computational overhead by leveraging the compactness of multi-Slater determinant trials in direct-product form (DP-MSD). This efficiency arises when the active space can be divided into noncoupling subspaces, a condition we term "decomposable active space". By employing localized-active space self-consistent field wave functions as an example of such trials, we demonstrate our proposed approach across a range of molecular systems, each exhibiting varying degrees of complexity in their electronic structures. Our findings indicate that the compact DP-MSD trials can reduce computational costs substantially, by up to 36 times for the C2H6N4 molecule where the two double bonds between nitrogen N=N are clearly separated by a C-C single bond, while maintaining accuracy when active spaces are decomposable. In the case of larger systems such as the benzene dimer, characterized by weak coupling between the two monomers, we observed a decrease in computational cost compared to using a complete active space trial, yet we retained the same level of accuracy. However, for systems where these active subspaces strongly couple, a scenario we refer to as "strong subspace coupling", the method's accuracy decreases compared to that achieved with a complete active space approach. We anticipate that our method will be beneficial for systems with noncoupling to weakly coupling subspaces that require local multireference treatments.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

Sorting drug conformers in enzyme active sites: the XTB way

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Prediction of induced magnetism in 2D Ti2C based MXenes by manipulating the mixed surface functionalization and metal substitution computed by xTB model Hamiltonian of the DFTB method

We employed the recently developed density functional tight binding (DFTB) method's Hamiltonian, GFN1-xTB, for modeling the mixed termination in Ti2C MXenes, namely three types of termination by combining -O and -OH, -O and -F, and -F and -OH. We demonstrated that the approach yields reliable predictions for the electronic and magnetic properties of such MXenes. The first highlighted result is that the mixed surface functionalization in Ti2CAxBy MXenes induces spin polarization with diverse magnetic alignments, including ferromagnetism and two types of antiferromagnetism. We further identified the magnetic alignment for the investigated MXene in terms of the compositions of the terminal groups. Moreover, the effect of the transition metal (Ti) substituted by the Sc atom on the electronic and magnetic properties was also investigated. We found that the studied systems maintain the magnetism and the metallic characteristics. A magnetic transition from antiferromagnetic (AFM) to ferrimagnetic (FiM) ordering was found for ScTi15C8F8(OH)8 and ScTi15C8F12(OH)4 compounds. Finally, we proved that incorporating the Sc atom into the lattice of Ti2CO2 and the mixed surface termination in Ti2CAxBy is an effective strategy to induce magnetism. Our study may provide a new potential application for designing MXene-based spintronics.

Perspective on Integrative Simulations of Bioenergetic Domains

Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.

Multilevel Framework for Analysis of Protein Folding Involving Disulfide Bond Formation

In this study, a three-layered multicenter ONIOM approach is implemented to characterize the naive folding pathway of bovine pancreatic trypsin inhibitor (BPTI). Each layer represents a distinct level of theory, where the initial layer, encompassing the entire protein, is modeled by a general all-atom force-field GFN-FF. An intermediate electronic structure layer consisting of three multicenter fragments is introduced with the state-of-the-art semiempirical tight-binding method GFN2-xTB. Higher accuracy, specifically addressing the breaking and formation of the three disulfide bonds, is achieved at the innermost layer using the composite DFT method r2SCAN-3c. Our analysis sheds light on the structural stability of BPTI, particularly the significance of interlinking disulfide bonds. The accuracy and efficiency of the multicenter QM/SQM/MM approach are benchmarked using the oxidative formation of cystine. For the folding pathway of BPTI, relative stabilities are investigated through the calculation of free energy contributions for selected intermediates, focusing on the impact of the disulfide bond. Our results highlight the intricate trade-off between accuracy and computational cost, demonstrating that the multicenter ONIOM approach provides a well-balanced and comprehensive solution to describe electronic structure effects in biomolecular systems. We conclude that multiscale energy landscape exploration provides a robust methodology for the study of intriguing biological targets.

CREST-A program for the exploration of low-energy molecular chemical space

Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.

Chiral Induced Spin Selectivity

Since the initial landmark study on the chiral induced spin selectivity (CISS) effect in 1999, considerable experimental and theoretical efforts have been made to understand the physical underpinnings and mechanistic features of this interesting phenomenon. As first formulated, the CISS effect refers to the innate ability of chiral materials to act as spin filters for electron transport; however, more recent experiments demonstrate that displacement currents arising from charge polarization of chiral molecules lead to spin polarization without the need for net charge flow. With its identification of a fundamental connection between chiral symmetry and electron spin in molecules and materials, CISS promises profound and ubiquitous implications for existing technologies and new approaches to answering age old questions, such as the homochiral nature of life. This review begins with a discussion of the different methods for measuring CISS and then provides a comprehensive overview of molecules and materials known to exhibit CISS-based phenomena before proceeding to identify structure-property relations and to delineate the leading theoretical models for the CISS effect. Next, it identifies some implications of CISS in physics, chemistry, and biology. The discussion ends with a critical assessment of the CISS field and some comments on its future outlook.

ATR-far-ultraviolet spectroscopy: a challenge to new σ chemistry

This review reports the recent progress on ATR-far ultraviolet (FUV) spectroscopy in the condensed phase. ATR-FUV spectroscopy for liquids and solids enables one to explore various topics in physical chemistry, analytical chemistry, nanoscience and technology, materials science, electrochemistry, and organic chemistry. In this review, we put particular emphasis on the three major topics: (1) studies on electronic transitions and structures of various molecules, which one cannot investigate via ordinary UV spectroscopy. The combined use of ATR-FUV spectroscopy and quantum chemical calculations allows for the investigation of various electronic transitions, including σ, n-Rydberg transitions. ATR-FUV spectroscopy may open a new avenue for σ-chemistry. (2) ATR-FUV spectroscopy enables one to measure the first electronic transition of water at approximately 160 nm without peak saturation. Using this band, one can study the electronic structure of water, aqueous solutions, and adsorbed water. (3) ATR-FUV spectroscopy has its own advantages of the ATR method as a surface analysis method. ATR-FUV spectroscopy is a powerful technique for exploring a variety of top surface phenomena (∼50 nm) in adsorbed water, polymers, graphene, organic materials, ionic liquids, and so on. This review briefly describes the principles, characteristics, and instrumentation of ATR-FUV spectroscopy. Next, a detailed description about quantum chemical calculation methods for FUV and UV regions is given. The recent application of ATR-FUV-UV spectroscopy studies on electronic transitions from σ orbitals in various saturated molecules is introduced first, followed by a discussion on the applications of ATR-FUV spectroscopy to studies on water, aqueous solutions, and adsorbed water. Applications of ATR-FUV spectroscopy in the analysis of other materials such as polymers, ionic liquids, inorganic semiconductors, graphene, and carbon nanocomposites are elucidated. In addition, ATR-FUV-UV-vis spectroscopy focusing on electrochemical interfaces is outlined. Finally, FUV-UV-surface plasmon resonance studies are discussed.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Molecular level investigation of interactions between pharmaceuticals and β-cyclodextrin (β-CD) functionalized adsorption sites for removal of pharmaceutical contaminants from water

This study describes a novel application of the use of molecular modeling tools for investigating the adsorption of organic micropollutants (OMPs) from water by nanocomposites. The partitioning of pharmaceuticals onto β-Cyclodextrin (β-CD) functionalized adsorbents was investigated at the molecular level to explore the atomistic interactions of pharmaceutical contaminants in water systems with β-CD and to provide insight into possible approaches for removal of pharmaceuticals from water. Molecular electrostatic surface potential mapping of β-CD derivatives was employed to examine the impact of substitution degree of β-CD and type of grafting agent on host-guest complexation. The stability of the complexes of selected pharmaceuticals and β-CD derivatives were assessed via molecular dynamics simulations to evaluate competitive adsorption between organic micropollutants (OMPs) and between OMPs and fulvic acid, a representative natural organic material (NOM) component found in water systems. Molecular electrostatic surface potential maps showed that grafting agents with aromatic and amine functional groups were found to provide attractive interactions for negatively charged OMPs. In addition, optimization of substitution degree of β-CD is necessary to enhance adsorption of target OMPs. Furthermore, it was found that aromatic ring bearing grafting agents can provide additional electrostatic attractions by π-π interactions with the aromatic ring of the OMPs. The impact of common water quality characteristics on adsorption was assessed and it was revealed that the effect of pH and calcium on adsorption depends on the ionizable functional groups present on the grafting agent. Molecular dynamics simulations showed that adsorption of target OMPs does not solely depend on hydrophobicity but is affected by electrostatic interactions. The simulations revealed that fulvic acid which is commonly present in environmental waters can be a competitor with ibuprofen for the β-CD cavity. Ultimately, this study showed that molecular level simulation can be effectively employed to investigate adsorption of OMPs by β-CD functionalized adsorbents and could be employed to enhance their design and subsequent environmental applications.

Exploring torsional conformer space with physical prior mean function-driven meta-Gaussian processes

We present a novel approach for systematically exploring the conformational space of small molecules with multiple internal torsions. Identifying unique conformers through a systematic conformational search is important for obtaining accurate thermodynamic functions (e.g., free energy), encompassing contributions from the ensemble of all local minima. Traditional geometry optimizers focus on one structure at a time, lacking transferability from the local potential-energy surface (PES) around a specific minimum to optimize other conformers. In this work, we introduce a physics-driven meta-Gaussian processes (meta-GPs) method that not only enables efficient exploration of target PES for locating local minima but, critically, incorporates physical surrogates that can be applied universally across the optimization of all conformers of the same molecule. Meta-GPs construct surrogate PESs based on the optimization history of prior conformers, dynamically selecting the most suitable prior mean function (representing prior knowledge in Bayesian learning) as a function of the optimization progress. We systematically benchmarked the performance of multiple GP variants for brute-force conformational search of amino acids. Our findings highlight the superior performance of meta-GPs in terms of efficiency, comprehensiveness of conformer discovery, and the distribution of conformers compared to conventional non-surrogate optimizers and other non-meta-GPs. Furthermore, we demonstrate that by concurrently optimizing, training GPs on the fly, and learning PESs, meta-GPs exhibit the capacity to generate high-quality PESs in the torsional space without extensive training data. This represents a promising avenue for physics-based transfer learning via meta-GPs with adaptive priors in exploring torsional conformer space.

Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations

We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.

Multipole Expansion of Atomic Electron Density Fluctuation Interactions in the Density-Functional Tight-Binding Method

The accuracy of the density-functional tight-binding (DFTB) method in describing noncovalent interactions is limited due to its reliance on monopole-based spherical charge densities. In this study, we present a multipole-extended second-order DFTB (mDFTB2) method that takes into account atomic dipole and quadrupole interactions. Furthermore, we combine the multipole expansion with the monopole-based third-order contribution, resulting in the mDFTB3 method. To assess the accuracy of mDFTB2 and mDFTB3, we evaluate their performance in describing noncovalent interactions, proton transfer barriers, and dipole moments. Our benchmark results show promising improvements even when using the existing electronic parameters optimized for the original DFTB3 model. Both mDFTB2 and mDFTB3 outperform their monopole-based counterparts, DFTB2 and DFTB3, in terms of accuracy. While mDFTB2 and mDFTB3 perform comparably for neutral and positively charged systems, mDFTB3 exhibits superior performance over mDFTB2 when dealing with negatively charged systems and proton transfers. Overall, the incorporation of the multipole expansion significantly enhances the accuracy of the DFTB method in describing noncovalent interactions and proton transfers.

Regularized and Opposite Spin-Scaled Functionals from Møller-Plesset Adiabatic Connection─Higher Accuracy at Lower Cost

Noncovalent interactions (NCIs) play a crucial role in biology, chemistry, material science, and everything in between. To improve pure quantum-chemical simulations of NCIs, we propose a methodology for constructing approximate correlation energies by combining an interpolation along the Møller-Plesset adiabatic connection (MP AC) with a regularization and spin-scaling strategy applied to MP2 correlation energies. This combination yields cosκos-SPL2, which exhibits superior accuracy for NCIs compared to any of the individual strategies. With the N4 formal scaling, cosκos-SPL2 is competitive or often outperforms more expensive dispersion-corrected double hybrids for NCIs. The accuracy of cosκos-SPL2 particularly shines for anionic halogen bonded complexes, where it surpasses standard dispersion-corrected DFT by a factor of 3 to 5.

When catchers meet - a computational study on the dimerization of the Buckycatcher

We study the dimerization of the buckycatcher in gas phase and in toluene. We created an extensive library of 36 different complexes, which were characterized at semi-empirical and DFT levels. Semi-empirical geometries and dimerization energies compare well against reference data or Density Functional Theory calculations we performed. Born-Oppenheimer molecular dynamics was used to understand what happens when two molecules of the buckycatcher meet, allowing us to infer on the lack of kinetic barriers when dimers form. Thermodynamically, it is possible that room temperature solutions contain dimerized buckycatcher. Using a very simple exchange model, it is shown, however, that dimerization cannot compete thermodynamically against complexation with fullerenes, which accounts for experimental observations.

Corrigendum: Coupled cluster theory on modern heterogeneous supercomputers

[This corrects the article DOI: 10.3389/fchem.2023.1154526.].

Tannic acid-derived selective capture of bacteria from apple juice

Given the enormous burden pathogens pose on human health, rapid capture and removal of bacteria for sterilization or further bacterial detection is essential. Herein, tannic acid-functionalized virus-like Fe₃O₄ (vFe₃O₄-TA) was established for bacterial enrichment. We investigated the ability of vFe₃O₄-TA to capture Gram-negative bacteria (E. coli, S. flex and S. typhi) and Gram-positive bacteria (S. aureus, MRSA and LM), respectively. Compared to the capture efficiency of <15 % for Gram-negative bacteria, vFe₃O₄-TA showed excellent selectivity and efficiency in isolating Gram-positive bacteria with >87 % removal efficiency. GFN-xTB semiempirical quantum chemical calculations revealed that the selective recognition originates from the high affinity between TA and peptidoglycan. Without impacting ingredients, the TA-mediated trapper also shows excellent ability to distinguish Gram-positive bacteria in juice samples. These results are expected to reveal the interaction of TA with bacteria, and inaugurate a potential natural safe tool for food safety control, medical treatment and environmental remediation.

Coupled cluster theory on modern heterogeneous supercomputers

This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory-a linear-scaling, massively parallel framework-as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.

Effect of surface functional groups on MXene conductivity

We report the in-plane electron transport in the MXenes (i.e., within the MXene layers) as a function of composition using the density-functional tight-binding method, in conjunction with the non-equilibrium Green's functions technique. Our study reveals that all MXene compositions have a linear relationship between current and voltage at lower potentials, indicating their metallic character. However, the magnitude of the current at a given voltage (conductivity) has different trends among different compositions. For example, MXenes without any surface terminations (Ti3C2) exhibit higher conductivity compared to MXenes with surface functionalization. Among the MXenes with -O and -OH termination, those with -O surface termination have lower conductivity than the ones with -OH surface terminations. Interestingly, conductivity changes with the ratio of -O and -OH on the MXene surface. Our calculated I-V curves and their conductivities correlate well with transmission functions and the electronic density of states around the Fermi level. The surface composition-dependent conductivity of the MXenes provides a path to tune the in-plane conductivity for enhanced pseudocapacitive performance.

Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation

Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical density matrix perturbation theory, and an adaptive Krylov subspace approximation for the integration of the electronic equations of motion within the XL-BOMB approach (KSA-XL-BOMD). The PyTorch implementation leverages the use of GPU and machine learning hardware accelerators for the simulations. The new XL-BOMD formulation allows studying more challenging chemical systems with charge instabilities and low electronic energy gaps. The current public release of PySeQM continues our development of modular architecture for large-scale simulations employing semi-empirical quantum-mechanical treatment. Applied to molecular dynamics, simulation of 840 carbon atoms, one integration time step executes in 4 s on a single Nvidia RTX A6000 GPU.

Hybrid Quantum Mechanical/Molecular Mechanical Methods For Studying Energy Transduction in Biomolecular Machines

Hybrid quantum mechanical/molecular mechanical (QM/MM) methods have become indispensable tools for the study of biomolecules. In this article, we briefly review the basic methodological details of QM/MM approaches and discuss their applications to various energy transduction problems in biomolecular machines, such as long-range proton transports, fast electron transfers, and mechanochemical coupling. We highlight the particular importance for these applications of balancing computational efficiency and accuracy. Using several recent examples, we illustrate the value and limitations of QM/MM methodologies for both ground and excited states, as well as strategies for calibrating them in specific applications. We conclude with brief comments on several areas that can benefit from further efforts to make QM/MM analyses more quantitative and applicable to increasingly complex biological problems.

Hydrogen-Bonded π Complexes between Phosphaethyne and Hydrogen Chloride

The highly labile complexes between phosphaethyne (HCP) and hydrogen chloride (HCl) with 1:1 and 1:2 stoichiometries have been generated in Ar and N₂ matrices at 10 K through laser photolysis of the molecular precursors 1-chlorophosphaethene (CH₂PCl) and dichloromethylphosphine (CH₃PCl₂), respectively. The IR spectrum of the 1:1 complex suggests the preference of a single "T-shaped" structure in which HCl acts as the hydrogen donor that interacts with the electron-rich C≡P triple bond. In contrast, three isomeric structures for the 1:2 complex bearing a core structure of the "T-shaped" 1:1 complex are present in the matrix. The spectroscopic identification of these rare HCP π-electron complexes is supported by D-isotope labeling and the quantum chemical calculations at the CCSD(T)-F12a/cc-pVTZ-F12 level of theory.

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

A Buckycatcher in Solution-A Computational Perspective

In this work, we study the buckycatcher (C₆₀H₂₈) in solution using quantum chemical models. We investigate the conformational equilibria in several media and the effects that molecules of solvent might have in interconversion barriers between the different conformers. These are studied in a hypothetical gas phase, in the dielectric of a solvent, as well as with hybrid solvation. In the latter case, due to a disruption of π-stacking interactions, the transition states are destabilized. We also evaluate the complexation of the buckycatcher with solvent-like molecules. In most cases studied, there should be no adducts formed because the enthalpy driving force cannot overcome entropic penalties.

Accurate Prediction of Three-Body Intermolecular Interactions via Electron Deformation Density-Based Machine Learning

This work extends the electron deformation density-based descriptor, originally developed in the electron deformation density-based interaction energy machine learning (EDDIE-ML) algorithm to predict dimer interaction energies, to the prediction of three-body interactions in trimers. Using a sequential learning process to select the training data, the resulting Gaussian process regression (GPR) model predicts the three-body interaction energy within 0.2 kcal mol^-1 of the SRS-MP2/cc-pVTZ reference values for the 3B69 and S22-3 trimer data sets. A hybrid kernel function is introduced, which combines contributions from the average and individual atomic environments, allowing the total trimer interaction energy to be predicted in addition to the three-body contribution using the same descriptor. To extend the range and diversity of trimer interaction energies available in the literature, a new data set based on a protein-ligand crystal structure is introduced, consisting of 509 structures of a central ligand with two protein fragments. Benchmark calculations are provided for the new data set, which contains significantly larger molecular interactions than current databases in the literature in addition to charged fragments. Compared to density funtional theory (DFT)- and wavefunction-based methods for calculating the three-body interaction energy, our model makes predictions in a significantly shorter time frame by reducing the number of required SCF calculations from 7 to 4 performed at the PBE0 level of theory, showcasing the utility and efficiency of our Δ-ML method particularly when applied to larger systems.

Comparative study of the efficiency of silicon carbide, boron nitride and carbon nanotube to deliver cancerous drug, azacitidine: A DFT study

Herein we have made a comparative study of the efficiency of three different nanotubes viz. Carbon nanotube (CNT), boron nitride nanotube (BNNT) and silicon carbide nanotube (SiCNT) to deliver the cancerous drug, Azacitidine (AZD). The atomistic description of the encapsulation process of AZD in these nanotubes has been analyzed by evaluating parameters like adsorption energy, electrostatic potential map, reduced density gradient (RDG). Higher adsorption energy of AZD with BNNT (-0.66eV), SiCNT (-0.92eV) compared to CNT (-0.56eV) confirms stronger binding affinity of the drug for the former than the later. Charge density and electrostatic potential map suggest that charge separation involving BNNT and CNT is more prominent than SiCNT. Evaluation of different thermodynamic parameters like Gibbs free energy, enthalpy change revealed that the overall encapsulation process is spontaneous and exothermic in nature and much favorable with BNNT and SiCNT. Stabilizing interactions of the drug with BNNT and SiCNT has been confirmed from RDG analysis. ADMP molecular dynamics simulation supports that the encapsulation process of the drug within the NT at room temperature. These results open up unlimited opportunities for the applications of these NTs as a drug delivery system in the field of nanomedicine.

Accelerating the density-functional tight-binding method using graphical processing units

Acceleration of the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) was accomplished using the MAGMA linear algebra library. Two major computational bottlenecks of DFTB ground-state calculations were addressed in our implementation: the Hamiltonian matrix diagonalization and the density matrix construction. The code was implemented and benchmarked on two different computer systems: (1) the SUMMIT IBM Power9 supercomputer at the Oak Ridge National Laboratory Leadership Computing Facility with 1-6 NVIDIA Volta V100 GPUs per computer node and (2) an in-house Intel Xeon computer with 1-2 NVIDIA Tesla P100 GPUs. The performance and parallel scalability were measured for three molecular models of 1-, 2-, and 3-dimensional chemical systems, represented by carbon nanotubes, covalent organic frameworks, and water clusters.

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.

SQMBox: Interfacing a semiempirical integral library to modular ab initio electronic structure enables new semiempirical methods

Ab initio and semiempirical electronic structure methods are usually implemented in separate software packages or use entirely different code paths. As a result, it can be time-consuming to transfer an established ab initio electronic structure scheme to a semiempirical Hamiltonian. We present an approach to unify ab initio and semiempirical electronic structure code paths based on a separation of the wavefunction ansatz and the needed matrix representations of operators. With this separation, the Hamiltonian can refer to either an ab initio or semiempirical treatment of the resulting integrals. We built a semiempirical integral library and interfaced it to the GPU-accelerated electronic structure code TeraChem. Equivalency between ab initio and semiempirical tight-binding Hamiltonian terms is assigned according to their dependence on the one-electron density matrix. The new library provides semiempirical equivalents of the Hamiltonian matrix and gradient intermediates, corresponding to those provided by the ab initio integral library. This enables the straightforward combination of semiempirical Hamiltonians with the full pre-existing ground and excited state functionality of the ab initio electronic structure code. We demonstrate the capability of this approach by combining the extended tight-binding method GFN1-xTB with both spin-restricted ensemble-referenced Kohn-Sham and complete active space methods. We also present a highly efficient GPU implementation of the semiempirical Mulliken-approximated Fock exchange. The additional computational cost for this term becomes negligible even on consumer-grade GPUs, enabling Mulliken-approximated exchange in tight-binding methods for essentially no additional cost.

Reparameterization of the chemical-potential equalization model with DFTB3: A practical balance between accuracy and transferability

To improve the performance of the third-order density-functional tight-binding method (DFTB3) for non-covalent interactions involving organic and biological molecules, a chemical-potential equalization (CPE) approach was introduced [J. Phys. Chem. A, 116, 9131 (2012)] and parameterized for the H, C, N, O, and S chemical elements [J. Chem. Phys., 143, 084123 (2015)]. Based largely on equilibrium structures, the parameterized DFTB3/CPE models were shown to exhibit improvements in molecular polarizabilities and intermolecular interactions. With more extensive analyses, however, we observe here that the available DFTB3/CPE models have two critical limitations: (1) they lead to sharply varying potential energy surfaces, thus causing numerical instability in molecular dynamics (MD) simulations, and (2) they lead to spurious interactions at short distances for some dimer complexes. These shortcomings are attributed to the employed screening functions and the overfitting of CPE parameters. In this work, we introduce a new strategy to simplify the parameterization procedure and significantly reduce free parameters down to four global (i.e., independent of element type) ones. With this strategy, two new models, DFTB3/CPE(r) and DFTB3/CPE(r†) are parameterized. The new models lead to smooth potential energy surfaces, stable MD simulations, and alleviate the spurious interactions at short distances, thus representing consistent improvements for both neutral and ionic hydrogen bonds.

Long-range corrected fragment molecular orbital density functional tight-binding method for excited states in large molecular systems

Herein, we present a new method to efficiently calculate electronically excited states in large molecular assemblies, consisting of hundreds of molecules. For this purpose, we combine the long-range corrected tight-binding density functional fragment molecular orbital method (FMO-LC-DFTB) with an excitonic Hamiltonian, which is constructed in the basis of locally excited and charge-transfer configuration state functions calculated for embedded monomers and dimers and accounts explicitly for the electronic coupling between all types of excitons. We first evaluate both the accuracy and efficiency of our fragmentation approach for molecular dimers and aggregates by comparing it with the full LC-TD-DFTB method. The comparison of the calculated spectra of an anthracene cluster shows a very good agreement between our method and the LC-TD-DFTB reference. The effective computational scaling of our method has been explored for anthracene clusters and for perylene bisimide aggregates. We demonstrate the applicability of our method by the calculation of the excited state properties of pentacene crystal models consisting of up to 319 molecules. Furthermore, the participation ratio of the monomer fragments to the excited states is analyzed by the calculation of natural transition orbital participation numbers, which are verified by the hole and particle density for a chosen pentacene cluster. The use of our FMO-LC-TDDFTB method will allow for future studies of excitonic dynamics and charge transport to be performed on complex molecular systems consisting of thousands of atoms.

Divide-and-Conquer Linear-Scaling Quantum Chemical Computations

Fragmentation and embedding schemes are of great importance when applying quantum-chemical calculations to more complex and attractive targets. The divide-and-conquer (DC)-based quantum-chemical model is a fragmentation scheme that can be connected to embedding schemes. This feature article explains several DC-based schemes developed by the authors over the last two decades, which was inspired by the pioneering study of DC self-consistent field (SCF) method by Yang and Lee (J. Chem. Phys. 1995, 103, 5674-5678). First, the theoretical aspects of the DC-based SCF, electron correlation, excited-state, and nuclear orbital methods are described, followed by the two-component relativistic theory, quantum-mechanical molecular dynamics simulation, and the introduction of three programs, including DC-based schemes. Illustrative applications confirmed the accuracy and feasibility of the DC-based schemes.

Structural Properties of Metal-Organic Frameworks at Elevated Thermal Conditions via a Combined Density Functional Tight Binding Molecular Dynamics (DFTB MD) Approach

The performance of different density functional tight binding (DFTB) methods for the description of six increasingly complex metal-organic framework (MOF) compounds have been assessed. In particular the self-consistent charge density functional tight binding (SCC DFTB) approach utilizing the 3ob and matsci parameter sets have been considered for a set of four Zn-based and two Al-based MOF systems. Moreover, the extended tight binding for geometries, frequencies, and noncovalent interactions (GFN2-xTB) approach has been considered as well. In addition to the application of energy minimizations of the respective unit cells, molecular dynamics (MD) simulations at constant temperature and pressure conditions (298.15 K, 1.013 bar) have been carried out to assess the performance of the different DFTB methods at nonzero thermal conditions. In order to obtain the XRD patterns from the MD simulations, a flexible workflow to obtain time-averaged XRD patterns from (in this study 5000) individual snapshots taken at regular intervals over the simulation trajectory has been applied. In addition, the comparison of pair-distribution functions (PDFs) directly accessible from the simulation data shows very good agreement with experimental reference data obtained via measurements employing synchrotron radiation in case of MOF-5. The comparison of the lattice constants and the associated X-ray diffraction (XRD) patterns with the experimental reference data demonstrate, that the SCC DFTB approach provides a highly efficient and accurate description of the target systems.

Structure-based approach: molecular insight of pyranocumarins against α-glucosidase through computational studies

α-glucosidase is an enzyme that catalyzes the release of α-glucose molecules through hydrolysis reactions. Regulation of this enzyme can increase sugar levels in type-2 diabetes mellitus (DM) patients. Pyranocoumarin derivatives have been identified as α-glucosidase inhibitors. Through an in silico approach, this work studied the inhibition of three pyranocoumarin compounds against the α-glucosidase at the molecular level. Molecular docking and molecular dynamics simulation were performed to understand the dynamics behavior of pyranocoumarin derivatives against α-glucosidase. The prediction of free binding energy (ΔG _bind) using the Quantum Mechanics/Molecular Mechanics-Generalized Born (QM/MM-GBSA) approach for each system had the following results, PC1-α-Glu: -13.97 kcal mol^-1, PC2-α-Glu: -3.69 kcal mol^-1, and PC3-α-Glu: -13.68 kcal mol^-1. The interaction energy of each system shows that the grid score, ΔG _bind, and ΔG _exp values had a similar correlation, that was PC1-α-Glu > PC3-α-Glu > PC2-α-Glu. Additionally, the decomposition energy analysis (ΔG ^residue _bind) was carried out to find out the contribution of the key binding residue. The results showed that there were 15 key binding residues responsible for stabilizing pyranocumarin binding with criteria of ΔG ^residue _bind < -1.00 kcal mol^-1. The evaluation presented in this work could provide information on the molecular level about the inhibitory efficiency of pyranocoumarin derivatives against a-glucosidase enzyme based on computational studies.

Automated and Efficient Generation of General Molecular Aggregate Structures

Modeling intermolecular interactions of complex non-covalent structures is important in many areas of chemistry. To facilitate the generation of reasonable dimer, oligomer, and general aggregate geometries, we introduce an automated computational interaction site screening (aISS) workflow. This easy-to-use tool combines a genetic algorithm employing the intermolecular force-field xTB-IFF for initial search steps with the general force-field GFN-FF and the semi-empirical GFN2-xTB method for geometry optimizations. Compared with the alternative CREST program, aISS yields similar results but with computer time savings of 1-3 orders of magnitude. This allows for the treatment of systems with thousands of atoms composed of elements up to radon, e.g., metal-organic complexes, or even polyhedra and zeolite cut-outs which were not accessible before. Moreover, aISS can identify reactive sites and provides options like site-directed (user-guided) screening.

Accurate measurement of sequential Ar desorption energies from the dispersion-dominated Ar1-3 complexes of aromatic molecules

We present experimental determination of the energies associated with the gradual desorption of Ar atoms from the aromatic molecular surface. Non-covalently bound 2,2'-pyridylbenzimidazole-Ar_1-3 complexes were produced in the gas phase and characterized using resonant two-photon ionization (R2PI) spectroscopy. The single Ar desorption from the PBI-Ar, PBI-Ar₂ and PBI-Ar₃ complexes were measured as 581 ± 18, 656 ± 30 and 537 ± 31 cm^-1, respectively. The energies were bracketed between the last observed band in the respective R2PI spectra and the disappeared intramolecular modes of PBI. The Ar_n dissociation energies in the S₁ state were measured as 581 ± 18, 1237 ± 48 and 1774 ± 79 cm^-1, respectively, for n = 1, 2 and 3. The calculated dissociation energies of the respective complexes, obtained using three computational methods, show excellent agreement with the experimental data. The ground state dissociation energies were estimated by subtracting the Δν shift of the origin band, and the respective values are 541 ± 18, 1160 ± 48 and 1634 ± 79 cm^-1. Overall, the calculated values resulted in scaling factors ranging from 0.956 to 1.017, which depict the predictive power of the methods to determine dispersion energies. The current investigation describes a unique methodology to accurately determine the dissociation and desorption energies of Ar atoms from the surfaces of bio-relevant aromatic molecules.

Structural and Energetic Origin of Different Product Specificities and Activities for SETD3 and Its Mutants on the Methylation of the β-Actin H73K Peptide: Insights from a QM/MM Study

The methylation of the lysine residue can affect some fundamental biological processes, and specific biological effects of the methylations are often related to product specificity of methyltransferases. The question remains concerning how active-site structural features and dynamics control the activity as well as the number (1, 2, or 3) of methyl groups on methyl lysine products. SET domain containing protein 3 (SETD3) has been identified recently as the β-actin histidine73-N₃ methyltransferase, and also, it has a weak methylation activity on the H73K β-actin peptide for which the target H73 residue is mutated into K73. Interestingly, the K73 methylation activity of SETD3 increases significantly as a result of the N255 → A or N255 → F/W273 → A mutation, and the N255A product specificity also differs from that of wild-type. Here, we performed QM/MM molecular dynamics and potential of mean force (PMF) simulations for SETD3 and its mutants (N255A and N255F/W273A) to study how SETD3 and its mutants could have different product specificities and activities for the K73 methylation. The PMF simulations show that the barrier for the first methylation of K73 is higher compared to the barrier of the H73 methylation in SETD3. Moreover, the second methylation of K73 has been found to have a barrier from the free energy simulation that is higher by 2.2 kcal/mol compared to the barrier of the first methyl transfer to K73, agreeing with the suggestion that SETD3 is a monomethylase. For the first, second, and third methylations of K73 in the N255A mutant, the barriers obtained from the PMF simulations for transferring the second and third methyl groups are found to be lower relative to the barrier for the first methyl transfer. Thus, N255A can be considered as a trimethyl lysine methyltransferase. In addition, for the first K73 methylation, the activities from the PMF simulations follow the order of N255F/W273A > N255A > WT, in agreement with experiments. The examination of the structural and dynamic results at the active sites provides better understanding of different product specificities and activities for the K73 methylations in SETD3 and its mutants. It is demonstrated that the existence of well-balanced interactions at the active site leading to the near attack conformation is of crucial importance for the efficient methyl transfers. Moreover, the presence of potential interactions (e.g., the C-H···O and cation-π interactions) that are strengthening at the transition state can also be important. Furthermore, the activity as well as product specificity of the K73 methylation also seems to be controlled by certain active-site water molecules which may be released to provide extra space for the addition of more methyl groups on K73.

Synthesis of conjugates of (aR,7S)-colchicine with monoterpenoids and investigation of their biological activity

Conjugates of the natural alkaloid (aR,7S)-colchicine with bicyclic monoterpenoids and their derivatives were synthesized for the first time. Molecular docking of the synthesized agents in the active site of the main viral protease of the SARS-CoV-2 virus was carried out. The cytotoxic properties of the agents against different cell lines and the ability to inhibit the main viral protease 3CLPro were studied.

QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones against Mycobacterium tuberculosis

In this study, a series of novel quinolinone-based thiosemicarbazones were designed in silico and their activities tested in vitro against Mycobacterium tuberculosis (M. tuberculosis). Quantitative structure-activity relationship (QSAR) studies were performed using quinolinone and thiosemicarbazide as pharmacophoric nuclei; the best model showed statistical parameters of R² = 0.83; F = 47.96; s = 0.31, and was validated by several different methods. The van der Waals volume, electron density, and electronegativity model results suggested a pivotal role in antituberculosis (anti-TB) activity. Subsequently, from this model a new series of quinolinone-thiosemicarbazone 11a-e was designed and docked against two tuberculosis protein targets: enoyl-acyl carrier protein reductase (InhA) and decaprenylphosphoryl-β-D-ribose-2'-oxidase (DprE1). Molecular dynamics simulation over 200 ns showed a binding energy of -71.3 to -12.7 Kcal/mol, suggesting likely inhibition. In vitro antimycobacterial activity of quinolinone-thiosemicarbazone for 11a-e was evaluated against M. bovis, M. tuberculosis H37Rv, and six different strains of drug-resistant M. tuberculosis. All compounds exhibited good to excellent activity against all the families of M. tuberculosis. Several of the here synthesized compounds were more effective than the standard drugs (isoniazid, oxafloxacin), 11d and 11e being the most active products. The results suggest that these compounds may contribute as lead compounds in the research of new potential antimycobacterial agents.

ReaxFF-based nonadiabatic dynamics method for azobenzene derivatives

ReaxFF reactive force fields have been parameterized for the ground and first excited states of azobenzene and its derivatives. In addition, an extended set of ab initio reference data ensures wide applicability, including to azosystems in complex environments. Based on the optimized force fields, nonadiabatic surface hopping simulations produce photoisomerization quantum yields and decay times of azobenzene, both in the gas phase and in n-hexane solution, in reasonable agreement with higher level theory and experiment. The transferability to other azo-compounds is illustrated for different arylazopyrazoles as well as ethylene-bridged azobenzene. Moreover, it has been shown that the model can be easily extended to adsorbates on metal surfaces. The simulation of the ring-opening of cyclobutene triggered by the photoisomerization of azobenzene in a macrocycle highlights the advantages of a reactive force field model.

DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science

In this paper, the history, present status, and future of density-functional theory (DFT) is informally reviewed and discussed by 70 workers in the field, including molecular scientists, materials scientists, method developers and practitioners. The format of the paper is that of a roundtable discussion, in which the participants express and exchange views on DFT in the form of 302 individual contributions, formulated as responses to a preset list of 26 questions. Supported by a bibliography of 777 entries, the paper represents a broad snapshot of DFT, anno 2022.

Computational Study of Methionine Methylation Process Catalyzed by SETD3

The SETD3 enzyme has been identified as the methyltransferase for the His73 methylation in β-actin, and such methylation plays an important role in regulating the actin's biochemical properties and fine-tuning the protein's cellular roles. Further studies have demonstrated that SETD3 may be able to methylase some other residues, including lysine and methionine, that substitute His73 in the β-actin peptide. The activity of SETD3 on the Met73 peptide is low without turnover. Interestingly, it has been shown that the N255V and N255A mutations of SETD3 can increase the activity by about 3-fold for the methionine methylation, while such mutations lead to a significant reduction of k_cat for the His73 methylation. The detailed mechanism that leads to such increase of the activity for the Met73 methylation as a result of the mutations has not been understood. In this work, QM/MM molecular dynamics (MD) and potential of mean force (PMF) free energy simulations are undertaken for investigating structural, dynamic, and energetic properties involving the complex of SETD3 and Met73 peptide and to study the SETD3-catalyzed methionine methylation and the effects of the N255V mutation. It is demonstrated that the free energy barrier in the case of the methionine methylation in SETD3 is about 10 kcal/mol higher than that for the histidine methylation. Moreover, the free energy barrier for the methionine methylation in the N255V mutant is about 1 kcal/mol lower than that in the wild-type enzyme. These results agree with previous experimental observation. The origin of the free-energy barrier changes as a result of the H to M substitution on the β-actin peptide and the N255V mutation of SETD3 is discussed based on the data obtained from the simulations.

Efficient Computation of the Interaction Energies of Very Large Non-covalently Bound Complexes

We present a new benchmark set consisting of 16 large non-covalently bound systems (LNCI16) ranging from 380 up to 1988 atoms and featuring diverse interaction motives. Gas-phase interaction energies are calculated with various composite DFT, semi-empirical quantum mechanical (SQM), and force field (FF) methods and are evaluated using accurate DFT reference values. Of the employed QM methods, PBEh-3c proves to be the most robust for large systems with a relative mean absolute deviation (relMAD) of 8.5% with respect to the reference interaction energies. r2SCAN-3c yields an even smaller relMAD, at least for the subset of complexes for which the calculation could be converged, but is less robust for systems with smaller HOMO–LUMO gaps. The inclusion of Fock-exchange is therefore important for the description of very large non-covalent interaction (NCI) complexes in the gas phase. GFN2-xTB was found to be the best performer of the SQM methods with an excellent result of only 11.1% deviation. From the assessed force fields, GFN-FF and GAFF achieve the best accuracy. Considering their low computational costs, both can be recommended for routine calculations of very large NCI complexes, with GFN-FF being clearly superior in terms of general applicability. Hence, GFN-FF may be routinely applied in supramolecular synthesis planning.

1 Introduction

2 The LNCI16 Benchmark Set

3 Computational Details

4 Generation of Reference Values

5 Results and Discussion

6 Conclusions

How to Stabilize Carbenes in Enzyme Active Sites without Metal Ions

Carbenes are highly reactive compounds with unique value to synthetic chemistry. However, a small number of natural enzymes have been shown to utilize carbene chemistry, and artificial enzymes engineered with directed evolution required transition metal ions to stabilize the carbene intermediates. To facilitate the design of broader classes of enzymes that can take advantage of the rich carbene chemistry, it is thus important to better understand how to stabilize carbene species in enzyme active sites without metal ions. Motivated by our recent studies of the anaerobic ergothioneine biosynthesis enzyme EanB, we examine carbene-protein interaction with both cluster models and QM/MM simulations. The cluster calculations find that an N-heterocyclic carbene interacts strongly with polar and positively charged protein motifs. In particular, the interaction between a guanidinium group and carbene is as strong as ∼30 kcal/mol, making arginine a great choice for the preferential stabilization of carbenes. We also compare the WT EanB and its mutant in which the key tyrosine was replaced by a non-natural analogue (F2Tyr) using DFTB3/MM simulations. The calculations suggest that the carbene intermediate in the F2Tyr mutant is more stable than that in the WT enzyme by ∼3.5 kcal/mol, due to active site rearrangements that enable a nearby arginine to better stabilize the carbene in the mutant. Overall, the current work lays the foundation for the pursuit of enzyme designs that can take advantage of the unique chemistry offered by carbenes without the requirement of metal ions.

The Accuracy of Semi-Empirical Quantum Chemistry Methods on Soot Formation Simulation

Soot molecules are hazardous compounds threatening human health. Computational chemistry provides efficient tools for studying them. However, accurate quantum chemistry calculation is costly for the simulation of large-size soot molecules and high-throughput calculations. Semi-empirical (SE) quantum chemistry methods are optional choices for balancing computational costs. In this work, we validated the performances of several widely used SE methods in the description of soot formation. Our benchmark study focuses on, but is not limited to, the validation of the performances of SE methods on reactive and non-reactive MD trajectory calculations. We also examined the accuracy of SE methods of predicting soot precursor structures and energy profiles along intrinsic reaction coordinate(s) (IRC). Finally, we discussed the spin density predicted by SE methods. The SE methods validated include AM1, PM6, PM7, GFN2-xTB, DFTB2, with or without spin-polarization, and DFTB3. We found that the shape of MD trajectory profiles, the relative energy, and molecular structures predicted by SE methods are qualitatively correct. We suggest that SE methods can be used in massive reaction soot formation event sampling and primary reaction mechanism generation. Yet, they cannot be used to provide quantitatively accurate data, such as thermodynamic and reaction kinetics ones.