Halogen Bond via an Electrophilic π-Hole on Halogen in Molecules: Does It Exist?

This study reveals a new non-covalent interaction called a π-hole halogen bond, which is directional and potentially non-linear compared to its sister analog (σ-hole halogen bond). A π-hole is shown here to be observed on the surface of halogen in halogenated molecules, which can be tempered to display the aptness to form a π-hole halogen bond with a series of electron density-rich sites (Lewis bases) hosted individually by 32 other partner molecules. The [MP2/aug-cc-pVTZ] level characteristics of the π-hole halogen bonds in 33 binary complexes obtained from the charge density approaches (quantum theory of intramolecular atoms, molecular electrostatic surface potential, independent gradient model (IGM-δginter)), intermolecular geometries and energies, and second-order hyperconjugative charge transfer analyses are discussed, which are similar to other non-covalent interactions. That a π-hole can be observed on halogen in halogenated molecules is substantiated by experimentally reported crystals documented in the Cambridge Crystal Structure Database. The importance of the π-hole halogen bond in the design and growth of chemical systems in synthetic chemistry, crystallography, and crystal engineering is yet to be fully explicated.

Impact of an Ionic Liquid Solution on Horseradish Peroxidase Activity.

Horseradish peroxidase (HRP) is an enzyme that oxidizes pollutants from wastewater. A previous report indicated that peroxidases can have an enhancement in initial enzymatic activity in an aqueous solution of 0.26 M 1-ethyl-3-methylimidazolium ethyl sulfate ([EMIm][EtSO4]) at neutral pH. However, the atomistic details remain elusive. In the enzymatic landscape of HRP, compound II (Cpd II) plays a key role and involves a histidine (H42) residue. Cpd II exists as oxoferryl (2a) or hydroxoferryl (2b(FeIV)) forms, where 2a is the predominantly observed form in experimental studies. Intriguingly, the ferric 2b(FeIII) form seen in synthetic complexes has not been observed in HRP. Here, we have investigated the structure and dynamics of HRP in pure water and aqueous [EMIm][EtSO4] (0.26 M), as well as the reaction mechanism of 2a to 2b conversion using polarizable molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations. When HRP is solvated in aq [EMIm][EtSO4], the catalytic water displaces, and H42 directly orients over the ferryl moiety, allowing a direct proton transfer (PT) with a significant energy barrier reduction. Conversely, in neat water, the reaction of 2a to 2b follows the previously reported mechanism. We further investigated the deprotonated form of H42. Analysis of the electric fields at the active site indicates that the aq [EMIm][EtSO4] medium facilitates the reaction by providing a more favorable environment compared with the system solvated in neat water. Overall, the atomic level supports the previous experimental observations and underscores the importance of favorable electric fields in the active site to promote catalysis.

Quantum mechanics insights into melatonin and analogs binding to melatonin MT1 and MT2 receptors.

Melatonin receptors MT1 and MT2 are G protein-coupled receptors that mediate the effects of melatonin, a hormone involved in circadian rhythms and other physiological functions. Understanding the molecular interactions between these receptors and their ligands is crucial for developing novel therapeutic agents. In this study, we used molecular docking, molecular dynamics simulations, and quantum mechanics calculation to investigate the binding modes and affinities of three ligands: melatonin (MLT), ramelteon (RMT), and 2-phenylmelatonin (2-PMT) with both receptors. Based on the results, we identified key amino acids that contributed to the receptor-ligand interactions, such as Gln181/194, Phe179/192, and Asn162/175, which are conserved in both receptors. Additionally, we described new meaningful interactions with Gly108/Gly121, Val111/Val124, and Val191/Val204. Our results provide insights into receptor-ligand recognition's structural and energetic determinants and suggest potential strategies for designing more optimized molecules. This study enhances our understanding of receptor-ligand interactions and offers implications for future drug development.

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.

A Perspective on Protein Structure Prediction Using Quantum Computers.

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Unraveling the Catalytic Mechanism of ß-Cyclodextrin in the Vitamin D Formation.

Previous experimental studies have shown that the isomerization reaction of previtamin D3 (PreD3) to vitamin D3 (VitD3) is accelerated 40-fold when it takes place within a ß-cyclodextrin dimer, in comparison to the reaction occurring in conventional isotropic solutions. In this study, we employ quantum mechanics-based molecular dynamics (MD) simulations and statistical multistructural variational transition state theory to unveil the origin of this acceleration. We find that the conformational landscape in the PreD3 isomerization is highly dependent on whether the system is encapsulated. In isotropic media, the triene moiety of the PreD3 exhibits a rich torsional flexibility. However, when encapsulated, such a flexibility is limited to a more confined conformational space. In both scenarios, our calculated rate constants are in close agreement with experimental results and allow us to identify the PreD3 flexibility restriction as the primary catalytic factor. These findings enhance our understanding of VitD3 isomerization and underscore the significance of MD and environmental factors in biochemical modeling.

Kinetic View of Enzyme Catalysis from Enhanced Sampling QM/MM Simulations.

The rate constants of enzyme-catalyzed reactions (kcat) are often approximated from the barrier height of the reactive step. We introduce an enhanced sampling QM/MM approach that directly calculates the kinetics of enzymatic reactions, without introducing the transition-state theory assumptions, and takes into account the dynamical equilibrium between the reactive and non-reactive conformations of the enzyme/substrate complex. Our computed kcat values are in order-of-magnitude agreement with the experimental data for two representative enzymatic reactions.

Eliminating Imaginary Vibrational Frequencies in Quantum-Chemical Cluster Models of Enzymatic Active Sites.

In constructing finite models of enzyme active sites for quantum-chemical calculations, atoms at the periphery of the model must be constrained to prevent unphysical rearrangements during geometry relaxation. A simple fixed-atom or "coordinate-lock" approach is commonly employed but leads to undesirable artifacts in the form of small imaginary frequencies. These preclude evaluation of finite-temperature free-energy corrections, limiting thermochemical calculations to enthalpies only. Full-dimensional vibrational frequency calculations are possible by replacing the fixed-atom constraints with harmonic confining potentials. Here, we compare that approach to an alternative strategy in which fixed-atom contributions to the Hessian are simply omitted. While the latter strategy does eliminate imaginary frequencies, it tends to underestimate both the zero-point energy and the vibrational entropy while introducing artificial rigidity. Harmonic confining potentials eliminate imaginary frequencies and provide a flexible means to construct active-site models that can be used in unconstrained geometry relaxations, affording better convergence of reaction energies and barrier heights with respect to the model size, as compared to models with fixed-atom constraints.

Computational insights into the binding modes, keto-enol tautomerization and stereo-electronically controlled decarboxylation of oxaloacetate in the active site of macrophomate synthase.

Oxaloacetic acid (OAA) is a ß-ketocarboxylic acid, which plays an important role as an intermediate in some metabolic pathways, including the tricarboxylic acid cycle, gluconeogenesis and fatty acid biosynthesis. Animal studies have indicated that supplementing oxaloacetic acid shows an increase of lifespan and other substantial health benefits including mitochondrial DNA protection, and protection of retinal, neural and pancreatic tissues. Most of the chemical transformations of OAA in the metabolic pathways have been extensively studied; however, the understanding of decarboxylation of OAA at the atomic level is relatively lacking. Here, we carried out MD simulations and combined quantum mechanical/molecular mechanical (QM/MM) calculations as an example to systematically elucidate the binding modes, keto-enol tautomerization and decarboxylation of OAA in the active site of macrophomate synthase (MPS), which is a Mg(II)-dependent bifunctional enzyme that catalyzes both the decarboxylation of OAA and [4+2] cycloaddition of 2-pyrone with the decarboxylated intermediate of OAA (pyruvate enolate). On the basis of our calculations, it was found that the Mg2+-coordinated oxaloacetate may exist in enol forms and keto forms. The four keto forms can be transformed into each other by simply rotating the C2-C3 single bond, nevertheless, the keto-enol tautomerization strictly requires the assistance of pocket water molecules. In addition, the decarboxylation is stereo-electronically controlled, i.e., it is the relative orientation of the terminal carboxyl anion that determines the rate of decarboxylation. As such, the chemistry of oxaloacetate in the active site of MPS is complex. On one hand, the most stable binding mode (K-I) may undergo enol-keto tautomerization to isomerize to the enol form, which may further react with the second substrate; on the other hand, K-I may isomerize to another binding mode K-II to proceed decarboxylation to generate pyruvate enolate and CO2. Starting from K-I, the enol-keto tautomerization corresponds to a barrier of 16.2 kcal mol-1, whereas the decarboxylation is associated with an overall barrier of 19.7 kcal mol-1. These findings may provide useful information for understanding the chemistry of OAA and the catalysis of related enzymes, and they are basically in agreement with the available experimental kinetic data.

Origin of Enantioselectivity in Engineered Cytochrome c-Catalyzed Carbon-Radical FePP Hydrolysis Revealed Using QM/MM (ABEEM Polarizable Force Field) and MD Simulations.

The origin of highly efficient asymmetric aminohydroxylation of styrene catalyzed by engineered cytochrome c is investigated by the developed Atom-Bond Electronegativity Equalization Method polarizable force field (ABEEM PFF), which is a combined outcome of electronic and steric effects. Model molecules were used to establish the charge parameters of the ABEEM PFF, for which the bond-stretching and angle-bending parameters were obtained by using a combination of modified Seminario and scan methods. The interactions between carbon-radical Fe-porphyrin (FePP) and waters are simulated by molecular dynamics, which shows a clear preference for the pre-R over the pre-S. This preference is attributed to the hydrogen-bond between the mutated 100S and 101P residues as well as van der Waals interactions, enforcing a specific conformation of the carbon-radical FePP complex within the binding pocket. Meanwhile, the hydrogen-bond between water and the nitrogen atom in the active intermediate dictates the stereochemical outcome. Quantum mechanics/molecular mechanics (QM/MM (ABEEM PFF)) and free-energy perturbation calculations elucidate that the 3RTS is characterized by sandwich-like structure among adjacent amino acid residues, which exhibits greater stability than crowed arrangement in 3STS and enables the R enantiomer to form more favorably. Thus, this study provides mechanistic insight into the catalytic reaction of hemoproteins.

Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning.

Solvatochromism occurs in both homogeneous solvents and more complex biological environments, such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes more complicated not only because of structural effects induced by the protein pocket but also because the protein environment is highly anisotropic. This is particularly evident for highly conjugated and flexible molecules such as carotenoids, whose excitation energy is strongly dependent on both the geometry and the electrostatics of the environment. Here, we introduce a machine learning (ML) strategy trained on quantum mechanics/molecular mechanics calculations of geometrical and electrochromic contributions to carotenoids' excitation energies. We employ this strategy to compare solvatochromism in protein and solvent environments. Despite the important specifities of the protein, ML models trained on solvents can faithfully predict excitation energies in the protein environment, demonstrating the robustness of the chosen descriptors.

Quantum chemistry calculation-aided discovery of potent small-molecule mimics of glutathione peroxidases for the treatment of cisplatin-induced hearing loss.

Hearing loss (HL) is a health burden that seriously affects the quality of life of cancer patients receiving platinum-based chemotherapy, and few FDA-approved treatment specifically targets this condition. The main mechanisms that contribute to cisplatin-induced hearing loss are oxidative stress and subsequent cell death, including ferroptosis revealed by us as a new mechanism recently. In this study, we employed the frontier molecular orbital (FMO) theory approach as a convenient prediction method for the glutathione peroxidase (GPx)-like activity of isoselenazolones and discovered new isoselenazolones with great GPx-like activity. Notably, compound 19 exhibited significant protective effects against cisplatin-induced hair cell (HC) damage in vitro and in vivo and effectively reverses cisplatin-induced hearing loss through oral administration. Further investigations revealed that this compound effectively alleviated hair cell oxidative stress, apoptosis and ferroptosis. This research highlights the potential of GPx mimics as a therapeutic strategy against cisplatin-induced hearing loss. The application of quantum chemistry (QC) calculations in the study of GPx mimics sheds light on the development of new, innovative treatments for hearing loss.

Modeling Zinc Complexes Using Neural Networks.

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

Oxidative Defect Detection Within Free and Packed DNA Systems: A Quantum Mechanical/Molecular Mechanics (QM/ MM) Approach.

Base excision repair enzymes (BERs) detect and repair oxidative DNA damage with efficacy despite the small size of the defects and their often only minor structural impact. A charge transfer (CT) model for rapid scanning of DNA stretches has been evoked to explain the high detection rate in the face of numerous, small lesions. The viability of CT DNA defect detection is explored via hybrid QM/MM computational studies that leverage the accuracy of quantum mechanics (QM) for a region of interest and the descriptive power of molecularmechanics (MM) for the remainder of the system. We find that the presence of an oxidative lesion lowers theredox free energy of oxidation by approximately 1.0 eV regardless of DNA compaction (free DNA versus packed DNA in nucleosome core particles) and damage location indicating the high feasibility of a CT-based process for defect detection in DNA.

First-principles design of heavy-atom-free singlet oxygen photosensitizers for photodynamic therapy.

In photodynamic therapy (PDT) treatment, heavy-atom-free photosensitizers (PSs) are a great source of singlet oxygen photosensitizer. Reactive oxygen species (ROS) are produced by an energy transfer from the lowest energy triplet excited state to the molecular oxygen of cancer cells. To clarify the photophysical characteristics in the excited states of a few experimentally identified thionated (>C=S) molecules and their oxygenated congeners (>C=O), a quantum chemical study is conducted. This study illustrates the properties of the excited states in oxygen congeners that render them unsuitable for PDT treatment. Concurrently, a hierarchy is presented based on the utility of the lowest-energy triplet excitons of thionated compounds. Their non-radiative decay rates are calculated for reverse-ISC and inter-system crossover (ISC) processes. In addition, the vibronic importance of C=O and C=S bonds is clarified by the computation of the Huang-Rhys factor, effective vibrational mode, and reorganization energy inside the Marcus-Levich-Jörtner system. ROS generation in thionated PSs exceeds their oxygen congeners as kf ≪ kISC, where radiative decay rate is designated as kf. As a result, the current work offers a calculated strategy for analyzing the effectiveness of thionated photosensitizers in PDT.

Synthesis, Cytotoxicity, and Photophysical Investigations of 2-Amino-4,6-diphenylnicotinonitriles: An Experimental and Theoretical Study.

In this study, we present a comprehensive investigation of 2-amino-4,6-diphenylnicotinonitriles (APNs, 1-6), including their synthesis, cytotoxicity against breast cancer cell lines, and photophysical properties. Compound 3 demonstrates exceptional cytotoxicity, surpassing the potency of Doxorubicin. The fluorescence spectra of the synthesized 1-6 in different solvents reveal solvent-dependent shifts in the emission maximum values, highlighting the influence of the solvent environment on their fluorescence properties. A quantum chemical TD-DFT analysis provides insights into the electronic structure and fluorescence behavior of 1-6, elucidating HOMO-LUMO energy gaps, electronegativity values, and dipole moments, contributing to a deeper understanding of their electronic properties and potential reactivity. These findings provide valuable knowledge for the development of APNs (1-6) as fluorescent sensors and potential anticancer agents.

Quantum mechanical analysis of yttrium-stabilized zirconia and alumina: implications for mechanical performance of esthetic crowns.

BACKGROUND: Yttrium-stabilized zirconia (YSZ) and alumina are the most commonly used dental esthetic crown materials. This study aimed to provide detailed information on the comparison between yttrium-stabilized zirconia (YSZ) and alumina, the two materials most often used for esthetic crowns in dentistry. METHODOLOGY: The ground-state energy of the materials was calculated using the Cambridge Serial Total Energy Package (CASTEP) code, which employs a first-principles method based on density functional theory (DFT). The electronic exchange-correlation energy was evaluated using the generalized gradient approximation (GGA) within the Perdew (Burke) Ernzerhof scheme. RESULTS: Optimization of the geometries and investigation of the optical properties, dynamic stability, band structures, refractive indices, and mechanical properties of these materials contribute to a holistic understanding of these materials. Geometric optimization of YSZ provides important insights into its dynamic stability based on observations of its crystal structure and polyhedral geometry, which show stable configurations. Alumina exhibits a distinctive charge, kinetic, and potential (CKP) geometry, which contributes to its interesting structural framework and molecular-level stability. The optical properties of alumina were evaluated using pseudo-atomic computations, demonstrating its responsiveness to external stimuli. The refractive indices, reflectance, and dielectric functions indicate that the transmission of light by alumina depends on numerous factors that are essential for the optical performance of alumina as a material for esthetic crowns. The band structures of both the materials were explored, and the band gap of alumina was determined to be 5.853 eV. In addition, the band structure describes electronic transitions that influence the conductivity and optical properties of a material. The stability of alumina can be deduced from its bandgap, an essential property that determines its use as a dental material. Refractive indices are vital optical properties of esthetic crown materials. Therefore, the ability to understand their refractive-index graphs explains their transparency and color distortion through how the material responds to light..The regulated absorption characteristics exhibited by YSZ render it a highly attractive option for the development of esthetic crowns, as it guarantees minimal color distortion. CONCLUSION: The acceptability of materials for esthetic crowns is strongly determined by mechanical properties such as elastic stiffness constants, Young's modulus, and shear modulus. YSZ is a highly durable material for dental applications, owing to its superior mechanical strength.

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways.

BACKGROUND: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. METHOD: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. RESULTS: We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. CONCLUSION: The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .

Quantum simulation of Pauli channels and dynamical maps: Algorithm and implementation.

Pauli channels are fundamental in the context of quantum computing as they model the simplest kind of noise in quantum devices. We propose a quantum algorithm for simulating Pauli channels and extend it to encompass Pauli dynamical maps (parametrized Pauli channels). A parametrized quantum circuit is employed to accommodate for dynamical maps. We also establish the mathematical conditions for an N-qubit transformation to be achievable using a parametrized circuit where only one single-qubit operation depends on the parameter. The implementation of the proposed circuit is demonstrated using IBM's quantum computers for the case of one qubit, and the fidelity of this implementation is reported.

DFT/TDDFT calculations of geometry optimization, electronic structure and spectral properties of clevudine and telbivudine for treatment of chronic hepatitis B.

Chronic hepatitis B remains a worldwide health concern. Presently, many drugs, such as Clevudine and Telbivudine, are recommended for the treatment of chronic hepatitis B disease. For this purpose, the quantum chemical analysis of ELUMO-HOMO (Egap), ionization potential (IP), electron affinity (EA), electronegativity (EN), chemical hardness (η), chemical potential (µ), chemical softness (S), electrophilicity index (ω), electron accepting capability (ω+), electron-donating capability (ω-), Nucleophilicity index (N), additional electronic charge (∆Nmax), Optical softness (σ0) and Dipole Moment, IR and UV-Vis spectra, molecular electrostatic potential (MEP) profile, Mulliken charge analysis, natural bond orbital (NBO) were examined in this study. The dipole moment of the compounds suggests their binding pose and predicted binding affinity. The electrophilic and nucleophilic regions were identified, and techniques such as NBO, UV-Vis, and IR were used to gain insights into the molecular structure, electronic transitions, and potential drug design for Hepatitis B treatment. Calculations for this study were carried out using the Gaussian 09 program package coupled with the DFT/TDDFT technique. The hybrid B3LYP functional method and the 6-311++G(d, p) basis set were used for the calculations.