Gallic acid improves color quality and stability of red wine via physico-chemical interaction and chemical transformation as revealed by thermodynamics, real wine dynamics and benchmark quantum mechanical calculations.

The aim of this study was to explore the copigmentation effect of gallic acid on red wine color and to dissect its mechanism at the molecular level. Three-dimensional studies, e.g., in model wine, in real wine and in silico, and multiple indicators, e.g., color, spectrum, thermodynamics and phenolic dynamics, were employed. The results showed that gallic acid significantly enhanced the color quality and stability of red wine. Physico-chemical interactions and chemical transformations should be the most likely mechanism, and physico-chemical interactions are also a prerequisite for chemical transformations. QM calculations of the physico-chemical interactions proved that the binding between gallic acid and malvidin-3-O-glucoside is a spontaneous exothermic reaction driven by hydrogen bonding and dispersion forces. The sugar moiety of malvidin-3-O-glucoside and the phenolic hydroxyl groups of gallic acid affect the formation of hydrogen bonds, while the dispersion interaction was related to the stacking of the molecular skeleton.

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.

Molecular Mechanisms behind Circular Dichroism Spectral Variations between Channelrhodopsin and Heliorhodopsin Dimers.

Channelrhodopsin (ChR) and heliorhodopsin (HeR) are microbial rhodopsins with similar structures but different circular dichroism (CD) spectra: ChR shows biphasic negative and positive bands, whereas HeR shows a single positive band. We explored the physicochemical factors underlying these differences through computational methods. Using the exciton model based on first-principles computations, we obtained the CD spectra of ChR and HeR. The obtained spectra indicate that the protein dimer structures and the quantum mechanical treatment of the retinal chromophore and its interacting amino acids are crucial for accurately reproducing the experimental spectra. Further calculations revealed that the sign of the excitonic coupling was opposite between the ChR and HeR dimers, which was attributed to the contrasting second term of the orientation factor between the two retinal chromophores. These findings demonstrate that slight variations in the intermolecular orientation of the two chromophores can result in significant differences in the CD spectral shape.

Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. Here, we offered an innovative solution to identify Mcl1 inhibitors with molecular dynamics-refined pharmacophore and machine learning methods. Considering the safety and druggability of FDA-approved drugs, virtual screening of the database was performed to discover Mcl1 inhibitors, and the hit was subsequently validated via TR-FRET, cytotoxicity, and flow cytometry assays. To reveal the binding characteristics shared by the hit and a typical Mcl1 selective inhibitor, we employed quantum mechanics and molecular mechanics (QM/MM) calculations, umbrella sampling, and metadynamics in this work. The combined studies suggested that fluvastatin had promising cell inhibitory potency and was suitable for further investigation. We believe that this research will shed light on the discovery of novel Mcl1 inhibitors that can be used as a supplemental treatment against leukemia and provide a possible method to improve the accuracy of drug repurposing with limited computational resources while balancing the costs of experimentation well.

Heterogeneous integration of spin-photon interfaces with a CMOS platform.

Colour centres in diamond have emerged as a leading solid-state platform for advancing quantum technologies, satisfying the DiVincenzo criteria1 and recently achieving quantum advantage in secret key distribution2. Blueprint studies3-5 indicate that general-purpose quantum computing using local quantum communication networks will require millions of physical qubits to encode thousands of logical qubits, presenting an open scalability challenge. Here we introduce a modular quantum system-on-chip (QSoC) architecture that integrates thousands of individually addressable tin-vacancy spin qubits in two-dimensional arrays of quantum microchiplets into an application-specific integrated circuit designed for cryogenic control. We demonstrate crucial fabrication steps and architectural subcomponents, including QSoC transfer by means of a 'lock-and-release' method for large-scale heterogeneous integration, high-throughput spin-qubit calibration and spectral tuning, and efficient spin state preparation and measurement. This QSoC architecture supports full connectivity for quantum memory arrays by spectral tuning across spin-photon frequency channels. Design studies building on these measurements indicate further scaling potential by means of increased qubit density, larger QSoC active regions and optical networking across QSoC modules.

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.

Decarboxylation Mechanism of iso-Orotate Decarboxylase Revisited.

iso-Orotate decarboxylase (IDCase), which is involved in the thymidine salvage pathway, has attracted considerable interest owing to its chemical similarity to a hypothetical DNA decarboxylase in mammals. Although valuable insights into the active DNA demethylation of 5-methyl-cytosine can be obtained from the decarboxylation mechanism of 5-carboxyl-uracil (5caU) catalyzed by IDCase, this mechanism remains under debate. In this study, the catalytic mechanism of 5caU decarboxylation by IDCase was studied using hybrid quantum mechanics/molecular mechanics (QM/MM) methodologies and density functional theory (DFT) calculations with a truncated model. The calculations supported a mechanism involving three sequential stages: activation of the 5caU substrate via proton transfer from an arginine (R262') to the carboxyl group of 5caU, formation of a tetrahedral intermediate, and decarboxylation of the tetrahedral intermediate to generate uracil as the product. The reaction pathways and structures obtained using the QM/MM and DFT methods coincided with each other. These simulations provided detailed insights into the unique mechanism of IDCase, clarifying various unresolved issues, such as the critical role of R262'. In addition, aspartate D323 was found to act as a general base in the tetrahedral intermediate formation step and a general acid in the later C-C bond cleavage step.

Effects of the Y432S Cancer-Associated Variant on the Reaction Mechanism of Human DNA Polymerase κ.

Human DNA polymerases are vital for genetic information management. Their function involves catalyzing the synthesis of DNA strands with unparalleled accuracy, which ensures the fidelity and stability of the human genomic blueprint. Several disease-associated mutations and their functional impact on DNA polymerases have been reported. One particular polymerase, human DNA polymerase kappa (Pol κ), has been reported to be susceptible to several cancer-associated mutations. The Y432S mutation in Pol κ, associated with various cancers, is of interest due to its impact on polymerization activity and markedly reduced thermal stability. Here, we have used computational simulations to investigate the functional consequences of the Y432S using classical molecular dynamics (MD) and coupled quantum mechanics/molecular mechanics (QM/MM) methods. Our findings suggest that Y432S induces structural alterations in domains responsible for nucleotide addition and ternary complex stabilization while retaining structural features consistent with possible catalysis in the active site. Calculations of the minimum energy path associated with the reaction mechanism of the wild type (WT) and Y432S Pol κ indicate that, while both enzymes are catalytically competent (in terms of energetics and the active site's geometries), the cancer mutation results in an endoergic reaction and an increase in the catalytic barrier. Interactions with a third magnesium ion and environmental effects on nonbonded interactions, particularly involving key residues, contribute to the kinetic and thermodynamic distinctions between the WT and mutant during the catalytic reaction. The energetics and electronic findings suggest that active site residues favor the catalytic reaction with dCTP3- over dCTP4-.

Cysteine Radical and Glutamate Collaboratively Enable C-H Bond Activation and C-N Bond Cleavage in a Glycyl Radical Enzyme HplG.

Hydroxyprolines are abundant in nature and widely utilized by many living organisms. Isomerization of trans-4-hydroxy-d-proline (t4D-HP) to generate 2-amino-4-ketopentanoate has been found to need a glycyl radical enzyme HplG, which catalyzes the cleavage of the C-N bond, while dehydration of trans-4-hydroxy-l-proline involves a homologous enzyme of HplG. Herein, molecular dynamics simulations and quantum mechanics/molecular mechanics (QM/MM) calculations are employed to understand the reaction mechanism of HplG. Two possible reaction pathways of HplG have been explored to decipher the origin of its chemoselectivity. The QM/MM calculations reveal that the isomerization proceeds via an initial hydrogen shift from the Cγ site of t4D-HP to a catalytic cysteine radical, followed by cleavage of the Cδ-N bond in t4D-HP to form a radical intermediate that captures a hydrogen atom from the cysteine. Activation of the Cδ-H bond in t4D-HP to bring about dehydration of t4D-HP possesses an extremely high energy barrier, thus rendering the dehydration pathway implausible in HplG. On the basis of the current calculations, conserved residue Glu429 plays a pivotal role in the isomerization pathway: the hydrogen bonding between it and t4D-HP weakens the hydroxyalkyl Cγ-Hγ bond, and it acts as a proton acceptor to trigger the cleavage of the C-N bond in t4D-HP. Our current QM/MM calculations rationalize the origin of the experimentally observed chemoselectivity of HplG and propose an H-bond-assisted bond activation strategy in radical-containing enzymes. These findings have general implications on radical-mediated enzymatic catalysis and expand our understanding of how nature wisely and selectively activates the C-H bond to modulate catalytic selectivity.

Wavefunction matching for solving quantum many-body problems.

Ab initio calculations have an essential role in our fundamental understanding of quantum many-body systems across many subfields, from strongly correlated fermions1-3 to quantum chemistry4-6 and from atomic and molecular systems7-9 to nuclear physics10-14. One of the primary challenges is to perform accurate calculations for systems where the interactions may be complicated and difficult for the chosen computational method to handle. Here we address the problem by introducing an approach called wavefunction matching. Wavefunction matching transforms the interaction between particles so that the wavefunctions up to some finite range match that of an easily computable interaction. This allows for calculations of systems that would otherwise be impossible owing to problems such as Monte Carlo sign cancellations. We apply the method to lattice Monte Carlo simulations15,16 of light nuclei, medium-mass nuclei, neutron matter and nuclear matter. We use high-fidelity chiral effective field theory interactions17,18 and find good agreement with empirical data. These results are accompanied by insights on the nuclear interactions that may help to resolve long-standing challenges in accurately reproducing nuclear binding energies, charge radii and nuclear-matter saturation in ab initio calculations19,20.

Vibration assisted electron tunnelling in COVID-19 infection using quantum state diffusion.

The spread of the COVID-19 virus has become a global health crisis, and finding effective treatments and preventions is a top priority. The field of quantum biology primarily focuses on energy or charge transfer, with a particular emphasis on photosynthesis. However, there is evidence to suggest that cellular receptors such as olfactory or neural receptors may also use vibration-assisted electron tunnelling to enhance their functions. Quantum tunnelling has also been observed in enzyme activity, which is relevant to the invasion of host cells by the SARS-CoV-2 virus. Additionally, COVID-19 appears to disrupt receptors such as olfactory receptors. These findings suggest that quantum effects could provide new insights into the mechanisms of biological systems and disease, including potential treatments for COVID-19. We have applied the open quantum system approach using Quantum State Diffusion to solve the non-linear stochastic Schrödinger equation (SSE) for COVID-19 virus infection. Our model includes the mechanism when the spike protein of the virus binds with an ACE2 receptor is considered as dimer. These two entities form a system and then coupled with the cell membrane, which is modelled as a set of harmonic oscillators (bath). By simulating the SSE, we find that there is vibration-assisted electron tunnelling happening in certain biological parameters and coupling regimes. Furthermore, our model contributes to the ongoing research to understand the fundamental nature of virus dynamics. It proposes that vibration-assisted electron tunneling could be a molecular phenomenon that augments the lock-and-key process for olfaction. This insight may enhance our understanding of the underlying mechanisms governing virus-receptor interactions and could potentially lead to the development of novel therapeutic strategies.

Bifonazole caffeate: The first molecular salt of bifonazole with enhanced biopharmaceutical property based on experiments and quantum chemistry research.

In order to make novel breakthroughs in molecular salt studies of BCS class-IV antifungal medication bifonazole (BIF), a salification-driven strategy towards ameliorating attributes and aiding augment efficiency is raised. This strategy fully harnesses structural characters together attributes and benefits of caffeic acid (CAF) to concurrently enhance dissolvability and permeability of BIF by introducing the two ingredients into the identical molecular salt lattice through the salification reaction, which, coupled with the aroused potential activity of CAF significantly amplifies the antifungal efficacy of BIF. Guided by this route, the first BIF-organic molecular salt, BIF-CAF, is directionally designed and synthesized with satisfactorily structural characterizations and integrated theoretical and experimental explorations on the pharmaceutical properties. Single-crystal X-ray diffraction resolving confirms that there is a lipid-water amphiphilic sandwich structure constructed by robust charge-assistant hydrogen bonds in the salt crystal, endowing the molecular salt with the potential to enhance both dissolvability and permeability relative to the parent drug, which is validated by experimental evaluations. Remarkably, the comprehensive DFT-based theoretical investigations covering frontier molecular orbital, molecular electrostatic potential, Hirshfeld surface analysis, reduced density gradient, topology, sphericity and planarity analysis strongly support these observations, thereby allowing some positive relationships between macroscopic properties and microstructures of the molecular salt can be made. Intriguingly, the optimal properties, together with the stimulated activity of CAF markedly augment in vitro antifungal ability of the molecular salt, with magnifying inhibition zones and reducing minimum inhibitory concentrations. These findings fill in the gaps on researches of BIF-organic molecular salt, and adequately exemplify the feasibility and validity by integrating theoretical and experimental approaches to resolve BIF's problems via the salification-driven tactic.

Carbon-based molecular properties efficiently predicted by deep learning-based quantum chemical simulation with large language models.

The prediction of thermodynamic properties of carbon-based molecules based on their geometrical conformation using fluctuation and density functional theories has achieved great success in the field of energy chemistry, while the excessive computational cost provides both opportunities and challenges for the integration of machine learning. In this work, a deep learning-based quantum chemical prediction model was constructed for efficient prediction of thermodynamic properties of carbon-based molecules. We constructed a novel framework - encoding the 3D information into a large language model (LLM), which in turn generates a 2D SMILES string, while embedding a learnable encoding designed to preserve the integrity of the original 3D information, providing better structural information for the model. Additionally, we have designed an equivariant learning module to encompass representations of conformations and feature learning for conformational sampling. This framework aims to predict thermodynamic properties more accurately than learning from 2D topology alone, while providing faster computational speeds than conventional simulations. By combining machine learning and quantum chemistry, we pioneer efficient practical applications in the field of energy chemistry. Our model advances the integration of data-driven and physics-based modeling to unlock novel insights into carbon-based molecules.

Theoretical Insights into N-Glycoside Bond Cleavage of 5-Carboxycytosine by Thymine DNA Glycosylase: A QM/MM Study.

Thymine DNA glycosylase (TDG)-mediated excision of 5-formylcytosine and 5-carboxylcytosine (5-caC) is a critical step in active DNA demethylation. Herein, we employed a combined quantum mechanics/molecular mechanics approach to investigate the reaction mechanism of TDG-catalyzed N-glycosidic bond cleavage of 5-caC. The calculated results show that TDG-catalyzed 5-caC excision follows a concerted (SN2) mechanism in which glycosidic bond dissociation is coupled with nucleophile attack. Protonation of the 5-caC anion contributes to the cleavage of the N-glycoside bond, in which the N3-protonated zwitterion and imino tautomers are more favorable than carboxyl-protonated amino tautomers. This is consistent with the experimental data. Furthermore, our results reveal that the configuration rearrangement process of the protonated 5-caC would lower the stability of the N-glycoside bond and substantially reduce the barrier height for the subsequent C1'-N1 bond cleavage. This should be attributed to the smaller electrostatic repulsion between the leaving base and the negative phosphate group as a result of the structural rearrangement.

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.

An efficient computational scheme for solving coupled time-fractional Schrödinger equation via cubic B-spline functions.

The time fractional Schrödinger equation contributes to our understanding of complex quantum systems, anomalous diffusion processes, and the application of fractional calculus in physics and cubic B-spline is a versatile tool in numerical analysis and computer graphics. This paper introduces a numerical method for solving the time fractional Schrödinger equation using B-spline functions and the Atangana-Baleanu fractional derivative. The proposed method employs a finite difference scheme to discretize the fractional derivative in time, while a θ-weighted scheme is used to discretize the space directions. The efficiency of the method is demonstrated through numerical results, and error norms are examined at various values of the non-integer parameter, temporal directions, and spatial directions.

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.

Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.

Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e.g., drugs/inhibitors), replacing the expensive QM method. Additionally, two levels of MLPs are combined for the first time to overcome MLP limitations. Our unique MLPs+ONIOM-based QR methods achieve QM-level accuracy with significantly higher efficiency. Furthermore, our refinements provide computational evidence for the existence of bonded and nonbonded forms of the Food and Drug Administration (FDA)-approved drug nirmatrelvir in one SARS-CoV-2 main protease structure. This study highlights that powerful MLPs accelerate QRs for reliable protein-drug complexes, promote broader QR applications and provide more atomistic insights into drug development.

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.

Computational study on the endocrine-disrupting metabolic activation of Benzophenone-3 catalyzed by cytochrome P450 1A1: A QM/MM approach.

Predicting the metabolic activation mechanism and potential hazardous metabolites of environmental endocrine-disruptors is a challenging and significant task in risk assessment. Here the metabolic activation mechanism of benzophenone-3 catalyzed by P450 1A1 was investigated by using Molecular Dynamics, Quantum Mechanics/Molecular Mechanics and Density Functional Theory approaches. Two elementary reactions involved in the metabolic activation of BP-3 with P450 1A1: electrophilic addition and hydrogen abstraction reactions were both discussed. Further conversion reactions of epoxidation products, ketone products and the formaldehyde formation reaction were investigated in the non-enzymatic environment based on previous experimental reports. Binding affinities analysis of benzophenone-3 and its metabolites to sex hormone binding globulin indirectly demonstrates that they all exhibit endocrine-disrupting property. Toxic analysis shows that the eco-toxicity and bioaccumulation values of the benzophenone-3 metabolites are much lower than those of benzophenone-3. However, the metabolites are found to have skin-sensitization effects. The present study provides a deep insight into the biotransformation process of benzophenone-3 catalyzed by P450 1A1 and alerts us to pay attention to the adverse effects of benzophenone-3 and its metabolites in human livers.