Molecular Reaction Research Articles

Molecular responses of Mytilus coruscus hemocytes to lipopolysaccharide and peptidoglycan as revealed by 4D-DIA based quantitative proteomics analysis.

Molecular responses of Mytilus coruscus hemocytes to lipopolysaccharide and peptidoglycan as revealed by 4D-DIA based quantitative proteomics analysis.

Product-feedback in the molecular interaction-based reaction–diffusion coupling system

Product-feedback in the molecular interaction-based reaction–diffusion coupling system

Microscopic and stochastic simulations of chemically active droplets.

Biomolecular condensates play a central role in the spatial organization of living matter. Their formation is now well understood as a form of liquid-liquid phase separation that occurs very far from equilibrium. For instance, they can be modeled as active droplets, where the combination of molecular interactions and chemical reactions result in microphase separation. However, so far, models of chemically active droplets are spatially continuous and deterministic. Therefore, the relationship between the microscopic parameters of the models and some crucial properties of active droplets (such as their polydispersity, their shape anisotropy, or their typical lifetime) is yet to be established. In this work, we address this question computationally, using Brownian dynamics simulations of chemically active droplets: the building blocks are represented explicitly as particles that interact with attractive or repulsive interactions, depending on whether they are in a droplet-forming state or not. Thanks to this microscopic and stochastic view of the problem, we reveal how driving the system away from equilibrium in a controlled way determines the fluctuations and dynamics of active emulsions.

Diastereomeric Configuration Drives an On-Surface Specific Rearrangement into Low Bandgap Non-Benzenoid Graphene Nanoribbons.

Stereochemistry, usually associated with the three-dimensional arrangement of atoms in molecules, is crucial in processes like life functions, drug action, or molecular reactions. This three-dimensionality typically originates from sp3 hybridization in organic molecules, but it is also present in out-of-plane sp2-based molecules as a consequence of helical structures, twisting processes, and/or the presence of nonbenzenoid rings, the latter significantly influencing their global stereochemistry and leading to the emergence of new exotic properties. In this sense, on-surface synthesis methodologies provide the perfect framework for the precise synthesis and characterization of organic systems at the atomic scale, allowing for the accurate assessment of the associated stereochemical effects. In this work, we demonstrate the importance of the initial diastereomeric configuration in the surface-induced skeletal rearrangement of a substituted cyclooctatetraene (COT) moiety-a historical landmark in the understanding of aromaticity-into a cyclopenta[c,d]azulene (CPA) one in a chevron-like graphene nanoribbon (GNR). These findings are evidenced by combining bond-resolved scanning tunneling microscopy with theoretical ab initio calculations. Interestingly, the major well-defined product, a CPA chevron-like GNR, exhibits the lowest bandgap reported to date for an all-carbon chevron-like GNR, as evidenced by scanning tunneling spectroscopy measurements. This work paves the way for the rational application of stereochemistry in the on-surface synthesis of novel graphene-based nanostructures.

Multistep Phase Transition and Molecular Reaction of Plasmonic Nanoparticles at the Three-Phase Contact Line of an Evaporating Sessile Droplet

Multistep Phase Transition and Molecular Reaction of Plasmonic Nanoparticles at the Three-Phase Contact Line of an Evaporating Sessile Droplet

Risk aversion can promote cooperation

Abstract Cooperative dynamics are central to our understanding of many phenomena in living and complex systems. However, we lack a universal mechanism to explain the emergence of cooperation. We present a novel framework for modelling social dilemma games with an arbitrary number of players by combining reaction networks, methods from quantum mechanics applied to stochastic complex systems, game theory and stochastic simulations of molecular reactions. Using this framework, we propose a novel and robust mechanism for cooperation based on risk aversion that leads to cooperative behaviour in population games. Rather than individuals seeking to maximise payouts in the long run, individuals seek to obtain a minimum set of resources with a given level of confidence and in a limited time span. We show that this mechanism can lead to the emergence of new equilibria in a range of social dilemma games.

Quantum Control of Resonance Lifetimes in Molecular Photodissociation with Intense Laser Fields.

Control of molecular reaction dynamics has been pursued in the last decades. Among these reactions are molecular photodissociation processes governed by resonances. Controlling the lifetime of such resonances imply to control the time duration of the processes. Here, some control schemes that apply moderately intense laser fields are proposed to modify (reducing or increasing) a resonance lifetime. The control strategy applies an intense field as a way to generate a new effective coupling that produces a resonance decay different from the natural one, with a different decay lifetime. In particular, different control schemes are suggested to reduce the lifetime of a long-lived resonance, and to increase the lifetime of a short-lived resonance. A large degree and flexibility of control both in the reduction and in the increase of the resonance lifetime is demonstrated. The experimental applicability of the schemes is discussed. The present schemes thus open the possibility of extensive and universal control of molecular photodissociation processes mediated by resonances.

Machine learning approaches for predicting protein-ligand binding sites from sequence data.

Proteins, composed of amino acids, are crucial for a wide range of biological functions. Proteins have various interaction sites, one of which is the protein-ligand binding site, essential for molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key biological functions. Accurate prediction of these binding sites is pivotal in computational drug discovery, helping to identify therapeutic targets and facilitate treatment development. Machine learning has made significant contributions to this field by improving the prediction of protein-ligand interactions. This paper reviews studies that use machine learning to predict protein-ligand binding sites from sequence data, focusing on recent advancements. The review examines various embedding methods and machine learning architectures, addressing current challenges and the ongoing debates in the field. Additionally, research gaps in the existing literature are highlighted, and potential future directions for advancing the field are discussed. This study provides a thorough overview of sequence-based approaches for predicting protein-ligand binding sites, offering insights into the current state of research and future possibilities.

Simulating atmospheric freezing of single aqueous droplets to ice in a cryogenically cooled ultrasonic levitator.

Atmospheric freezing of water droplets suspended in air followed by cloud formation and precipitation represent fundamental steps of the terrestrial water cycle. These aqueous droplets exhibit distinct freezing mechanisms and thermodynamic requirements compared to bulk water often forming metastable supercooled water at subzero temperatures on the Celsius scale (<273 K) prior to crystallization. Here, we report on a real-time spectroscopic investigation combined with simultaneous visualizations of single aqueous droplet freezing events inside a cryogenically cooled ultrasonic levitation chamber with the ultimate goal of probing the molecular structure evolution and stages of ice formation. The observed droplet freezing follows a pseudoheterogeneous ice nucleation mechanism mimicking the process that occurs for atmospherically supercooled water droplets at the air-water interface. This proof-of-concept experimental setup allows future crystallization studies of homo- and heterogeneously doped aqueous droplets under simulated atmospheric environments-also in the presence of reactive trace gases, thus untangling dynamic molecular interactions and chemical reactions, which are of fundamental interest to low-temperature atmospheric chemistry delineating with ice nucleation mechanisms.

Ultrafast Infrared Plasmonics.

Ultrafast plasmonics represents a cutting-edge frontier in light-matter interactions, providing a unique platform to study electronic interactions and collective motions across femtosecond to picosecond timescales. In the infrared regime, where energy aligns with the rearrangements of low-energy electrons, molecular vibrations, and thermal fluctuations, ultrafast plasmonics can be a powerful tool for revealing ultrafast electronic phase transitions, controlling molecular reactions, and driving subwavelength thermal processes. Here, the evolution of ultrafast infrared plasmonics, discussing the recent progress in their manipulation, detection, and applications is reviewed. The future opportunities, including their potential to probe electronic correlations, investigate intrinsic ultrafast plasmonic interactions, and enable advanced applications in quantum information are highlighted, which may be promoted by multi-physical field integrated ultrafast techniques.

Thermodynamic and Kinetic Studies of Mononuclear Non-Heme High-Valent (FeO)2+ Complexes.

Mononuclear nonheme high-valent (FeO)2+ complexes participate in many enzymatic oxidation-reduction cycles in a living body and play a key role in organic synthesis. The concept of molecular ID (molecular identities) was proposed and applied in our previous work; it covers all thermodynamic data for compounds containing an active carbon-hydrogen bond: oxidation potential, hydride anion affinity, proton affinity, and hydrogen atom affinity. To facilitate quantitative analysis of the physical organic chemistry and molecular biology properties of (FeO)2+ complexes, the molecular identities and reaction thermodynamic platform of representative complexes were established based on the thermodynamic data, such as (N4Py)(FeO)2+ and (Bn-TPEN)(FeO)2+, and their kinetic characteristics. Finally, the findings of this study are as follows: first, the reaction between (N4Py)(FeO)2+ and hydride donors 1/2 (Scheme 1) followed a one-step hydride anion transfer mechanism. The reactions between (N4Py)(FeO)2+ and hydride donors 3 (Scheme 1) and between (Bn-TPEN)(FeO)2+ and hydride donors 1 followed the hydrogen atom-electron transfer mechanism. Second, by comparison of high-valent (RuO)2+ complexes and organic hydride acceptors, the essential laws in selecting the reaction mechanism were obtained to determine the reaction mechanism of this study. Third, the reaction between (N4Py)(FeO)2+ and 1 followed the electron-proton-electron transfer mechanism under acidic conditions.

On-Chip Stimulated Raman Scattering Imaging and Quantification of Molecular Diffusion in Aqueous Microfluidics.

Numerous chemical reactions and most life processes occur in aqueous solutions, where the physical diffusion of small molecules plays a vital role, including solvent water molecules, solute biomolecules, and ions. Conventional methods of measuring diffusion coefficients are often limited by technical complexity, large sample consumption, or significant time cost. Here, we present an optical imaging method to study molecular diffusion by combining stimulated Raman scattering (SRS) microscopy with microfluidics: a "Y"-shaped microfluidic channel forming two laminar flows with a stable concentration gradient across the interface. SRS imaging of a specific molecule allows us to obtain a high-resolution chemical profile of the diffusion region at varying inspection locations and flow rates, which enables the extraction of diffusion coefficients using the convection-diffusion model. As a proof of concept, we measured diffusion coefficients of molecules including water, protein, and multiple ions, with a sample volume of less than 1 mL and a time cost of less than 10 min. Moreover, we demonstrated a high-resolution three-dimensional (3D) reconstruction of the diffusion patterns in the microfluidic channel. The high-speed microfluidic SRS platform holds the potential for quantitative measurements of molecular diffusion, chemical reaction, and fluidic dynamics at the liquid-liquid interfaces.

The flavonoid metabolic pathway genes Ac4CL1, Ac4CL3 and AcHCT1 positively regulate the kiwifruit immune response to Pseudomonas syringae pv. actinidiae.

Psa primarily utilises the type III secretion system (T3SS) to deliver effector proteins (T3Es) into host cells, thereby regulating host immune responses. However, the mechanism by which kiwifruit responds to T3SS remains unclear. To elucidate the molecular reaction of kiwifruit plants to Psa infection, M228 and mutant M228△hrcS strains were employed to inoculate Actinidia chinensis var. chinensis for performing comparative transcriptional and metabolomic analyses. Transcriptome analysis identified 973 differentially expressed genes (DEGs) related to flavonoid synthesis, pathogen interaction, and hormone signaling pathways during the critical period of Psa infection at 48h post-inoculation. In the subsequent metabolomic analysis, flavonoid-related differential metabolites were significantly enriched after the loss of T3SS.Through multi-omics analysis, 22 differentially expressed genes related to flavonoid biosynthesis were identified. Finally, it was discovered that the transient overexpression of 3 genes significantly enhanced kiwifruit resistance to Psa. qRT-PCR analysis indicated that Ac4CL1, Ac4CL3 and AcHCT1 promote host resistance to disease, while Ac4CL3 negatively regulates host resistance to Psa. These findings enrich the plant immune regulation network involved in the interaction between kiwifruit and Psa, providing functional genes and directions with potential application for breeding kiwifruit resistance to canker disease.

Energy Landscapes in Chemical Reactions and Transport.

Both, molecular chemical reactions and transport of atoms in solid media are determined by the energy landscape in which the seemingly different processes take place. Chemical reactions can be described as cooperative translocation of two chemical entities on a common potential energy surface. Transport of atoms in a solid can be envisaged as the translocation of a single particle in the potential energy landscape of all other particles constituting the solid. The goal of this manuscript is to demonstrate common grounds but also distinct differences in the physico-chemical processes, their experimental quantification and their theoretical modelling. This work will span the range from the historical foundations all the way to the current challenges. While scientists at the beginning of the 20th century where commonly active in both fields, e. g., Wilhelm Jost has pioneered and shaped the field of transport in solids and reaction kinetics in Germany, the fields have drifted apart for the last 50 decades. It is now time to bring the fields together again. Ultimately, it is suggested that knowledge gained in the field of transport may in fact stimulate advancement in the field of molecular reactivity and vice versa. Here, the energy landscapes are pivotal for knowledge-based advancement.

Molecular dynamics-guided reaction discovery reveals endoperoxide-to-alkoxy radical isomerization as key branching point in α-pinene ozonolysis

Secondary organic aerosols (SOAs) significantly impact Earth’s climate and human health. Although the oxidation of volatile organic compounds (VOCs) has been recognized as the major contributor to the atmospheric SOA budget, the mechanisms by which this process produces SOA-forming highly oxygenated organic molecules (HOMs) remain unclear. A major challenge is navigating the complex chemical landscape of these transformations, which traditional hypothesis-driven methods fail to thoroughly investigate. Here, we explore the oxidation of α-pinene, a critical atmospheric biogenic VOC, using a novel reaction discovery approach based on molecular dynamics and state-of-the-art enhanced sampling techniques. Our approach successfully identifies all established reaction pathways of α-pinene ozonolysis, as well as discovers multiple novel species and pathways without relying on a priori chemical knowledge. In particular, we unveil a key branching point that leads to the rapid formation of alkoxy radicals, whose high and diverse reactivity help to explain hitherto unexplained oxidation pathways suggested by mass spectral peaks observed in α-pinene ozonolysis experiments. This branching point is likely prevalent across a variety of atmospheric VOCs and could be crucial in establishing the missing link to SOA-forming HOMs.

Gas-phase Formation of Large, Astronomically Relevant Polycyclic Aromatic Hydrocarbon Clusters

As one class of important carbon reservoirs in interstellar clouds, large polycyclic aromatic hydrocarbons (PAHs) and their derivative species play an important role in the formation and evolution of interstellar carbonaceous compounds. To understand these chemical routes, the gas-phase ion–molecular collision reaction between large, astronomically relevant PAH (dicoronylene, DC, C48H20) cations and smaller neutral superhydrogenated PAHs (2, 3–benzofluorene, C17H12) are investigated. Series of large DC/2, 3–benzofluorene cluster cations (e.g., [(C17H12)6C48H14]+, 236 atoms, and [(C17H12)5C48]+, 193 atoms) are efficiently formed by gas-phase condensation under laser irradiation conditions. With theoretical calculations, the structure of newly formed DC/2, 3-benzofluorene cluster cations and the bonding energy for these formation reactions are obtained. Moreover, the IR spectra of DC/2, 3-benzofluorene cluster cations are also calculated. The gas-phase reactions between large PAH species occur relatively easily, resulting in a very large number of reactions and very complex molecular clusters. The adduct processes and the formed molecular structure relatively depend on the carbon reaction sites. The carbon edge sites have different chemical reactivity, which may affect the abundance of these relevant interstellar substances. Furthermore, intermolecular hydrogen transfer plays an important role in cluster formation processes, which can lead the newly formed clusters to become more stable. We infer that small superhydrogenated PAH molecules (e.g., 2, 3-benzofluorene) can effectively aggregate on the large PAH molecules (e.g., dehydrogenated DC cations or carbon clusters) in the gas phase, which provides proposed chemical-evolution routes (ion–molecular reaction pathways) for the formation of the nanometer-sized dust grains in a bottom-up process (in building block pathways) in the interstellar medium.

Molecular sandwich-based DNAzyme catalytic reaction towards transducing efficient nanopore electrical detection of antigen proteins.

Despite significant advances in nanopore nucleic acid sequencing and sensing, protein detection remains challenging due to the inherent complexity of protein molecular properties (i.e., net charges, polarity, molecular conformation & dimension) and sophisticated environmental parameters (i.e., biofluids), resulting in unsatisfactory electrical signal resolution for protein detection such as poor accessibility, selectivity and sensitivity. The selection of an appropriate electroanalytical approach is strongly desired which should be capable of offering easily detectable and readable signals regarding proteins particularly depending on the practical application. Herein, a molecular sandwich-based cooperative DNAzyme catalytic reaction nanopore detecting approach was designed. Specifically, this approach uses Mg2+ catalyzed DNAzyme (10-23) toward nucleic acids digestion for efficient antigen protein examination. The proposed strategy operates by initial formation of a molecular sandwich containing capture antibody-antigen-detection antibody for efficient entrapment of target proteins (herein taking the HIV p24 antigen for example) and immobilization on magnetic beads surfaces. After that, the DNAzyme was linked to the detection antibody via a biotin-streptavidin interaction. In the presence of Mg2+, the DNAzyme catalytic reaction was triggered to digest nucleic acid substrates and release unique cleavage fragments as reporters capable of transducing more easily detectable nucleic acids as a substitute for the complicated and hard to yield protein signals, in a nanopore. Notably, experimental validation confirms the detecting stability and sensitivity for the target antigen referenced with other antigen proteins, meanwhile it demonstrates a detection efficacy in a human serum environment at very low concentration (LoD ∼1.24 pM). This cooperative DNAzyme nanopore electroanalytical approach denotes an advance in protein examination, and may benefit in vitro testing of proteinic biomarkers for disease diagnosis and prognosis assessment.

Uncovering molecular mechanisms of soybean response to 12C6+ heavy ion irradiation through integrated transcriptomic and metabolomic profiling.

Uncovering molecular mechanisms of soybean response to 12C6+ heavy ion irradiation through integrated transcriptomic and metabolomic profiling.

Characterizations of Degree-based Topological Indices in Boron-embedded Benzenoid Graphs

Background: Chemical graph theory is a crucial tool for characterizing molecular properties and reactions. It utilizes a rigorous mathematical framework to reveal the complex structures and dynamics of molecules. Methods: The atomic structure of boron is incorporated into an n-dimensional oxide network to create two sets of boron-embedded benzenoid networks. By employing mathematical analysis and graph theory, degree-based topological indices are derived. Results: Analytical solutions for molecular descriptors of degree-based topological indices in boron-embedded benzenoid networks are computed. Conclusion: The unique structures of boron-embedded benzenoid networks significantly influence the topological indices, highlighting the interplay between molecular structures.

Genetic improvement of low-lignin poplars: a new strategy based on molecular recognition, chemical reactions and empirical breeding.

As an important source of pollution in the papermaking process, the presence of lignin in poplar can seriously affect the quality and process of pulping. During lignin synthesis, Caffeoyl-CoA-O methyltransferase (CCoAOMT), as a specialized catalytic transferase, can effectively regulate the methylation of caffeoyl-coenzyme A (CCoA) to feruloyl-coenzyme A. Targeting CCoAOMT, this study investigated the substrate recognition mechanism and the possible reaction mechanism, the key residues of lignin binding were mutated and the lignin content was validated by deep convolutional neural-network model based on genome-wide prediction (DCNGP). The molecular mechanics results indicate that the binding of S-adenosyl methionine (SAM) and CCoA is sequential, with SAM first binding and inducing inward constriction of the CCoAOMT; then CCoA binds to the pocket, and this process closes the outer channel, preventing contamination by impurities and ensuring that the reaction proceeds. Next, the key residues in the recognition process of SAM (F69 and D91) and CCoA (I40, N170, Y188 and D218) were analyzed, and we identified that K146 as a base catalyst is important for inducing the methylation reaction. Immediately after that, the possible methylation reaction mechanism was deduced by the combination of Restrained Electrostatic Potential (RESP) and Independent Gradient Model (IGM) analysis, focusing on the catalytic center electron cloud density and RESP charge distribution. Finally, the DCNGP results verified that the designed mutant groups were all able to effectively reduce the lignin content and increase the S-lignin content/ G-lignin content ratio, which was beneficial for the subsequent lignin removal. Multifaceted consideration of factors that reduce lignin content and combined deep learning to screen for favorable mutations in target traits provides new ideas for targeted breeding of low-lignin poplars.