Complex Biochemical Reactions Research Articles

Influence of (ultra-)processing methods on aquatic proteins and product quality.

Aquatic products are a high-quality source of protein for humans, and the changes in protein during aquatic product processing are crucial for nutritional value, product performance, and consumer health. With the advancement of science and technology, aquatic product processing methods have become increasingly diverse. In addition to traditional methods such as thermal processing (steaming, roasting, and frying) and pickling, emerging non-thermal processing technologies, such as high pressure, ultrasound, and irradiation, are also being applied. During (ultra-)processing, aquatic products undergo complex biochemical reactions, among which protein oxidation significantly affects the quality of aquatic products. Protein oxidation can alter the molecular structure of proteins, thereby changing their functional properties and ultimately impacting product quality. This paper primarily explored the effects of protein changes under different processing methods on aquatic product quality and human health, as well as techniques for controlling protein oxidation. It aims to provide a theoretical basis for selecting appropriate processing methods, improving aquatic product quality, and controlling protein oxidation in aquatic products, and to offer scientific guidance for practical production.

Extremophiles and Extremophilic Behaviour-New Insights and Perspectives.

Extremophiles, throughout evolutionary time, have evolved a plethora of unique strategies to overcome hardships associated with the environments they are found in. Modifying their genome, showing a bias towards certain amino acids, redesigning their proteins, and enhancing their membranes and other organelles with specialised chemical compounds are only some of those strategies. Scientists can utilise such attributes of theirs for a plethora of biotechnological and astrobiological applications. Moreover, the rigorous study of such microorganisms regarding their evolution and ecological niche can offer deep insight into science's most paramount inquiries such as how life originated on Earth and whether we are alone in the universe. The intensification of studies involving extremophiles in the future can prove to be highly beneficial for humanity, even potentially ameliorating modern problems such as those related to climate change while also expanding our knowledge about the complex biochemical reactions that ultimately resulted in life as we know it today.

Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks

Blood coagulation, which involves a group of complex biochemical reactions, is a crucial step in hemostasis to stop bleeding at the injury site of a blood vessel. Coagulation abnormalities, such as hypercoagulation and hypocoagulation, could either cause thrombosis or hemorrhage, resulting in severe clinical consequences. Mathematical models of blood coagulation have been widely used to improve the understanding of the pathophysiology of coagulation disorders, guide the design and testing of new anticoagulants or other therapeutic agents, and promote precision medicine. However, estimating the parameters in these coagulation models has been challenging as not all reaction rate constants and new parameters derived from model assumptions are measurable. Although various conventional methods have been employed for parameter estimation for coagulation models, the existing approaches have several shortcomings. Inspired by the physics-informed neural networks, we propose Coagulo-Net, which synergizes the strengths of deep neural networks with the mechanistic understanding of the blood coagulation processes to enhance the mathematical models of the blood coagulation cascade. We assess the performance of the Coagulo-Net using two existing coagulation models with different extents of complexity. Our simulation results illustrate that Coagulo-Net can efficiently infer the unknown model parameters and dynamics of species based on sparse measurement data and data contaminated with noise. In addition, we show that Coagulo-Net can process a mixture of synthetic and experimental data and refine the predictions of existing mathematical models of coagulation. These results demonstrate the promise of Coagulo-Net in enhancing current coagulation models and aiding the creation of novel models for physiological and pathological research. These results showcase the potential of Coagulo-Net to advance computational modeling in the study of blood coagulation, improving both research methodologies and the development of new therapies for treating patients with coagulation disorders.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network.

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

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.

Gold Nanoclusters Synthesized within Single-Chain Nanoparticles as Catalytic Nanoreactors in Water.

Metalloenzymes are able to catalyze complex biochemical reactions in cellular (aqueous) media with high efficiency. In recent years, a variety of metal-containing single-chain nanoparticles (SCNPs) have been synthesized as simplified metalloenzyme-mimetic nano-objects. However, most of the metal-containing SCNPs reported so far contained complexed metal ions but not metal nanoclusters (NCs) with diameter <5 nm, which could be used as powerful, emerging catalysts. Herein, we report the synthesis of gold nanoclusters (Au-NCs) within SCNPs and the further use of Au-NCs/SCNPs as catalytic nanoreactors in water. We demonstrate that a common motif contained in several drugs (i.e., the aminophenyl-oxazolidinone fragment present in Rivaroxaban, Sutezolid, and Linezolid) can be efficiently prepared in water from a hydrophobic precursor compound by using the Au-NCs/SCNPs as efficient catalytic nanoreactors. In summary, this work paves the way forthe synthesis of metal-NCs/SCNPs for advanced catalysis in aqueous media.

Bottom-up data integration in polymer models of chromatin organisation

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large data sets probing this process from multiple angles, bottom-up frameworks which allow the incorporation of the sequence-specific nature of biochemistry in a unified model of 3D chromatin structure remain scarce. Here we propose SEMPER (Sequence Enhanced Magnetic PolymER), a novel stochastic polymer model which naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. We introduce a novel approximate Bayesian algorithm to quantify a posteriori the relative importance of various factors, including the polymeric nature of DNA, in determining chromatin epigenetic state, thus providing a transparent way to generate biological hypotheses. While accurate prediction of contact frequencies (a problem already extensively studied in the literature) is not our main aim, as a byproduct of the inference procedure and without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and nontrivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data and opens the way to a new class of more systematic approaches to interpreting epigenomic data.

Microbial interactions shape cheese flavour formation

Cheese fermentation and flavour formation are the result of complex biochemical reactions driven by the activity of multiple microorganisms. Here, we studied the roles of microbial interactions in flavour formation in a year-long Cheddar cheese making process, using a commercial starter culture containing Streptococcus thermophilus and Lactococcus strains. By using an experimental strategy whereby certain strains were left out from the starter culture, we show that S. thermophilus has a crucial role in boosting Lactococcus growth and shaping flavour compound profile. Controlled milk fermentations with systematic exclusion of single Lactococcus strains, combined with genomics, genome-scale metabolic modelling, and metatranscriptomics, indicated that S. thermophilus proteolytic activity relieves nitrogen limitation for Lactococcus and boosts de novo nucleotide biosynthesis. While S. thermophilus had large contribution to the flavour profile, Lactococcus cremoris also played a role by limiting diacetyl and acetoin formation, which otherwise results in an off-flavour when in excess. This off-flavour control could be attributed to the metabolic re-routing of citrate by L. cremoris from diacetyl and acetoin towards α-ketoglutarate. Further, closely related Lactococcus lactis strains exhibited different interaction patterns with S. thermophilus, highlighting the significance of strain specificity in cheese making. Our results highlight the crucial roles of competitive and cooperative microbial interactions in shaping cheese flavour profile.

Elucidating Softening Mechanism of Honey Peach (Prunus persica L.) Stored at Ambient Temperature Using Untargeted Metabolomics Based on Liquid Chromatography-Mass Spectrometry

Peach fruit softening is the result of a series of complex physiological and biochemical reactions that influence shelf life and consumer acceptance; however, the precise mechanisms underlying softening remain unclear. We conducted a metabolomic study of the flesh and peel of the honey peach (Prunus persica L.) to identify critical metabolites before and after fruit softening. Compared to the pre-softening profiles, 155 peel metabolites and 91 flesh metabolites exhibited significant changes after softening (|log2(FC)| &gt; 1; p &lt; 0.05). These metabolites were mainly associated with carbohydrate metabolism, respiratory chain and energy metabolism (citrate cycle, pantothenate and CoA biosynthesis, nicotinate and nicotinamide metabolism, and pentose and glucuronate interconversions), reactive oxygen species (ROS) metabolism, amino acid metabolism, and pyrimidine metabolism. During peach fruit softening, energy supply, carbohydrate and amino acid metabolism, oxidative damage, and plant hormone metabolism were enhanced, whereas amino acid biosynthesis and cell growth declined. These findings contribute to our understanding of the complex mechanisms of postharvest fruit softening, and may assist breeding programs in improving peach fruit quality during storage.

An Artificial Liposome Compartment with Size Exclusion Molecular Transport.

The cellular compartment plays an essential role in organizing the complex and diverse biochemical reactions within the cell. By mimicking the function of such cellular compartments, the challenge of constructing artificial compartments has been taken up to develop new biochemical tools for efficient material production and diagnostics. The important features required for the artificial compartment are that it isolates the interior from the external environment and is further functionalized to control the transport of target chemicals to regulate the interior concentration of both substrate and reaction products. In this study, an artificial compartment with size-selective molecular transport function was constructed by using a DNA origami-guided liposome prepared by modifying the method reported by Perrault et al. This completely isolates the liposome interior, including the DNA origami skeleton, from the external environment and allows the assembly of a defined number of molecules of interest inside and/or outside the compartment. By incorporating a bacterial membrane protein, OmpF, into the liposome, the resulting artificial compartment was shown to transport only the molecule of interest with a molecular weight below 600 Da from the external environment into the interior of the compartment.

Study on Metabolic Effects of protein for Microbial Metabolic Engineering

Microbial metabolic engineering is a new bioengineering technology, which can improve the yield of compounds, optimize energy utilization and solve various problems faced by human beings by changing the genetic information, metabolic pathway and metabolic flux of microorganisms. Protein is the main undertaker of life activities, and its metabolic process involves complex biochemical reactions and cell regulation mechanisms. Protein metabolism plays an important role and application in microbial metabolic engineering. The purpose of this paper is to explore the metabolic effects of protein for microbial metabolic engineering, so as to deeply understand the metabolic characteristics and regulation mechanism of microorganisms. Microbial metabolic engineering has important applications in improving the yield of compounds, optimizing energy utilization, environmental treatment and medicine. The research and application of protein metabolism can provide more in-depth theoretical basis and technical support for microbial metabolism engineering, thus promoting the development and utilization of microbial resources.

Thermoring-based heat activation switches in the TRPV1 biothermometer

Non-covalent interactions in bio-macromolecules are individually weak but collectively important. How they take a concerted action in a complex biochemical reaction network to realize their thermal stability and activity is still challenging to study. Here graph theory was used to investigate how the temperature-dependent non-covalent interactions as identified in the 3D structures of the thermo-gated capsaicin receptor TRPV1 could form a systemic fluidic grid-like mesh network with topological grids constrained as the thermo-rings to govern heat-sensing. The results showed that the heat-evoked melting of the biggest grid initiated a matched temperature threshold to release the lipid from the active vanilloid site for channel activation. Meanwhile, smaller grids were required to stabilize heat efficacy. Altogether, the change in the total grid sizes upon the change in the total noncovalent interactions along the lipid-dependent gating pathway was necessary for the matched temperature sensitivity. Therefore, this grid thermodynamic model may be broadly significant for the structural thermostability and the functional thermoactivity of bio-macromolecules.

Optical manipulation of neuronal mitochondria using scanning optical tweezers

Mitochondria play a crucial role in the physiological functions and energy metabolism of neurons, which can help in the understanding of complex biochemical reactions associated with various neurodegenerative diseases. Neurons, being highly differentiated terminal cells, require a greater number of mitochondria than ordinary cells to generate significant amounts of ATP, which is necessary for the growth of differentiated neuronal structures like axons and dendrites and the transmission of electrical signals along neuronal axons. Advancements in imaging technology, electrophysiology, and fluorescence targeting labeling have facilitated the study of mitochondrial movements in neurons and axons. However, disordered mitochondrial movements can hinder their analysis and characterization. Thus, it becomes necessary to artificially control their transport. Here, we demonstrate the utilization of scanning optical tweezers (SOTs) on the stable trapping and precise transport of soma or axon of neurons and enable. The presented method provides an optical approach to the control of mitochondria or other organelles in complex and variable biological environment.

Quantifying Information of Dynamical Biochemical Reaction Networks.

A large number of complex biochemical reaction networks are included in the gene expression, cell development, and cell differentiation of in vivo cells, among other processes. Biochemical reaction-underlying processes are the ones transmitting information from cellular internal or external signaling. However, how this information is measured remains an open question. In this paper, we apply the method of information length, based on the combination of Fisher information and information geometry, to study linear and nonlinear biochemical reaction chains, respectively. Through a lot of random simulations, we find that the amount of information does not always increase with the length of the linear reaction chain; instead, the amount of information varies significantly when this length is not very large. When the length of the linear reaction chain reaches a certain value, the amount of information hardly changes. For nonlinear reaction chains, the amount of information changes not only with the length of this chain, but also with reaction coefficients and rates, and this amount also increases with the length of the nonlinear reaction chain. Our results will help to understand the role of the biochemical reaction networks in cells.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

Bioempowerment of Therapeutic Living Cells by Single‐Cell Surface Engineering

AbstractLiving cells have the irreplaceable capability to achieve a wide range of complex biochemical reactions precisely and efficiently, which makes them attractive materials for therapeutic applications. In lieu of the traditional biochemical and biological approaches primarily focused on the augmentation of the innate functions of cells, there has been appreciable progress in the development of engineered therapeutic cells, mainly based on the chemical modifications of cell surfaces, at the single‐cell level, which empowers individual living cells with designed therapeutic functions in a cytocompatible manner. This review highlights the latest advances in the development of therapeutic living cells using single‐cell surface engineering, for potential applications in blood transfusion, drug delivery, cancer therapy, probiotic therapy, and tissue engineering and regenerative medicine. The methodological strategies for functionalizing cell surfaces with biomolecules, and inorganic and organic materials, to endow living cells with extrinsic physicochemical and biological properties as well as to increase the durability and efficacy of engineered therapeutic cells, are also briefly overviewed. The review ends with a perspective that discusses the construction of active cell‐in‐shell nanobiohybrid systems, in which exogenous materials formed on cell surfaces mutually and intimately communicate with the cells inside, as a future research direction for single‐cell surface engineering.

A multifunctional monolithic interfacial sensor based on gold nanoparticle

The spatial and temporal uneven distribution of complex biochemical reactions creates the diversity of biological systems. And the microenvironment confers fine regulation of these reactions, a stunning example of which is liquid-liquid phase separation (LLPS). LLPS can form a separate compartment without the physical separation formed by conventional membrane structures, and the reactions within the interface have specific reaction dynamics. Inspired by this, we report an interfacial sensor based on gold nanoparticles showing that interfacial factors have similar properties operating in natural biological environments and sensors. It repels molecules outside the interface and adjusts the DNA conformation within the interface to produce unique dynamics. The sensor adopts a modular design, allowing functional modules assembled on a single nanoparticle to avoid complex designs. We demonstrate the functionality of logical operations, using apurinic/apyrimidinic endonuclease 1 and micro RNA as inputs, showing that the sensor has the ability and potential to become a multifunctional platform with clear interface nature.

Evaluation of copper-induced biomolecular changes in different porin mutants of Escherichia coli W3110 by infrared spectroscopy.

Copper (Cu), one of the heavy metals, plays a vital role in many complex biochemical reactions as a trace element. However, it often becomes toxic when its concentration in the cell exceeds a certain level. Homeostasis of metals in the cell is primarily related to regulating metal transport into and out of the cell. Therefore, it is thought that porin proteins, which have a role in membrane permeability, may also play a role in developing Cu resistance. This study identified the differences between the molecular profiles of wild-type Escherichia coli W3110 and its seven different porin mutants exposed to Cu ions using attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy. The results showed that the absence of porin genes elicits global changes in the structure and composition of membrane lipids and proteins, in both the absence and presence of Cu. The lack of porin genes significantly elevated the amounts of fatty acids and phospholipids. When the alterations in protein secondary structures were compared, the quantity of amide I proteins was diminished by the presence of Cu. However, the amount of amide II proteins increased in porin mutant groups independent of Cu presence or absence. The DNAs are transformed from B- and Z-form to A-form due to porin mutations and the presence of Cu ions. The lack of porin genes increased polysaccharide content independent of Cu presence. This study can help characterize Cu detoxification efficiency and guide for obtaining active living cells to be used in bioremediation.

Integrating metabolomics and metatranscriptomics to explore the formation pathway of aroma-active volatile phenolics and metabolic profile during industrial radish paocai fermentation

The aroma profile of industrial Sichuan paocai is formed and regulated by complex physiological and biochemical reactions and microbial metabolism, but little is known so far. In this study, we comprehensively analyzed the changes of metabolic profile and gene expression profile, mainly explored the formation pathways of two skeleton aroma-active compounds, 4-ethylphenol and 4-ethylguaiacol, and verified the pathways at multiple levels. The results showed that a total of 136 volatile metabolites and 560 non-volatile metabolites were identified in the whole fermentation process. The types and concentrations of metabolites in paocai were higher than those in brine, and gradually converged with fermentation. Differential analysis of metabolism and transcription levels were both enriched in three pathways: amino acid metabolism, phenylpropanoid metabolism and lipid metabolism. Among them, 4-ethylphenol and 4-ethylguaiacol, the products of the phenylpropanoid metabolism, were converted from p-coumaric acid and ferulic acid in plant cell walls, respectively. Under the action of decarboxylase produced by yeast (such as Debaryomyces Hansenii) and lactic acid bacteria (such as Lactobacillus versmoldensis), intermediate metabolites vinylphenols were produced, and the intermediate metabolites further produce the final products under the action of vinylphenol reductase. The key gene copy number, enzyme activity, and metabolite concentration in the pathways were detected to provide stronger evidence for the formation pathways. This study provided meaningful new insights for the development of aroma-producing enzymes and further guidance for the flavor improvement of industrial paocai.

Self-organizing modeling and control of activated sludge process based on fuzzy neural network

The wastewater treatment process contains multiple complex biochemical reactions featured by strong nonlinear and time-varying dynamics due to the built-in discontinuity and uncertainty. Herein, a self-organizing fuzzy neural network with an efficient scheme for parsimonious (SOFNN-ESP) was orchestrated to improve the self-organizing modeling of municipal wastewater treatment process by combing predictive algorithms to deal with the complex water treatment procedure. The SOFNN-ESP algorithm could identify sewage treatment plants by a high-throughput parameter screening system and recursive least square method in real-time, which provided dynamic setting feedback and promoted water quality. The integration of the SOFNN-ESP algorithm and a model predictive control (MPC) further improved the accuracy in water quality controlling via immediately adjusting weight parameters of the network. This gradient algorithm also realized the online dynamic tracking of dissolved oxygen and nitrate nitrogen level by simultaneous tracking of multiple performance indicators and optimizing setting values of the control variable. SOFNN-ESP-MPC gave an error of <5 % when the peak error of proportional integral differential controller was >10 %. Concerning the benchmark simulation model No.1 of municipal wastewater treatment, the SOFNN-ESP-MPC method exhibited a compact network structure and outstanding generalization performance. The self-organizing modeling and predictive control strategy proposed in this study could effectively improve the prediction accuracy and control efficiency of the activated sludge process model, which is of great significance for the efficiency improvement of sewage treatment process.