Cancer cells metabolize a large fraction of glucose to lactate, even under a sufficient oxygen supply. This phenomenon-the \"Warburg Effect\"-is often regarded as not yet understood. Cancer cells change gene expression to increase the uptake and utilization of glucose for biosynthesis pathways and glycolysis, but they do not adequately up-regulate the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS). Thereby, an increased glycolytic flux causes an increased production of cytosolic NADH. However, since the corresponding gene expression changes are not neatly fine-tuned in the cancer cells, cytosolic NAD+ must often be regenerated by loading excess electrons onto pyruvate and secreting the resulting lactate, even under sufficient oxygen supply. Interestingly, the Michaelis constants (KM values) of the enzymes at the pyruvate junction are sufficient to explain the priorities for pyruvate utilization in cancer cells: 1. mitochondrial OXPHOS for efficient ATP production, 2. electrons that exceed OXPHOS capacity need to be disposed of and secreted as lactate, and 3. biosynthesis reactions for cancer cell growth. In other words, a number of cytosolic electrons need to take the \"emergency exit\" from the cell by lactate secretion to maintain the cytosolic redox balance.

Amino acid depletion therapy is a promising approach for cancer treatment. It exploits the differences in the metabolic processes between healthy and cancerous cells. Certain microbial enzymes induce cancer cell apoptosis by removing essential amino acids. L-asparaginase is an enzyme approved by the FDA for the treatment of acute lymphoblastic leukemia. The enzymes currently employed in clinics come from two different sources: Escherichia coli and Erwinia chrysanthemi. Nevertheless, the search for improved enzymes and other sources continues because of several factors, including immunogenicity, in vivo instability, and protease degradation. Before determining whether L-asparaginase is clinically useful, research should consider the Michaelis constant, turnover number, and maximal velocity. The identification of L-asparaginase from microbial sources has been the subject of various studies. The primary goals of this review are to explore the most current approaches used in the search for therapeutically useful L-asparaginases and to establish whether these investigations identified the crucial characteristics of L-asparaginases before declaring their therapeutic potential.

Michaelis constant (KM) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of KM are difficult and time-consuming, prediction of the KM values from machine and deep learning models would increase the pace of the enzymes kinetics studies. Existing machine and deep learning models are limited to the specific enzymes, i.e., a minority of enzymes or wildtype enzymes. Here, we used a deep learning framework PaddlePaddle to implement a machine and deep learning approach (GraphKM) for KM prediction of wildtype and mutant enzymes. GraphKM is composed by graph neural networks (GNN), fully connected layers and gradient boosting framework. We represented the substrates through molecular graph and the enzymes through a pretrained transformer-based language model to construct the model inputs. We compared the difference of the model results made by the different GNN (GIN, GAT, GCN, and GAT-GCN). The GAT-GCN-based model generally outperformed. To evaluate the prediction performance of the GraphKM and other reported KM prediction models, we collected an independent KM dataset (HXKm) from literatures.

Aim: Investigation of the pharmacokinetics of sorafenib (SRF) in rats with hepatocellular carcinoma (HCC). Methods: A reproducible ultra-HPLC-MS method for simultaneous determination of serum SRF, N-hydroxymethyl sorafenib and N-demethylation sorafenib. Results: Both the maximum serum concentrations (2.5-times) and the area under the serum concentration-time curve from 0 h to infinity (4.5-times) of SRF were observed to be significantly higher, with a greater than 3.0-fold decrease in the clearance rate in the HCC-bearing rats compared with these values in healthy animals. Further study revealed approximately 3.8- and 3.2-times increases in the apparent Michaelis constant for N-hydroxymethyl sorafenib and N-demethylation sorafenib conversions in the HCC-bearing rats. Conclusion: The low efficiency for the SRF conversions was a key contributor to the increased serum concentrations of SRF.
[Box: see text].

BACKGROUND: Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem).
RESULTS: To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values.
CONCLUSIONS: MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.

Nanozymes are particles with diameters in the range of 1-100 nm, which has been widely studied due to their biological enzyme-like properties and stability that natural enzymes do not have. In this study, several reducing agents with different structures (catechol (Cc), hydroquinone (Hq), resorcinol (Rs), vitamin C (Vc), pyrogallic acid (Ga), sodium citrate (Sc), sodium malate (Sm), and sodium tartrate (St)) were used to prepare colloidal gold with a negative charge and similar particle size by controlling the temperature and pH. The affinity analysis of the substrate H2O2 and TMB showed that the order of activities of colloidal gold Nanozymes prepared by different reducing agents was Cc, Hq, Rs, Vc, Ga, Sc, Sm, St. It was also found that the enzyme activity of colloidal gold reduced by benzene rings is higher than that of the colloidal gold enzyme reduced by linear chains. Finally, we discussed the activity of the colloidal gold peroxidase based on the number and position of isomers and functional groups; and demonstrated that the nanozymes activity is affected by the surface activity of colloidal gold, the elimination of hydroxyl radicals and the TMB binding efficiency.

Knowledge of the Michaelis-Menten parameters and their meaning in different circumstances is an essential prerequisite to understanding enzyme function and behaviour. The published literature contains an abundance of values reported for many enzymes. The problem concerns assessing the appropriateness and validity of such material for the purpose to which it is to be applied. This review considers the evaluation of such data with particular emphasis on the assessment of its fitness for purpose.

Four phenylacetaldehyde dehydrogenases (designated as FeaB or StyD) originating from styrene-degrading soil bacteria were biochemically investigated. In this study, we focused on the Michaelis-Menten kinetics towards the presumed native substrate phenylacetaldehyde and the obviously preferred co-substrate NAD+. Furthermore, the substrate specificity on four substituted phenylacetaldehydes and the co-substrate preference were studied. Moreover, these enzymes were characterized with respect to their temperature as well as long-term stability. Since aldehyde dehydrogenases are known to show often dehydrogenase as well as esterase activity, we tested this capacity, too. Almost all results showed clearly different characteristics between the FeaB and StyD enzymes. Furthermore, FeaB from Sphingopyxis fribergensis Kp5.2 turned out to be the most active enzyme with an apparent specific activity of 17.8 ± 2.1 U mg-1. Compared with that, both StyDs showed only activities less than 0.2 U mg-1 except the overwhelming esterase activity of StyD-CWB2 (1.4 ± 0.1 U mg-1). The clustering of both FeaB and StyD enzymes with respect to their characteristics could also be mirrored in the phylogenetic analysis of twelve dehydrogenases originating from different soil bacteria.

The upper bound of enzyme concentration for accurately estimating the parameters in Michaelis-Menten (MM) equation is not completely determined and still under discussion, even though many researchers have investigated the equation\'s validity for a long time. In the paper, we broadly investigated the correlation between the system of ordinary differential equations for monosubstrate irreversible enzyme reaction (HMM-system) and its derivative MM equation focusing on the relationship between initial enzyme concentration [E]0 and Michaelis constant Km by numerical simulation. According to the results, the initial reaction velocity v0 is still a function of initial substrate concentration [S]0 at [E]0

Thermodynamics and kinetics of biochemical reactions depend not only on temperature, but also on pressure and on the presence of cosolvents in the reaction medium. Understanding their effects on biochemical processes is a crucial step towards the design and optimization of industrially relevant enzymatic reactions. Such reactions typically do not take place in pure water. Cosolvents might be present as they are either required as stabilizer, as solubilizer, or in their function to overcome thermodynamic or kinetic limitations. Further, a vast number of enzymes has been found to be piezophilic or at least pressure-tolerant, meaning that nature has adapted them to high-pressure conditions. In this manuscript, we review existing data and we additionally present some new data on the combined cosolvent and pressure influence on the kinetics of biochemical reactions. In particular, we focus on cosolvent and pressure effects on Michaelis constants and catalytic constants of α-CT-catalysed peptide hydrolysis reactions. Two different substrates were considered in this work, N-succinyl-L-phenylalanine-p-nitroanilide and H-phenylalanine-p-nitroanilide. Urea, trimethyl-N-amine oxide, and dimethyl sulfoxide have been under investigation as these cosolvents are often applied in technical as well as in demonstrator systems. Pressure effects have been studied from ambient pressure up to 2 kbar. The existing literature data and the new data show that pressure and cosolvents must not be treated as independent effects. Non-additive interactions on a molecular level lead to a partially compensatory effect of cosolvents and pressure on the kinetic parameters of the hydrolysis reactions considered.