A guide to the Michaelis-Menten equation: steady state and beyond

Identification of procyanidins as α-glucosidase inhibitors, pancreatic lipase inhibitors, and antioxidants from the bark of Cinnamomum cassia by multi-bioactivity-labeled molecular networking

This study examined the suppressive effects of 16 selected plant-based foods on α-glucosidase and pancreatic lipase and their antioxidant properties. Among these, the bark of Cinnamomum cassia (Cinnamon, WLN-FM 15) showed the highest inhibitory activity against α-glucosidase and the highest antioxidant activity. Additionally, WLN-FM 15 showed promising results in the other tests. To further identify the bioactive constituents of WLN-FM 15, a multi-bioactivity-labeled molecular networking approach was used through a combination of GNPS-based molecular networking, DPPH-HPLC, and affinity-based ultrafiltration-HPLC. A total of nine procyanidins were identified as antioxidants and inhibitors of α-glucosidase and pancreatic lipase in WLN-FM 15. Subsequently, procyanidins A1, A2, B1, and C1 were isolated, and their efficacy was confirmed through functional assays. In summary, WLN-FM 15 has the potential to serve as a functional food ingredient with the procyanidins as its bioactive constituents. These results also suggest that the multi-bioactivity-labeled molecular networking approach is reliable for identifying bioactive constituents in plant-based foods.

Dual-mode optical biocompatible-sensor enabled by enzyme-like activity of UiO-66 (Zr) for ultra-sensitive ROS detection in vitro

The simple, effective and highly sensitive detection of hydrogen peroxide (H2O2), which belongs to the reactive oxygen species (ROS), at low concentrations plays an indispensable role in the field of environmental protection, biological research and safety. In this study, a dual-mode optical biosensor, UiO-66@OPD, was developed based on the inherent peroxidase mimicking activity of UiO-66 (Zr) and the optical reaction of ortho-phenylenediamine (OPD) by extending the π-system through oxidative coupling, prototropism and elimination to form OPDox, thereby exhibiting strong orangish absorbance and greenish fluorescence. The catalase-mimicking activity of UiO-66 (Zr) was demonstrated by the catalytic oxidation of methylene blue in the presence of H2O2. Moreover, the Michaelis-Menten kinetic model confirmed the intrinsic peroxidase-like activity of UiO-66@OPD as a modified MOFzyme. The synthesized UiO-66 (Zr) facilitated the oxidation of OPD to OPDox by degrading H2O2 to the hydroxyl radicals. During the oxidation process, the absorption peak at 415 nm and the fluorescence peak at 565 nm of the synthesized probe were significantly enhanced by increasing the H2O2 concentration. Moreover, a colorimetric and fluorometric ultrasensitive sensor shows a good linear relationship between the intensity enhancement and H2O2 concentration in the range of 0-600 nM for absorption and fluorescence spectra with R2 = 0.9772, and R2 = 0.9948, respectively. To demonstrate the biological performance and biocompatibility of UiO-66@OPD as a biosensor, MTT evaluation was performed for the three cell lines MCF-10 A, HEK293 and A549, indicating high biocompatibility and good cell viability for biological applications. Ultimately, this convenient, environmentally friendly, biocompatible and cost-effective catalase-mimicking-based sensor system will open a new perspective for the development of portable kite-based biosensors In vitro.

Azobenzene-Based Photoswitchable Substrates for Advanced Mechanistic Studies of Model Haloalkane Dehalogenase Enzyme Family

The engineering of efficient enzymes for large-scale production of industrially relevant compounds is a challenging task. Utilizing rational protein design, which relies on a comprehensive understanding of mechanistic information, holds significant promise for achieving success in this endeavor. Pre-steady-state kinetic measurements, obtained either through fast-mixing techniques or photoswitchable substrates, provide crucial mechanistic insights. The latter approach not only furnishes mechanistic clarity but also affords real-time structural elucidation of reaction intermediates via time-resolved femtosecond crystallography. Unfortunately, only a limited number of such valuable mechanistic probes are available. To address this gap, we applied a multidisciplinary approach, including computational analysis, chemical synthesis, physicochemical property screening, and enzyme kinetics to identify promising candidates for photoswitchable probes. We demonstrate the approach by designing an azobenzene-based photoswitchable substrate tailored for haloalkane dehalogenases, a prototypic class of enzymes pivotal in developing computational tools for rational protein design. The probe was subjected to steady-state and pre-steady-state kinetic analysis, which revealed new insights about the catalytic behavior of the model biocatalysts. We employed laser-triggered Z-to-E azobenzene photoswitching to generate the productive isomer in situ, opening avenues for advanced mechanistic studies using time-resolved femtosecond crystallography. Our results not only pave the way for the mechanistic understanding of this model enzyme family, incorporating both kinetic and structural dimensions, but also propose a systematic approach to the rational design of photoswitchable enzymatic substrates.

Revisiting Structure-activity Relationships: Unleashing the potential of selective Janus kinase 1 inhibitors

Janus kinases (JAKs), a kind of non-receptor tyrosine kinases, the function has been implicated in the regulation of cell proliferation, differentiation and apoptosis, immune, inflammatory response and malignancies. Among them, JAK1 represents an essential target for modulating cytokines involved in inflammation and immune function. Rheumatoid arthritis, atopic dermatitis, ulcerative colitis and psoriatic arthritis are areas where approved JAK1 drugs have been applied for the treatment. In the review, we provided a brief introduction to JAK1 inhibitors in market and clinical trials. The structures of high active JAK1 compounds (IC50 ≤ 0.1 nM) were highlighted, with primary focus on structure-activity relationship and selectivity. Moreover, the druggability processes of approved drugs and high active compounds were analyzed. In addition, the issues involved in JAK1 compounds clinical application as well as strategies to surmount these challenges, were discussed.

Construction of Metal-Organic Framework-Based Heterogeneous Pepsin and Its Degradation Performance and Mechanism for Phthalic Acid Esters

Biological enzyme-driven degradation of environmental pollutants has attracted widespread attention because it is ecofriendly and highly efficient. Immobilized enzyme technology has emerged as a promising technique in enzymology that addresses the limitations associated with free enzymes. Traditional solid-loaded enzyme substrates are often affected by blockages and restricted substrate accessibility. In this study, we synthesized an efficient heterogeneous pepsin catalyst, named PEP@M-MIL100(Fe), by covalently combining carboxylated ferrite structural expanded metal-organic frameworks with pepsin. This catalyst demonstrated excellent environmental adaptability and remarkable catalytic degradation capabilities. Notably, it rapidly degraded the persistent microplastic pollutant diisononyl phthalate (DINP) within just 150 min, with a removal efficiency of up to 95.88%. Impressively, even after 10 consecutive uses, the catalyst maintained its high performance. We proposed an innovative steady-state heterogeneous enzyme-catalyzed degradation mechanism, i.e., diffusion (D)-absorption (A)-binding (B)-reaction (R)-degradation (D)-link mechanism, which emphasizes the influence of substrate diffusion rates in this process. This work presents the first successful application of pepsin to DINP degradation and offers a sustainable and effective approach for addressing contemporary pollution challenges.

Human risk assessment through development and application of a physiologically based toxicokinetic model for 4-tert-octylphenol

4-tert-octylphenol (4-tert-OP) is an ecologically hazardous substance, and exposure to it in the environment has been consistently reported in the past. Despite the hazards and widespread exposure to 4-tert-OP, tools for scientific assessment of 4-tert-OP exposure risk level in humans are lacking. The main purpose of this study was to develop a physiologically-based-toxicokinetic (PBTK) model for 4-tert-OP and to perform quantitative risk assessment of 4-tert-OP in various population groups using the established model. Based on the results of toxicokinetic experiments on male rats, the PBTK model for 4-tert-OP was established and verified, and this was converted to a model for humans through interspecies extrapolation. Based on the previously reported no-observed-adverse-effect-levels for rats, it was possible to estimate the 4-tert-OP reference dose in humans through reverse dosimetry using the model. Biomonitoring data derived from various population groups were applied to the human PBTK model to calculate external exposures and margin of safety for 4-tert-OP for each population group. The PBTK model established in this study adequately explained the toxicokinetic experimental values at acceptable levels and was able to quantitatively predict the 4-tert-OP exposure level in the testes related to male reproductive toxicity. In addition, the degree of external exposure to 4-tert-OP could be scientifically estimated based on biomonitoring values derived from various biological matrices. The reference doses for systemic and reproductive toxicity caused by 4-tert-OP in male humans were calculated to be 0.16 and 1.12 mg/kg/day, respectively. The mean external exposure to 4-tert-OP in each population group estimated based on plasma and urine biomonitoring data was 0.04-66.24 mg/kg/day, showing very large exposure diversity between groups. Exposure risks to 4-tert-OP in populations ranged from safe to risky, suggesting the need for continued monitoring and risk management of 4-tert-OP worldwide. This study provides valuable scientific insight regarding the 4-tert-OP human risk assessment.

Mechanistic insights into the conversion of flavin adenine dinucleotide (FAD) to 8-formyl FAD in formate oxidase: a combined experimental and in-silico study

Formate oxidase (FOx), which contains 8-formyl flavin adenine dinucleotide (FAD), exhibits a distinct advantage in utilizing ambient oxygen molecules for the oxidation of formic acid compared to other glucose-methanol-choline (GMC) oxidoreductase enzymes that contain only the standard FAD cofactor. The FOx-mediated conversion of FAD to 8-formyl FAD results in an approximate 10-fold increase in formate oxidase activity. However, the mechanistic details underlying the autocatalytic formation of 8-formyl FAD are still not well understood, which impedes further utilization of FOx. In this study, we employ molecular dynamics simulation, QM/MM umbrella sampling simulation, enzyme activity assay, site-directed mutagenesis, and spectroscopic analysis to elucidate the oxidation mechanism of FAD to 8-formyl FAD. Our results reveal that a catalytic water molecule, rather than any catalytic amino acids, serves as a general base to deprotonate the C8 methyl group on FAD, thus facilitating the formation of a quinone-methide tautomer intermediate. An oxygen molecule subsequently oxidizes this intermediate, resulting in a C8 methyl hydroperoxide anion that is protonated and dissociated to form OHC-RP and OH-. During the oxidation of FAD to 8-formyl FAD, the energy barrier for the rate-limiting step is calculated to be 22.8 kcal/mol, which corresponds to the required 14-hour transformation time observed experimentally. Further, the elucidated oxidation mechanism reveals that the autocatalytic formation of 8-formyl FAD depends on the proximal arginine and serine residues, R87 and S94, respectively. Enzymatic activity assay validates that the mutation of R87 to lysine reduces the kcat value to 75% of the wild-type, while the mutation to histidine results in a complete loss of activity. Similarly, the mutant S94I also leads to the deactivation of enzyme. This dependency arises because the nucleophilic OH- group and the quinone-methide tautomer intermediate are stabilized through the noncovalent interaction provided by R87 and S94. These findings not only explain the mechanistic details of each reaction step but also clarify the functional role of R87 and S94 during the oxidative maturation of 8-formyl FAD, thereby providing crucial theoretical support for the development of novel flavoenzymes with enhanced redox properties.

Parkinson's disease-associated mutations in α-synuclein alters its lipid-bound state

Lipid-binding properties of α-synuclein play a central role in protein aggregation and progression of Parkinson's disease (PD). α-Synuclein, an intrinsically disordered protein, binds to lipid membranes through the formation of two amphipathic helices that insert into the lipid bilayer. All disease-associated single point mutations have been identified to be within these helical regions of α-synuclein: V15A, A30P, E46K, H50Q, G51D, A53T, and A53V. However, the effects of these mutations on the bound states of the two α helices of the protein have yet to be fully characterized. In this report, we use a tryptophan fluorescence assay to measure the binding of the α helices of these PD-associated mutants to lipid membranes within the lipid-depletion regime. We characterize the binding behavior of each helix, revealing that, generally, the PD-associated mutants shift the equilibrium bound state away from the N-terminal helix of the protein toward helix 2 at lower lipid concentrations. Altogether, our results indicate that disruption to the equilibrium binding of the two α helices of α-synuclein could play a role in PD progression.

CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes

BACKGROUND

There exists a critical transition or tipping point during the complex biological process. Such critical transition is usually accompanied by the catastrophic consequences. Therefore, hunting for the tipping point or critical state is of significant importance to prevent or delay the occurrence of catastrophic consequences. However, predicting critical state based on the high-dimensional small sample data is a difficult problem, especially for single-cell expression data.

RESULTS

In this study, we propose the comprehensive neighbourhood-based perturbed mutual information (CPMI) method to detect the critical states of complex biological processes. The CPMI method takes into account the relationship between genes and neighbours, so as to reduce the noise and enhance the robustness. This method is applied to a simulated dataset and six real datasets, including an influenza dataset, two single-cell expression datasets and three bulk datasets. The method can not only successfully detect the tipping points, but also identify their dynamic network biomarkers (DNBs). In addition, the discovery of transcription factors (TFs) which can regulate DNB genes and nondifferential 'dark genes' validates the effectiveness of our method. The numerical simulation verifies that the CPMI method is robust under different noise strengths and is superior to the existing methods on identifying the critical states.

CONCLUSIONS

In conclusion, we propose a robust computational method, i.e., CPMI, which is applicable in both the bulk and single cell datasets. The CPMI method holds great potential in providing the early warning signals for complex biological processes and enabling early disease diagnosis.

CYP2J2-mediated metabolism of arachidonic acid in heart: A review of its kinetics, inhibition and role in heart rhythm control

Cytochrome P450 2 J2 (CYP2J2) is primarily expressed extrahepatically and is the predominant epoxygenase in human cardiac tissues. This highlights its key role in the metabolism of endogenous substrates. Significant scientific interest lies in cardiac CYP2J2 metabolism of arachidonic acid (AA), an omega-6 polyunsaturated fatty acid, to regioisomeric bioactive epoxyeicosatrienoic acid (EET) metabolites that show cardioprotective effects including regulation of cardiac electrophysiology. From an in vitro perspective, the accurate characterization of the kinetics of CYP2J2 metabolism of AA including its inhibition and inactivation by drugs could be useful in facilitating in vitro-in vivo extrapolations to predict drug-AA interactions in drug discovery and development. In this review, background information on the structure, regulation and expression of CYP2J2 in human heart is presented alongside AA and EETs as its endogenous substrate and metabolites. The in vitro and in vivo implications of the kinetics of this endogenous metabolic pathway as well as its perturbation via inhibition and inactivation by drugs are elaborated. Additionally, the role of CYP2J2-mediated metabolism of AA to EETs in cardiac electrophysiology will be expounded.

Design and Evaluation of a Robust CRISPR Kinetic Assay for Hot-Spot Genotyping

Next-generation sequencing offers highly multiplexed and accurate detection of nucleic acid sequences but at the expense of complex workflows and high input requirements. The ease of use of CRISPR-Cas12 assays is attractive and may enable highly accurate detection of sequences implicated in, for example, cancer pathogenic variants. CRISPR assays often employ end-point measurements of Cas12 trans-cleavage activity after Cas12 activation by the target; however, end point-based methods can be limited in accuracy and robustness by arbitrary experimental choices. To overcome such limitations, we develop and demonstrate here an accurate assay targeting a mutation of the epidermal growth factor gene implicated in lung cancer (exon 19 deletion). The assay is based on characterizing the kinetics of Cas12 trans-cleavage to discriminate the mutant from wild-type targets. We performed extensive experiments (780 reactions) to calibrate key assay design parameters, including the guide RNA sequence, reporter sequence, reporter concentration, enzyme concentration, and DNA target type. Interestingly, we observed a competitive reaction between the target and reporter molecules that has important consequences for the design of CRISPR assays, which use preamplification to improve sensitivity. Finally, we demonstrate the assay on 18 tumor-extracted amplicons and 100 training iterations with 99% accuracy and discuss discrimination parameters and models to improve wild type versus mutant classification.

Monomeric Esterase: Insights into Cooperative Behavior, Hysteresis/Allokairy

Herein, we present a novel esterase enzyme, Ade1, isolated from a metagenomic library of Amazonian dark earths soils, demonstrating its broad substrate promiscuity by hydrolyzing ester bonds linked to aliphatic groups. The three-dimensional structure of the enzyme was solved in the presence and absence of substrate (tributyrin), revealing its classification within the α/β-hydrolase superfamily. Despite being a monomeric enzyme, enzymatic assays reveal a cooperative behavior with a sigmoidal profile (initial velocities vs substrate concentrations). Our investigation brings to light the allokairy/hysteresis behavior of Ade1, as evidenced by a transient burst profile during the hydrolysis of substrates such as p-nitrophenyl butyrate and p-nitrophenyl octanoate. Crystal structures of Ade1, coupled with molecular dynamics simulations, unveil the existence of multiple conformational structures within a single molecular state (E̅1). Notably, substrate binding induces a loop closure that traps the substrate in the catalytic site. Upon product release, the cap domain opens simultaneously with structural changes, transitioning the enzyme to a new molecular state (E̅2). This study advances our understanding of hysteresis/allokairy mechanisms, a temporal regulation that appears more pervasive than previously acknowledged and extends its presence to metabolic enzymes. These findings also hold potential implications for addressing human diseases associated with metabolic dysregulation.

Engineering bio-brick protein scaffolds for organizing enzyme assemblies

Enzyme scaffolding is an emerging approach for enhancing the catalytic efficiency of multi-enzymatic cascades by controlling their spatial organization and stoichiometry. This study introduces a novel family of engineered SCAffolding Bricks, named SCABs, utilizing the consensus tetratricopeptide repeat (CTPR) domain for organized multi-enzyme systems. Two SCAB systems are developed, one employing head-to-tail interactions with reversible covalent disulfide bonds, the other relying on non-covalent metal-driven assembly via engineered metal coordinating interfaces. Enzymes are directly fused to SCAB modules, triggering assembly in a non-reducing environment or by metal presence. A proof-of-concept with formate dehydrogenase (FDH) and L-alanine dehydrogenase (AlaDH) shows enhanced specific productivity by 3.6-fold compared to free enzymes, with the covalent stapling outperforming the metal-driven assembly. This enhancement likely stems from higher-order supramolecular assembly and improved NADH cofactor regeneration, resulting in more efficient cascades. This study underscores the potential of protein engineering to tailor scaffolds, leveraging supramolecular spatial-organizing tools, for more efficient enzymatic cascade reactions.

X-ray structure and enzymatic study of a bacterial NADPH oxidase highlight the activation mechanism of eukaryotic NOX

NADPH oxidases (NOX) are transmembrane proteins, widely spread in eukaryotes and prokaryotes, that produce reactive oxygen species (ROS). Eukaryotes use the ROS products for innate immune defense and signaling in critical (patho)physiological processes. Despite the recent structures of human NOX isoforms, the activation of electron transfer remains incompletely understood. SpNOX, a homolog from Streptococcus pneumoniae, can serves as a robust model for exploring electron transfers in the NOX family thanks to its constitutive activity. Crystal structures of SpNOX full-length and dehydrogenase (DH) domain constructs are revealed here. The isolated DH domain acts as a flavin reductase, and both constructs use either NADPH or NADH as substrate. Our findings suggest that hydride transfer from NAD(P)H to FAD is the rate-limiting step in electron transfer. We identify significance of F397 in nicotinamide access to flavin isoalloxazine and confirm flavin binding contributions from both DH and Transmembrane (TM) domains. Comparison with related enzymes suggests that distal access to heme may influence the final electron acceptor, while the relative position of DH and TM does not necessarily correlate with activity, contrary to previous suggestions. It rather suggests requirement of an internal rearrangement, within the DH domain, to switch from a resting to an active state. Thus, SpNOX appears to be a good model of active NOX2, which allows us to propose an explanation for NOX2's requirement for activation.

Development of an Innovative Biosensor Based on Graphene/PEDOT/Tyrosinase for the Detection of Phenolic Compounds in River Waters

Phenolic compounds, originating from industrial, agricultural, and urban sources, can leach into flowing waters, adversely affecting aquatic life, biodiversity, and compromising the quality of drinking water, posing potential health hazards to humans. Thus, monitoring and mitigating the presence of phenolic compounds in flowing waters are essential for preserving ecosystem integrity and safeguarding public health. This study explores the development and performance of an innovative sensor based on screen-printed electrode (SPE) modified with graphene (GPH), poly(3,4-ethylenedioxythiophene) (PEDOT), and tyrosinase (Ty), designed for water analysis, focusing on the manufacturing process and the obtained electroanalytical results. The proposed biosensor (SPE/GPH/PEDOT/Ty) was designed to achieve a high level of precision and sensitivity, as well as to allow efficient analytical recoveries. Special attention was given to the manufacturing process and optimization of the modifying elements' composition. This study highlights the potential of the biosensor as an efficient and reliable solution for water analysis. Modification with graphene, the synthesis and electropolymerization deposition of the PEDOT polymer, and tyrosinase immobilization contributed to obtaining a high-performance and robust biosensor, presenting promising perspectives in monitoring the quality of the aquatic environment. Regarding the electroanalytical experimental results, the detection limits (LODs) obtained with this biosensor are extremely low for all phenolic compounds (8.63 × 10-10 M for catechol, 7.72 × 10-10 M for 3-methoxycatechol, and 9.56 × 10-10 M for 4-methylcatechol), emphasizing its ability to accurately measure even subtle variations in the trace compound parameters. The enhanced sensitivity of the biosensor facilitates detection and quantification in river water samples. Analytical recovery is also an essential aspect, and the biosensor presents consistent and reproducible results. This feature significantly improves the reliability and usefulness of the biosensor in practical applications, making it suitable for monitoring industrial or river water.

Nanohybrid nanozyme based colourimetric immunosensor for porcine gelatin

Enzyme mimicking nanomaterials, nanozymes, have gained considerable interest in the scientific community because of their superior properties compared to natural enzymes, including their high stability at extreme conditions, cheaper availability, and ease of synthesis. Herein, we report novel colloidal gold nanoparticles - graphene nanoplatelets - chitosan (CS) with peroxidase mimicking properties used to carry out highly sensitive and selective immunoassay for porcine gelatin detection. The interaction between anti-gelatin antibody conjugated nanozyme with porcine gelatin proteins produced an ultrasensitive immunoassay response in the form of a colourimetric signal directly proportional to the porcine gelatin protein concentration. The nanozyme produced a colourimetric response in the presence of its substrate, 3,3',5,5'-tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2), demonstrating its peroxidase mimicking properties. The results revealed that the nanozyme exhibited remarkable selectivity and sensitivity in the assay, detecting proteins at concentrations as low as 86.42 pg/mL. Additionally, the immunosensor demonstrated a broad linear detection range spanning from 200 pg/mL to 2 ng/mL.

Rationalizing the Influence of the Binding Affinity on the Activity of Phosphoserine Phosphatases

The Sabatier principle states that catalytic activity can be maximized when the substrate binding affinity is neither too strong nor too weak. Recent studies have shown that the activity of several hydrolases is maximized at intermediate values of the binding affinity (Michaelis-Menten constant: Km ). However, it remains unclear whether this concept of artificial catalysis is applicable to enzymes in general, especially for those which have evolved under different reaction environments. Herein, we show that the activity of phosphoserine phosphatase is also enhanced at an intermediate Km value of approximately 0.5 mM. Within our dataset, the variation of Km by three orders of magnitude accounted for a roughly 18-fold variation in the activity. Owing to the high phylogenetic and physiological diversity of our dataset, our results support the importance of optimizing Km for enzymes in general. On the other hand, a 77-fold variation in the activity was attributed to other physicochemical parameters, such as the Arrhenius prefactor of kcat , and could not be explained by the Sabatier principle. Therefore, while tuning the binding affinity according to the Sabatier principle is an important consideration, the Km value is only one of many physicochemical parameters which must be optimized to maximize enzymatic activity.

Replication efficiencies of human cytomegalovirus-infected epithelial cells are dependent on source of virus production

Human cytomegalovirus (HCMV) is a prevalent betaherpesvirus, and infection can lead to a range of symptomatology from mononucleosis to sepsis in immunocompromised individuals. HCMV is also the leading viral cause of congenital birth defects. Lytic replication is supported by many cell types with different kinetics and efficiencies leading to a plethora of pathologies. The goal of these studies was to elucidate HCMV replication efficiencies for viruses produced on different cell types upon infection of epithelial cells by combining experimental approaches with data-driven computational modeling. HCMV was generated from a common genetic background of TB40-BAC4, propagated on fibroblasts (TB40Fb) or epithelial cells (TB40Epi), and used to infect epithelial cells. We quantified cell-associated viral genomes (vDNA), protein levels (pUL44, pp28), and cell-free titers over time for each virus at different multiplicities of infection. We combined experimental quantification with data-driven simulations and determined that parameters describing vDNA synthesis were similar between sources. We found that pUL44 accumulation was higher in TB40Fb than TB40Epi. In contrast, pp28 accumulation was higher in TB40Epi which coincided with a significant increase in titer for TB40Epi over TB40Fb. These differences were most evident during live-cell imaging, which revealed syncytia-like formation during infection by TB40Epi. Simulations of the late lytic replication cycle yielded a larger synthesis constant for pp28 in TB40Epi along with increase in virus output despite similar rates of genome synthesis. By combining experimental and computational modeling approaches, our studies demonstrate that the cellular source of propagated virus impacts viral replication efficiency in target cell types.

Screening of oxidative behavior in catalytic amyloid assemblies

Once considered a thermodynamic minimum of the protein fold or as simply by-products of a misfolding process, amyloids are increasingly showing remarkable potential for promoting enzyme-like catalysis. Recent studies have demonstrated a diverse range of catalytic behaviors that amyloids can promote way beyond the hydrolytic behaviors originally reported. We and others have demonstrated the strong propensity of catalytic amyloids to facilitate redox reactions both in the presence and in the absence of metal cofactors. Here, we present a detailed protocol for measuring the oxidative ability of supramolecular peptide assemblies.

Discretised Flux Balance Analysis for Reaction-Diffusion Simulation of Single-Cell Metabolism

Metabolites have to diffuse within the sub-cellular compartments they occupy to specific locations where enzymes are, so reactions could occur. Conventional flux balance analysis (FBA), a method based on linear programming that is commonly used to model metabolism, implicitly assumes that all enzymatic reactions are not diffusion-limited though that may not always be the case. In this work, we have developed a spatial method that implements FBA on a grid-based system, to enable the exploration of diffusion effects on metabolism. Specifically, the method discretises a living cell into a two-dimensional grid, represents the metabolic reactions in each grid element as well as the diffusion of metabolites to and from neighbouring elements, and simulates the system as a single linear programming problem. We varied the number of rows and columns in the grid to simulate different cell shapes, and the method was able to capture diffusion effects at different shapes. We then used the method to simulate heterogeneous enzyme distribution, which suggested a theoretical effect on variability at the population level. We propose the use of this method, and its future extensions, to explore how spatiotemporal organisation of sub-cellular compartments and the molecules within could affect cell behaviour.

HetMM: A Michaelis-Menten model for non-homogeneous enzyme mixtures

The Michaelis-Menten model requires its reaction velocities to come from a preparation of homogeneous enzymes, with identical or near-identical catalytic activities. However, this condition is not always met. We introduce a kinetic model that relaxes this requirement, by assuming there are an unknown number of enzyme species drawn from a probability distribution whose standard deviation is estimated. Through simulation studies, we demonstrate the method accurately discriminates between homogeneous and heterogeneous data, even with moderate levels of experimental error. We applied this model to three homogeneous and three heterogeneous biological systems, showing that the standard and heterogeneous models outperform respectively. Lastly, we show that heterogeneity is not readily distinguished from negatively cooperative binding under the Hill model. These two distinct attributes-inequality in catalytic ability and interference between binding sites-yield similar Michaelis-Menten curves that are not readily resolved without further experimentation. Our user-friendly software package allows homogeneity testing and parameter estimation.

An electrochemical AChE-based biosensor for organophosphate pesticides using a modified CuNWs/rGO nanocomposite on a screen-printed carbon electrode

An enzymatic electrochemical biosensor was built for the indirect detection of organophosphates (OPs), based on acetylcholinesterase inhibition. The biosensor was fabricated for enhanced performance on a screen-printed carbon electrode (SPCE), modified with copper nanowires (CuNWs) composited with reduced graphene oxide (rGO). The oxidation current was measured using the cyclic voltammogram (CV) method, as generated by the enzymatic interaction between acetylcholinesterase (AChE) and its substrate, acetylthiocholine (ATCh). The biosensing response is the reduction in signal caused by the inhibition of acetylcholinesterase in the presence of an organophosphate inhibitor. Benchmarking shows that the CuNWs/rGO nanocomposite enhanced the signal current considerably and decreased the oxidation potential for electrochemical detection of the OP chlorpyrifos, exhibiting a wide linear detection rangefrom 10 µg/L-200 µg/L, with a limit of detection of 3.1 µg/L and limit of quantification of 12.5 µg/L. This sensor is useful for the analysis of chlorpyrifosin drinking water and orange juice, with high recovery rates and no interference effects.

How Plant Toxins Cause Early Larval Mortality in Herbivorous Insects: An Explanation by Modeling the Net Energy Curve

Plants store chemical defenses that act as toxins against herbivores, such as toxic isothiocyanates (ITCs) in Brassica plants, hydrolyzed from glucosinolate (GLS) precursors. The fitness of herbivorous larvae can be strongly affected by these toxins, causing immature death. We modeled this phenomenon using a set of ordinary differential equations and established a direct relationship between feeding, toxin exposure, and the net energy of a larva, where the fitness of an organism is proportional to its net energy according to optimal foraging theory. Optimal foraging theory is widely used in ecology to model the feeding and searching behavior of organisms. Although feeding provides energy gain, plant toxins and foraging cause energy loss for the larvae. Our equations explain that toxin exposure and foraging can sharply reduce larval net energy to zero at an instar. Since herbivory needs energy, the only choice left for a larva is to stop feeding at that time point. If that is significantly earlier than the end of the last instar stage, the larva dies without food. Thus, we show that plant toxins can cause immature death in larvae from the perspective of optimal foraging theory.

MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy

Complex biological systems often undergo sudden qualitative changes during their dynamic evolution. These critical transitions are typically characterized by a catastrophic progression of the system. Identifying the critical point is critical to uncovering the underlying mechanisms of complex biological systems. However, the system may exhibit minimal changes in its state until the critical point is reached, and in the face of high throughput and strong noise data, traditional biomarkers may not be effective in distinguishing the critical state. In this study, we propose a novel approach, mutual information weighted entropy (MIWE), which uses mutual information between genes to build networks and identifies critical states by quantifying molecular dynamic differences at each stage through weighted differential entropy. The method is applied to one numerical simulation dataset and four real datasets, including bulk and single-cell expression datasets. The critical states of the system can be recognized and the robustness of MIWE method is verified by numerical simulation under the influence of different noises. Moreover, we identify two key transcription factors (TFs), CREB1 and CREB3, that regulate downstream signaling genes to coordinate cell fate commitment. The dark genes in the single-cell expression datasets are mined to reveal the potential pathway regulation mechanism.

mNFE: microbiome network flow entropy for detecting pre-disease states of type 1 diabetes

In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point or critical state in a reliable manner, thereby providing accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states. To address this problem, we proposed a new method - microbiome network flow entropy (mNFE) based on a single sample from each individual - for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identified some non-differential 'dark species' play important roles during the T1D progression. mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach not only for T1D pre-disease diagnosis or preventative treatment but also for preventative medicine of other diseases by gut microbiome.

On Kinetic Constraints That Catalysis Imposes on Elementary Processes

Catalysis, the acceleration of product formation by a substance that is left unchanged, typically results from multiple elementary processes, including diffusion of the reactants toward the catalyst, chemical steps, and release of the products. While efforts to design catalysts are often focused on accelerating the chemical reaction on the catalyst, catalysis is a global property of the catalytic cycle that involves all processes. These are controlled by both intrinsic parameters such as the composition and shape of the catalyst and extrinsic parameters such as the concentration of the chemical species at play. We examine here the conditions that catalysis imposes on the different steps of a reaction cycle and the respective role of intrinsic and extrinsic parameters of the system on the emergence of catalysis by using an approach based on first-passage times. We illustrate this approach for various decompositions of a catalytic cycle into elementary steps, including non-Markovian decompositions, which are useful when the presence and nature of intermediate states are a priori unknown. Our examples cover different types of reactions and clarify the constraints on elementary steps and the impact of species concentrations on catalysis.

Words of advice: teaching macromolecular crystallography

The ability to view structures of proteins at atomic resolution, facilitated by the rise of macromolecular crystallography, has had a tremendous impact in many areas of sciences, including molecular pharmacology, drug discovery and biotechnology. However, the teaching of macromolecular crystallography in universities across the globe has been less than optimal. This could be attributed to the interdisciplinary nature of this subject, making it appear esoteric and incomprehensible, at least at first glance, for students who have exclusive training in only one specific discipline. For the instructor, this problem is compounded further by the plethora of complex concepts and specialized terminologies that the science of macromolecular crystallography has accumulated over the course of its evolution. Moreover, the advent of robotics and several sophisticated software algorithms have reduced the incentive to understand the beautiful conceptual bedrock on which this subject is based. As a way of addressing some of the challenges delineated above, this Words of Advice article attempts to formulate the broad framework within which the teaching and learning of macromolecular crystallography should be approached. It advocates the acknowledgement that this is an interdisciplinary field, with substantial contributions from chemical, physical, biological and mathematical sciences, requiring the evolution of teaching approaches that acknowledge this reality. Moreover, it suggests the use of visual tools, use of computational resources and history to make the subject more relatable to students.

An electrochemical sensor based on FeCo bimetallic single-atom nanozyme for sensitive detection of H2O2

Efficient catalytic decomposition of H2O2 is accompanied by electron transfer through Fe-Nx active sites of hemin in the human body. Inspired by this reaction process, the Fe SAs/Co CNs were successfully synthesized by combined Co atomic sites with nitrogen-carbon doped Fe single-atom active sites (SAs). The synergy between transition metals not only reduces agglomeration during synthesis but also improves its own electrical conductivity due to the interaction between Fe and Co that promotes the formation of graphite surface. Crucially, the synergistic effect of the Co site significantly enhanced the peroxidase activity of the Fe SAs and the reaction rate of the Fenton-like reaction, resulting in an efficient detection of H2O2. Catalytic kinetic calculations and enzymatic kinetic calculations were used to verify the electron transfer rate and catalytic performance of their constructed electrochemical sensing interfaces. The results showed that Fe SAs/Co CNs@GCE showed better detection performance than Fe SAs CNs@GCE. It was applied successfully to detect H2O2 released from cells in real-time as well. The linear detection range of Fe SAs/Co CNs@GCE for H2O2 was 1-16664 μM, and the detection limit was as low as 0.25 μM. Furthermore, an electrochemical sensing chip was constructed using Fe SAs/Co CNs@SPE and the prepared microfluidic channel. The constructed portable FeSAs/Co CNs@SPE had a linear range of 1-400 μM and a detection limit of 0.36 μM and achieved the recovery detection of H2O2 in serum. The electrochemical sensing interfaces constructed based on Fe SAs/Co CNs all have efficient catalytic performance and excellent real-time hydrogen peroxide detection performance, which have practical application potential for human oxidative stress level detection. And it provides a novel approach to the trace detection of bioactive small molecules.

Modeling of covalent modifications of histones to estimate the binding affinity

Covalent histone modifications such as methylation, acetylation, phosphorylation, and other epigenetic modifications of the chromatin play an essential role in regulating eukaryotic cells of which most of these reactions are catalyzed by the enzymes. The binding energy of enzymes is often determined by experimental data via mathematical and statistical models due to specific modifications. Many theoretical models have been introduced to study histone modifications and reprogramming experiments in mammalian cells, in which all efforts in determining the affinity binding are essential part of the work. Here, we introduce a one-dimensional statistical Potts model to accurately determine the enzyme's binding free energy using the experimental data for various types of cells. We study the methylation of lysine 4 and 27 on histone H3 and suppose that each histone has one modification site with one of the seven states: H3K27me3, H3K27me2, H3K27me1, unmodified, H3K4me1, H3K4me2, and H3K4me3. Based on this model, the histone covalent modification is described. Moreover, by using simulation data, the histone's binding free energy and the energy of chromatin states are determined, when they are subject to changes from unmodified to active or repressive states, by finding the probability of the transition.

Kinetics of the enzyme titration process by reversible modifiers

The effect of reversible modifiers on the initial rate of enzyme catalysed reactions has been investigated in a quasi-equilibrium approximation using the general modifier mechanism of Botts and Morales. It has been shown that, when investigating the dependence of the initial rate on the modifier concentration at a fixed substrate concentration, the kinetics of enzyme titration by reversible modifiers can generally be described using two kinetic constants. Just as the dependence of the initial rate on the substrate concentration (at a fixed modifier concentration) is described using two kinetic constants: the Michaelis constant Km and the limiting rate Vm. Only one constant M50 is needed to describe the kinetics of linear inhibition, and in the case of nonlinear inhibition and activation, along with M50 the constant QM is also needed. Knowing the values of the constants M50 and QM, it is possible to unambiguously determine the modification efficiency, that is, to calculate how many times the initial rate of the enzyme catalysed reaction will change when a certain modifier concentration is added to the incubation medium. The properties of these fundamental constants have been analysed in detail and the dependence of these constants on other parameters of the Botts-Morales model have been shown. Equations describing the dependence of relative reaction rates on the modifier concentration using these kinetic constants are presented. Various ways of linearising these equations for calculating the kinetic constants M50 and QM from experimental data are also presented.

Enzymatic kinetics of photosystem II with DCBQ as a substrate in extended Michaelis-Menten model

This study aimed to examine enzymatic kinetics of photosystem II (PSII) of maize mesophyll chloroplasts using the artificial electron acceptor 2,6-dichloro-1,4-benzoquinone (DCBQ) as a substrate. We extended Michealis-Menten kinetics model assuming that DCBQ can accept electrons from PSII in two ways: from a QB directly or from QA by docking in the QB site. We used a Clark oxygen electrode for measuring the PSII activity, depending on the concentration of DCBQ. We found that: [1] DCBQ acts as an electron acceptor or [2] as an inhibitor for PSII. At a concentration < 0.2 mM, DCBQ accepted electrons from the QB at a rate of 889 electrons/s, while at >> 0.2 mM it replaced QB following which the activity decreased to zero. DCBQ located in the QB also increased the affinity of the substrate to PSII. We determined the kinetic parameters for the chloroplasts of plants growing under high and low light intensity, to change thylakoid stacking and thus the rate of electron transport. The parameter KmB, which is a measure of the affinity of DCBQ to PSII, showed quantitative changes based on light intensity, while K was proportional to the size of the plastoquinone pool. We believe that our model can be applied as a tool to study "State transitions" and induced changes in grana stacking in plants exposed to various stresses, which will facilitate the regulation of electron transfer pathways through an appropriate balance between linear and cyclic electron transport.

Non-equilibrium modalities of inhibition: Characterizing irreversible inhibition for the ErbB receptor family members

Most drug target interactions for clinically approved small-molecules are non-equilibrium slow-onset, tight-binding or irreversible in nature, with pronounced element of time-dependence of inhibition. Analysis of such modality of inhibition requires a continuous enzyme kinetic measurement that can yield complete progress curves and an automated high-throughput analysis pipeline. Given the increasing emphasis on designing non-equilibrium modes of inhibiting an enzyme target (especially irreversible), the above specified pipeline for data generation and analysis is essential for extracting parameters to guide decisions in early drug discovery. In this manuscript, the methodology and data analysis protocol from our irreversible inhibitor characterization campaigns for the ErbB receptor family members is presented. Guidance is provided on the appropriate design of assay to generate quality data, setting up the analysis and estimation of inactivation rate (kinact) and the pseudo-equilibrium binding affinity (KI) constant (or their ratio kinact/KI) in a high-throughput manner for the inhibitor interacting with the protein target of interest.

Partial Reversible Inhibition of Enzymes and Its Metabolic and Pharmaco-Toxicological Implications

Partial reversible inhibition of enzymes, also called hyperbolic inhibition, is an uncommon mechanism of reversible inhibition, resulting from a productive enzyme-inhibitor complex. This type of inhibition can involve competitive, mixed, non-competitive and uncompetitive inhibitors. While full reversible inhibitors show linear plots for reciprocal enzyme initial velocity versus inhibitor concentration, partial inhibitors produce hyperbolic plots. Similarly, dose-response curves show residual fractional activity of enzymes at high doses. This article reviews the theory and methods of analysis and discusses the significance of this type of reversible enzyme inhibition in metabolic processes, and its implications in pharmacology and toxicology.

Identification and biochemical characterization of small molecule inhibitors of ERK2 that target the D-recruitment site

Extracellular signal-regulated kinase (ERK) is the culmination of a mitogen-activated protein kinase cascade that regulates cellular processes like proliferation, migration, and survival. Consequently, abnormal ERK signaling often plays a role in the tumorigenesis and metastasis of numerous cancers. ERK inhibition is a sought-after treatment for cancers, especially since clinically approved drugs that target signaling upstream of ERK often induce acquired resistance. Furthermore, the ERK2 isoform may have a differential role in various cancers from the other canonical isoform, ERK1. We demonstrate that small molecules can inhibit ERK2 catalytic and noncatalytic functions by binding to the D-recruitment site (DRS), a protein-protein interaction site distal to the enzyme active site. Using a fluorescence anisotropy-based high-throughput screening, we identify compounds that bind to the DRS and exhibit dose-dependent inhibition of ERK2 activity and ERK2 phosphorylation. We characterize the dose-dependent potency of ERK2 inhibitors using fluorescence anisotropy-based binding assays, fluorescence-based ERK2 substrate phosphorylation assays, and in vitro ERK2 activation assays. In our example, the binding of a DRS inhibitor can be prevented by mutating the DRS residue Cys-159 to serine, indicating that this residue is essential for the interaction. Resulting inhibitors from this process can be assessed in cellular and in vivo experiments for inhibition of ERK signaling and can be evaluated as potential cancer drugs.

Trypsin from Pyloric Caeca of Asian Seabass: Purification, Characterization, and Its Use in the Hydrolysis of Acid-Soluble Collagen

The study aimed to purify trypsin from the pyloric caeca of Asian seabass (Lates calcarifer), and investigate its proteolytic capability toward acid-soluble collagen (ASC) in comparison with commercial porcine trypsin (CPT). Trypsin was purified from pyloric caeca, a leftover from the evisceration process, via ammonium sulphate (40-60% saturation) precipitation, and a soybean trypsin inhibitor (SBTI)-Sepharose 4B column. A 18.5-fold purification and a yield of 15.2% were obtained. SDS-PAGE analysis confirmed a single band of trypsin with a molecular weight of 23.5 kDa. Purified trypsin also showed the single band in native-PAGE. The optimal pH and temperature of trypsin for BAPNA (the specific substrate for amidase) hydrolysis were 8.5 and 60 °C, respectively. The trypsin was stable within the pH range of 7.0-9.5 and temperature range of 25-55 °C. Protease inhibition study confirmed that the purified enzyme was trypsin. The purified trypsin had a Michaelis-Menten constant (Km) and catalytic constant (kcat) of 0.078 mM and 5.4 s-1, respectively, when BAPNA was used. For the hydrolysis of TAME (the specific substrate for esterase), the Km and Kcat were 0.09 mM and 4.8 s-1, respectively. Partially purified seabass trypsin (PPST) had a slightly lower hydrolysis capacity toward ASC than CPT, as evidenced by the lower degree of hydrolysis and protein degradation when the former was used. Both the α-chain and β-chain became more degraded as the hydrolysis time increased. Based on MALDI-TOP, peptides with MW of 2992-2970 Da were dominant in the hydrolysates. Therefore, seabass trypsin could be used in the production of hydrolyzed collagen. It could have economic importance to the market, by replacing some commercial proteases, which have religious constraints.

Steady-state kinetic analysis of reversible enzyme inhibitors: A case study on calf intestine alkaline phosphatase

Enzymes are important drug targets and inhibition of enzymatic activity is an important therapeutic strategy. Enzyme assays measuring catalytic activity are utilized in both the discovery and development of new drugs. Colorimetric assays based on the release of 4-nitrophenol from substrates are commonly used. 4-Nitrophenol is only partly ionized to 4-nitrophenolate under typical assay conditions (pH 7-9) leading to under-estimation of product formation rates due to the much lower extinction coefficient of 4-nitrophenol compared to 4-nitrophenolate. Determination of 4-nitrophenol pKa values based on absorbance at 405 nm as a function of experimental pH values is reported, allowing for calculation of a corrected extinction coefficient at the assay pH. Characterization of inhibitor properties using steady-state enzyme kinetics is demonstrated using calf intestine alkaline phosphatase and 4-nitrophenyl phosphate as substrate at pH ∼8.2. The following kinetic parameters were determined: Km= 40±3 µM; Vmax= 72.8±1.2 µmolmin-1mg protein-1; kcat= 9.70±0.16 s-1; kcat/Km= 2.44±0.16 × 105 M-1s-1 (mean± SEM, N = 4). Sodium orthovanadate and EDTA were used as model inhibitors and the following pIC50 values were measured using dose-response curves: 6.61±0.08 and 3.07±0.03 (mean±SEM, N = 4). Rapid dilution experiments determined that inhibition was reversible for sodium orthovanadate and irreversible for EDTA. A Ki value for orthovanadate of 51±8 nM (mean±SEM, N = 3) was determined. Finally, data analysis and statistical design of experiments are discussed.

Algorithmic criteria for the validity of quasi-steady state and partial equilibrium models: the Michaelis-Menten reaction mechanism

We present "on the fly" algorithmic criteria for the accuracy and stability (non-stiffness) of reduced models constructed with the quasi-steady state and partial equilibrium approximations. The criteria comprise those introduced in Goussis (Combust Theor Model 16:869-926, 2012) that addressed the case where each fast time scale is due to one reaction and a new one that addresses the case where a fast time scale is due to more than one reactions. The development of these criteria is based on the ability to approximate accurately the fast and slow subspaces of the tangent space. Their validity is assessed on the basis of the Michaelis-Menten reaction mechanism, for which extensive literature is available regarding the validity of the existing various reduced models. The criteria predict correctly the regions in both the parameter and phase spaces where each of these models is valid. The findings are supported by numerical computations at indicative points in the parameter space. Due to their algorithmic character, these criteria can be readily employed for the reduction of large and complex mathematical models.

Unraveling the catalytic mechanism of SARS-CoV-2 papain-like protease with allosteric modulation of C270 mutation using multiscale computational approaches

Papain-like protease (PL^pro) is a promising therapeutic target against SARS-CoV-2, but its restricted S1/S2 subsites pose an obstacle in developing active site-directed inhibitors. We have recently identified C270 as a novel covalent allosteric site for SARS-CoV-2 PL^pro inhibitors. Here we present a theoretical investigation of the proteolysis reaction catalyzed by the wild-type SARS-CoV-2 PL^pro as well as the C270R mutant. Enhanced sampling MD simulations were first performed to explore the influence of C270R mutation on the protease dynamics, and sampled thermodynamically favorable conformations were then submitted to MM/PBSA and QM/MM MD simulations for thorough characterization of the protease-substrate binding and covalent reactions. The disclosed proteolysis mechanism of PL^pro, as characterized by the occurrence of proton transfer from the catalytic C111 to H272 prior to the substrate binding and with deacylation being the rate-determining step of the whole proteolysis process, is not completely identical to that of the 3C-like protease, another key cysteine protease of coronaviruses. The C270R mutation alters the structural dynamics of the BL2 loop that indirectly impairs the catalytic function of H272 and reduces the binding of the substrate with the protease, ultimately showing an inhibitory effect on PL^pro. Together, these results provide a comprehensive understanding at the atomic level of the key aspects of SARS-CoV-2 PL^pro proteolysis, including the catalytic activity allosterically regulated by C270 modification, which is crucial to the follow-up inhibitor design and development.

Engineered repeat proteins as scaffolds to assemble multi-enzyme systems for efficient cell-free biosynthesis

Multi-enzymatic cascades with enzymes arranged in close-proximity through a protein scaffold can trigger a substrate channeling effect, allowing for efficient cofactor reuse with industrial potential. However, precise nanometric organization of enzymes challenges the design of scaffolds. In this study, we create a nanometrically organized multi-enzymatic system exploiting engineered Tetrapeptide Repeat Affinity Proteins (TRAPs) as scaffolding for biocatalysis. We genetically fuse TRAP domains and program them to selectively and orthogonally recognize peptide-tags fused to enzymes, which upon binding form spatially organized metabolomes. In addition, the scaffold encodes binding sites to selectively and reversibly sequester reaction intermediates like cofactors via electrostatic interactions, increasing their local concentration and, consequently, the catalytic efficiency. This concept is demonstrated for the biosynthesis of amino acids and amines using up to three enzymes. Scaffolded multi-enzyme systems present up to 5-fold higher specific productivity than the non-scaffolded ones. In-depth analysis suggests that channeling of NADH cofactor between the assembled enzymes enhances the overall cascade throughput and the product yield. Moreover, we immobilize this biomolecular scaffold on solid supports, creating reusable heterogeneous multi-functional biocatalysts for consecutive operational batch cycles. Our results demonstrate the potential of TRAP-scaffolding systems as spatial-organizing tools to increase the efficiency of cell-free biosynthetic pathways.

Agitated ants: regulation and self-organization of incipient nest excavation via collisional cues

Ants are millimetres in scale yet collectively create metre-scale nests in diverse substrates. To discover principles by which ant collectives self-organize to excavate crowded, narrow tunnels, we studied incipient excavation in small groups of fire ants in quasi-two-dimensional arenas. Excavation rates displayed three stages: initially excavation occurred at a constant rate, followed by a rapid decay, and finally a slower decay scaling in time as t-1/2. We used a cellular automata model to understand such scaling and motivate how rate modulation emerges without global control. In the model, ants estimated their collision frequency with other ants, but otherwise did not communicate. To capture early excavation rates, we introduced the concept of 'agitation'-a tendency of individuals to avoid rest if collisions are frequent. The model reproduced the observed multi-stage excavation dynamics; analysis revealed how parameters affected features of multi-stage progression. Moreover, a scaling argument without ant-ant interactions captures tunnel growth power-law at long times. Our study demonstrates how individual ants may use local collisional cues to achieve functional global self-organization. Such contact-based decisions could be leveraged by other living and non-living collectives to perform tasks in confined and crowded environments.

A theory of chemical reactions in biomolecules in solution: Generalized Langevin mode analysis (GLMA)

The generalized Langevin mode analysis (GLMA) is applied to chemical reactions in biomolecules in solution. The theory sees a chemical reaction in solution as a barrier-crossing process, similar to the Marcus theory. The barrier is defined as the crossing point of two free-energy surfaces that are attributed to the reactant and product of the reaction. It is assumed that both free-energy surfaces are quadratic or harmonic. The assumption is based on the Kim-Hirata theory of structural fluctuation of protein, which proves that the fluctuation around an equilibrium structure is quadratic with respect to the structure or atomic coordinates. The quadratic surface is a composite of many harmonic functions with different modes or frequencies. The height of the activation barrier will be dependent on the mode or frequency-the less the frequency, the lower the barrier. Hence, it is essential to decouple the fluctuational modes into a hierarchical order. GLMA is impeccable for this purpose. It is essential for a theoretical study of chemical reactions to choose a reaction coordinate along which the reaction proceeds. We suppose that the mode whose center of coordinate and/or the frequency changes most before and after the reaction is the one relevant to the chemical reaction and choose the coordinate as the reaction coordinate. The rate of reaction along the reaction coordinate is k_rate=ν⁡exp-ΔF^(†)/k_BT, which is similar to the Marcus expression for the electron transfer reaction. In the equation, ΔF^(†) is the activation barrier defined by ΔF^(†)≡F^(r)Q^†-F^(r)(Q_eq ^(r)), where F^(r)(Q_eq ^(r)) and F^(r)Q^† denote the free energies at equilibrium Q_eq ^(r) and the crossing point Q^†, respectively, both on the free energy surface of the reactant.

Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review

According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CL_int,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.

Li vs Na: Divergent Reaction Patterns between Organolithium and Organosodium Complexes and Ligand-Catalyzed Ketone/Aldehyde Methylenation

Organosodium chemistry is underdeveloped compared with organolithium chemistry, and all the reported organosodium complexes exhibit similar, if not identical, reactivity patterns to their lithium counterparts. Herein, we report a rare organosodium monomeric complex, namely, [Na(CH₂SiMe₃)(Me₆Tren)] (1-Na) (Me₆Tren: tris[2-(dimethylamino)ethyl]amine) stabilized by a tetra-dentate neutral amine ligand Me₆Tren. Employing organo-carbonyl substrates (ketones, aldehydes, amides, ester), we demonstrated that 1-Na features distinct reactivity patterns compared with its lithium counterpart, [Li(CH₂SiMe₃)(Me₆Tren)] (1-Li). Based on this knowledge, we further developed a ligand-catalysis strategy to conduct ketone/aldehyde methylenations, using [NaCH₂SiMe₃]_∞ as the CH₂ feedstock, replacing the widely used but hazardous/expensive C═O methylenation methods, such as Wittig, Tebbe, Julia/Julia-Kocieński, Peterson, and so on.

New Methodology for Determining Plasma Protein Binding Kinetics Using an Enzyme Reporter Assay Coupling with High-Resolution Mass Spectrometry

Determination of drug binding kinetics in plasma is important yet extremely challenging. Accordingly, we introduce "dynamic free fraction" as a new binding parameter describing drug-protein binding kinetics. We demonstrate theoretically and experimentally that the dynamic free fraction can be determined by coupling the drug binding assay with a reporter enzyme in combination with high-resolution mass spectrometry measuring the relative initial steady-state rates of enzymatic reactions in the absence and presence of matrix proteins. This novel and simple methodology circumvents a long-standing challenge inherent in existing methods for determining binding kinetics constants, such as k_on and k_off, and enables assessment of the impact of protein binding kinetics on pharmaceutical properties of drugs. As demonstrated with nine model drugs, the predicted liver extraction ratio, a measure of efficiency of drug removal by the liver, correlates significantly better to the observed extraction ratio when using the dynamic free fraction (f_D) in place of the unbound fraction (f_u) of the drug in plasma. Similarly, the in vivo hepatic clearance of these drugs, a measure of liver drug elimination, is highly comparable to the clearance values calculated with the dynamic free fraction (f_D), which is markedly better than those calculated with the unbound fraction (f_u). In contrast to the prevailing view, these results indicate that protein binding kinetics is an important pharmacokinetic property of a drug. As plasma protein binding is one of the most important drug properties, this new methodology may represent a breakthrough and could have a real impact on the field.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Kinetic Mathematical Modeling of Oxidative Phosphorylation in Cardiomyocyte Mitochondria

Oxidative phosphorylation (OXPHOS) is an oxygen-dependent process that consumes catabolized nutrients to produce adenosine triphosphate (ATP) to drive energy-dependent biological processes such as excitation-contraction coupling in cardiomyocytes. In addition to in vivo and in vitro experiments, in silico models are valuable for investigating the underlying mechanisms of OXPHOS and predicting its consequences in both physiological and pathological conditions. Here, we compare several prominent kinetic models of OXPHOS in cardiomyocytes. We examine how their mathematical expressions were derived, how their parameters were obtained, the conditions of their experimental counterparts, and the predictions they generated. We aim to explore the general landscape of energy production mechanisms in cardiomyocytes for future in silico models.

An Experimental Framework for Developing Point-of-Need Biosensors: Connecting Bio-Layer Interferometry and Electrochemical Impedance Spectroscopy

Biolayer interferometry (BLI) is a well-established laboratory technique for studying biomolecular interactions important for applications such as drug development. Currently, there are interesting opportunities for expanding the use of BLI in other fields, including the development of rapid diagnostic tools. To date, there are no detailed frameworks for implementing BLI in target-recognition studies that are pivotal for developing point-of-need biosensors. Here, we attempt to bridge these domains by providing a framework that connects output(s) of molecular interaction studies with key performance indicators used in the development of point-of-need biosensors. First, we briefly review the governing theory for protein-ligand interactions, and we then summarize the approach for real-time kinetic quantification using various techniques. The 2020 PRISMA guideline was used for all governing theory reviews and meta-analyses. Using the information from the meta-analysis, we introduce an experimental framework for connecting outcomes from BLI experiments (KD, kon, koff) with electrochemical (capacitive) biosensor design. As a first step in the development of a larger framework, we specifically focus on mapping BLI outcomes to five biosensor key performance indicators (sensitivity, selectivity, response time, hysteresis, operating range). The applicability of our framework was demonstrated in a study of case based on published literature related to SARS-CoV-2 spike protein to show the development of a capacitive biosensor based on truncated angiotensin-converting enzyme 2 (ACE2) as the receptor. The case study focuses on non-specific binding and selectivity as research goals. The proposed framework proved to be an important first step toward modeling/simulation efforts that map molecular interactions to sensor design.

A Discovery Biotransformation Strategy: Combining In Silico Tools with High-Resolution Mass Spectrometry and Software-Assisted Data Analysis for High-Throughput Metabolism

Understanding compound metabolism in early drug discovery aids medicinal chemistry in designing molecules with improved safety and ADME properties. While advancements in metabolite prediction brings increasedconfidence, structural decisions require experimental data. In vitro metabolism studies using liquid chromatography and high-resolution mass spectrometry (LC-MS) are generally resource intensive and performed on very few compounds, limiting the chemical space that can be examined.Here, we describe a novel metabolism strategy increasing compound throughput using residual in vitro clearance samples conducted at drug concentrations of 0.5 µM. Analysis by robust UHPLC separation and accurate-mass MS detection ensures major metabolites are identified from a single injection. In silico prediction (parent cLogD) tailors chromatographic conditions, with data-dependent MS/MS targeting predicted metabolites. Software-assisted data mining, structure elucidation and automatic reporting are used.Confidence in the globally-aligned workflow is demonstrated with sixteen marketed drugs. The approach is now implemented routinely across our laboratories. To date, the success rate for identification of at least one major metabolite is 85%. The utility of these data has been demonstrated across multiple projects, allowing earlier medicinal chemistry decisions to increase efficiency and impact of the design-make-test cycle; thus improving the translatability of early in vitro metabolism data.