Application of topological methods in enzyme kinetics.
Ontology highlight
ABSTRACT:
SUBMITTER: Indge KJ
PROVIDER: S-EPMC1164744 | biostudies-other | 1977 Jun
REPOSITORIES: biostudies-other
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Project description:1. The normalization of biochemical data to weight them appropriately for parameter estimation is considered, with reference particularly to data from tracer kinetics and enzyme kinetics. If the data are in replicate, it is recommended that the sum of squared deviations for each experimental variable at each time or concentration point is divided by the local variance at that point. 2. If there is only one observation for each variable at each sampling point, normalization may still be required if the observations cover more than one order of magnitude, but there is no absolute criterion for judging the effect of the weighting that is produced. The goodness of fit that is produced by minimizing the weighted sum of squares of deviations must be judged subjectively. It is suggested that the goodness of fit may be regarded as satisfactory if the data points are distributed uniformly on either side of the fitted curve. A chi-square test may be used to decide whether the distribution is abnormal. The proportion of the residual variance associated with points on one or other side of the fitted curve may also be taken into account, because this gives an indication of the sensitivity of the residual variance to movement of the curve away from particular data points. These criteria for judging the effect of weighting are only valid if the model equation may reasonably be expected to apply to all the data points. 3. On this basis, normalizing by dividing the deviation for each data point by the experimental observation or by the equivalent value calculated by the model equation may both be shown to produce a consistent bias for numerically small observations, the former biasing the curve towards the smallest observations, the latter tending to produce a curve that is above the numerically smaller data points. It was found that dividing each deviation by the mean of observed and calculated variable appropriate to it produces a weighting that is fairly free from bias as judged by the criteria mentioned above. This normalization factor was tested on published data from both tracer kinetics and enzyme kinetics.
| S-EPMC1177869 | biostudies-other
Project description:Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK (High-Throughput Microfluidic Enzyme Kinetics), a microfluidic platform for high-throughput expression, purification, and characterization of more than 1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium), we performed more than 670,000 reactions and determined more than 5000 kinetic and physical constants for multiple substrates and inhibitors. We uncovered extensive kinetic partitioning to a misfolded state and isolated catalytic effects, revealing spatially contiguous regions of residues linked to particular aspects of function. Regions included active-site proximal residues but extended to the enzyme surface, providing a map of underlying architecture not possible to derive from existing approaches. HT-MEK has applications that range from understanding molecular mechanisms to medicine, engineering, and design.
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Project description:CONSPECTUS: The active site of an enzyme is surrounded by a fluctuating environment of protein and solvent conformational states, and a realistic calculation of chemical reaction rates and kinetic isotope effects of enzyme-catalyzed reactions must take account of this environmental diversity. Ensemble-averaged variational transition state theory with multidimensional tunneling (EA-VTST/MT) was developed as a way to carry out such calculations. This theory incorporates ensemble averaging, quantized vibrational energies, energy, tunneling, and recrossing of transition state dividing surfaces in a systematic way. It has been applied successfully to a number of hydrogen-, proton-, and hydride-transfer reactions. The theory also exposes the set of effects that should be considered in reliable rate constants calculations. We first review the basic theory and the steps in the calculation. A key role is played by the generalized free energy of activation profile, which is obtained by quantizing the classical potential of mean force as a function of a reaction coordinate because the one-way flux through the transition state dividing surface can be written in terms of the generalized free energy of activation. A recrossing transmission coefficient accounts for the difference between the one-way flux through the chosen transition state dividing surface and the net flux, and a tunneling transmission coefficient converts classical motion along the reaction coordinate to quantum mechanical motion. The tunneling calculation is multidimensional, accounting for the change in vibrational frequencies along the tunneling path and shortening of the tunneling path with respect to the minimum energy path (MEP), as promoted by reaction-path curvature. The generalized free energy of activation and the transmission coefficients both involve averaging over an ensemble of reaction paths and conformations, and this includes the coupling of protein motions to the rearrangement of chemical bonds in a statistical mechanically correct way. The standard deviations of the transmissions coefficients provide information on the diversity of the distribution of reaction paths, barriers, and protein conformations along the members of an ensemble of reaction paths passing through the transition state. We first illustrate the theory by discussing the application to both wild-type and mutant Escherichia coli dihydrofolate reductase and hyperthermophilic Thermotoga maritima dihydrofolate reductase (DHFR); DHFR is of special interest because the protein conformational changes have been widely studied. Then we present shorter discussions of several other applications of EA-VTST/MT to transfer of protons, hydrogen atoms, and hydride ions and their deuterated analogs. Systems discussed include hydride transfer in alcohol dehydrogenase, xylose isomerase, and thymidylate synthase, proton transfer in methylamine dehydrogenase, hydrogen atom transfer in methylmalonyl-CoA mutase, and nucleophilic substitution in haloalkane dehalogenase and two-dimensional potentials of mean force for potentially coupled proton and hydride transfer in the ?-oxidation of butyryl-coenzyme A catalyzed by short-chain acyl-CoA dehydrogenase and in the pyruvate to lactate transformation catalyzed by lactate dehydrogenase.
| S-EPMC4334256 | biostudies-literature
Project description:BACKGROUND:Mesenchymal stromal cells (MSC) demonstrate innate and regulatory immunologic functions and have been widely explored for cell therapy applications. Mechanisms by which MSCs achieve therapeutic effects are theorized, though appropriate dosing and duration of these mechanisms in vivo warrant deeper investigation. One, rapid immunosuppressive function of MSCs is through ectoenzyme expression of CD73 and CD39 which cooperatively hydrolyze inflammatory, extracellular adenosine triphosphate (ATP) to anti-inflammatory adenosine. Extracellular ATP has a key role in autoimmune and inflammatory diseases, which administered MSCs have the potential to modulate in a timescale that is befitting of shorter acting therapeutic function. METHODS:In vitro experiments were performed to determine the hydrolysis rates of ATP by MSCs. Through kinetic modeling from experimental results, the rate at which a single cell can metabolize ATP was determined. Based on these rates, the ability of MSCs to downregulate inflammatory signaling pathways was prospectively validated using model system parameters with respect to two different mechanisms: extracellular ATP stimulates lymphocytes to suppress proliferation and induce apoptosis and with co-stimulation, it stimulates monocytes to release pro-inflammatory IL-1?. MSCs were co-cultured with immune cells using transwell inserts and compared to immune cell only groups. RESULTS:Hydrolysis of ATP was efficiently modeled by first-order enzyme kinetics. For in vitro culture, the rate at which a single cell can hydrolyize ATP is 8.9 nmol/min. In the presence of extracellular ATP, cocultures of MSCs reduced cytotoxicity and allows for proliferation of lymphocytes while limiting IL-1? secretion from monocytes. CONCLUSIONS:Such use of these models may allow for better dosing predictions for MSCs to be used therapeutically for chronic inflammatory diseases such as rheumatoid arthritis, diabetes, pancreatitis, and other systemic inflammatory response syndromes. For the first time, the effect of MSCs on ATP hydrolysis in immune cell response is quantitatively analyzed on a cell-molecule basis by modeling the hydrolysis as an enzyme-substrate reaction. The results also give insight into MSCs' dynamic response mechanisms to ameliorate effects of extracellular ATP whether it be through positive or negative regulation.
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Project description:A microfluidic platform or "microfluidic mapper" is demonstrated, which in a single experiment performs 36 parallel biochemical reactions with 36 different combinations of two reagents in stepwise concentration gradients. The volume used in each individual reaction was 36 nl. With the microfluidic mapper, we obtained a 3D enzyme reaction plot of horseradish peroxidase (HRP) with Amplex Red (AR) and hydrogen peroxide (H2O2), for concentration ranges of 11.7 ?M to 100.0 ?M and 11.1 ?M to 66.7 ?M for AR and H2O2, respectively. This system and methodology could be used as a fast analytical tool to evaluate various chemical and biochemical reactions especially where two or more reagents interact with each other. The generation of dual concentration gradients in the present format has many advantages such as parallelization of reactions in a nanoliter-scale volume and the real-time monitoring of processes leading to quick concentration gradients. The microfluidic mapper could be applied to various problems in analytical chemistry such as revealing of binding kinetics, and optimization of reaction kinetics.
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Project description:Lysine acetyltransferase 8 (KAT8) is a histone acetyltransferase (HAT) responsible for acetylating lysine 16 on histone H4 (H4K16) and plays a role in cell cycle progression as well as acetylation of the tumor suppressor protein p53. Further studies on its biological function and drug discovery initiatives will benefit from the development of small molecule inhibitors for this enzyme. As a first step towards this aim we investigated the enzyme kinetics of this bi-substrate enzyme. The kinetic experiments indicate a ping-pong mechanism in which the enzyme binds Ac-CoA first, followed by binding of the histone substrate. This mechanism is supported by affinity measurements of both substrates using isothermal titration calorimetry (ITC). Using this information, the KAT8 inhibition of a focused compound collection around the non-selective HAT inhibitor anacardic acid has been investigated. Kinetic studies with anacardic acid were performed, based on which a model for the catalytic activity of KAT8 and the inhibitory action of anacardic acid (AA) was proposed. This enabled the calculation of the inhibition constant Ki of anacardic acid derivatives using an adaptation of the Cheng-Prusoff equation. The results described in this study give insight into the catalytic mechanism of KAT8 and present the first well-characterized small-molecule inhibitors for this HAT.
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Project description:Calculating the probability of each possible outcome for a patient at any time in the future is currently possible only in the simplest cases: short-term prediction in acute diseases of otherwise healthy persons. This problem is to some extent analogous to predicting the concentrations of species in a reactor when knowing initial concentrations and after examining reaction rates at the individual molecule level. The existing theoretical framework behind predicting contagion and the immediate outcome of acute diseases in previously healthy individuals is largely analogous to deterministic kinetics of chemical systems consisting of one or a few reactions. We show that current statistical models commonly used in chronic disease epidemiology correspond to simple stochastic treatment of single reaction systems. The general problem corresponds to stochastic kinetics of complex reaction systems. We attempt to formulate epidemiologic problems related to chronic diseases in chemical kinetics terms. We review methods that may be adapted for use in epidemiology. We show that some reactions cannot fit into the mass-action law paradigm and solutions to these systems would frequently exhibit an antiportfolio effect. We provide a complete example application of stochastic kinetics modeling for a deductive meta-analysis of two papers on atrial fibrillation incidence, prevalence, and mortality.
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Project description:BackgroundThe cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks.ResultsOur results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes.ConclusionDespite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.
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Project description:Sumoylation is a reversible post-translational modification that regulates certain significant biochemical functions in proteins. The protein alterations caused by sumoylation are associated with the incidence of some human diseases. Therefore, identifying the sites of sumoylation in proteins may provide a direction for mechanistic research and drug development. Here, we propose a new computational approach for identifying sumoylation sites using an encoding method based on topological data analysis. The features of our model captured the key physical and biological properties of proteins at multiple scales. In a 10-fold cross validation, the outcomes of our model showed 96.45% of sensitivity (Sn), 94.65% of accuracy (Acc), 0.8946 of Matthew's correlation coefficient (MCC), and 0.99 of area under curve (AUC). The proposed predictor with only topological features achieves the best MCC and AUC in comparison to the other released methods. Our results suggest that topological information is an additional parameter that can assist in the prediction of sumoylation sites and provide a novel perspective for further research in protein sumoylation.
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