Enzyme Kinetics and Principles of Enzyme Inhibition
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Chapter 4. Enzyme Kinetics
Chapter 4. Enzyme Kinetics Enzyme Engineering 4.1 Why is enzyme kinetics important? 1. It provides valuable information for enzyme mechanism 2. It gives an insight into the role of an enzyme under physiological conditions 3. It can help show how the enzyme activity is controlled and regulated 1 4.2 How to obtain enzyme kinetics? Measure the formation of product or disappearance of substrate Mg2+ D-Glucose + ATP D-Glucose-6-phosphate + ADP 1. Discontinuous method Add the enzyme, then stop the reaction at different time by adding acid to the sample Measure the concentration of substrate or product using HPLC or other chromatography 4.2 How to obtain enzyme kinetics? 2. Continuous method Mg2+ D-Glucose + ATP D-Glucose-6-phosphate + ADP NADP+ NADPH D-Glucono-δ-lactone 6-phosphate + ADP NADPH absorb at 340nm, while NADP+ does not If a coupled reaction is used, the second reaction should not be rate-limiting (sufficient amount of the enzyme must be provided) Continuous method is not always available 2 4.2 How to obtain enzyme kinetics? Radioactive or fluorescent-labeling is useful Precautions The substrate, buffers, etc. must be extremely pure Enzyme should be pure Enzyme should be stable as the assay is proceeded * Temp, pH must be maintained carefully Once steady state is achieved, reaction rate must be constant and proportional to the enzyme added Initial rate of reaction should be measured to avoid possible complexity 4.3 How to analyze kinetic data? 4.3.1 One substrate reactions k 1 k2 E + S ES E + P k-1 Equilibrium assumption (1913) Second reaction is slower than first reverse reaction (k-1 >> k2) Vmax[S] V0 = Michaelis-Menten eqn K s +[S] [E][S] Vmax = k2[E]0 K = s [ES] 3 4.3 How to analyze kinetic data? Steady state assumption (1925) [ES] is constant Vmax[S] V0 = Km +[S] Vmax = k2[E]0 k2 + k−1 Km = k1 4.3.1 One substrate reactions Lineweaver-Burk equation 1 K 1 1 = m + v [S] Vmax Vmax Eddie-Hofstee equation v V v = max − [S] Km Km 4 4.3.1 One substrate reactions Significance of the result 1. -
The Self-Inhibitory Nature of Metabolic Networks and Its Alleviation Through Compartmentalization
ARTICLE Received 30 Oct 2016 | Accepted 23 May 2017 | Published 10 Jul 2017 DOI: 10.1038/ncomms16018 OPEN The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization Mohammad Tauqeer Alam1,2, Viridiana Olin-Sandoval1,3, Anna Stincone1,w, Markus A. Keller1,4, Aleksej Zelezniak1,5,6, Ben F. Luisi1 & Markus Ralser1,5 Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite–enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism. 1 Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. 2 Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. 3 Department of Food Science and Technology, Instituto Nacional de Ciencias Me´dicas y Nutricio´n Salvador Zubira´n, Vasco de Quiroga 15, Tlalpan, 14080 Mexico City, Mexico. -
Catalase Kinetics
Experiment #4: Catalase Kinetics MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Chemistry 5.310 Laboratory Chemistry 1 EXPERIMENT #4 A Study of the Kinetics of the Enzyme Catalase and its Reaction 2 3 With H2O2. A Further Study on Protein Assay Quantitation of Catalase I. OVERVIEW OF THE EXPERIMENT In this experiment, the student will investigate the enzyme activity of catalase by studying the decomposition of H2O2 to form water and oxygen. Using an oxygen based pressure sensor the student measures the amount of oxygen produced and then calculates the rate of the enzyme catalyzed reaction under various conditions. The student also completes a Protein Assay on an unknown sample of catalase using the Coomassie® Plus Protein Assay from Pierce to determine the protein concentration of the sample. The correlation between the actual and calculated concentration gives an indication of the experimental skills used in carrying out the experiment. II. OBJECTIVES This is an integrated experiment that includes topics from physical chemistry and biochemistry. It is designed to introduce students to the basics of: • Studying the effects of reaction environment, including temperature, and varying substrate concentration on the rate of an enzyme-catalyzed reaction. • Combining physical chemistry and biochemistry, principles and theory, with the goal of determining various biochemical and biophysical constants for the enzyme catalase. • How to acquire experimental kinetic data for an enzyme catalyzed reaction. • Learning how to perform data manipulation in order to extract out information such as rate constants from experimental kinetic data. • Correct handling of UV-VIS Spectroscopy 1 This experiment was synthesized by John J. -
Plant Protease Inhibitors: a Defense Strategy in Plants
Biotechnology and Molecular Biology Review Vol. 2 (3), pp. 068-085, August 2007 Available online at http://www.academicjournals.org/BMBR ISSN 1538-2273 © 2007 Academic Journals Standard Review Plant protease inhibitors: a defense strategy in plants Huma Habib and Khalid Majid Fazili* Department of Biotechnology, The University of Kashmir, P/O Naseembagh, Hazratbal, Srinagar -190006, Jammu and Kashmir, India. Accepted 7 July, 2007 Proteases, though essentially indispensable to the maintenance and survival of their host organisms, can be potentially damaging when overexpressed or present in higher concentrations, and their activities need to be correctly regulated. An important means of regulation involves modulation of their activities through interaction with substances, mostly proteins, called protease inhibitors. Some insects and many of the phytopathogenic microorganisms secrete extracellular enzymes and, in particular, enzymes causing proteolytic digestion of proteins, which play important roles in pathogenesis. Plants, however, have also developed mechanisms to fight these pathogenic organisms. One important line of defense that plants have to fight these pathogens is through various inhibitors that act against these proteolytic enzymes. These inhibitors are thus active in endogenous as well as exogenous defense systems. Protease inhibitors active against different mechanistic classes of proteases have been classified into different families on the basis of significant sequence similarities and structural relationships. Specific protease inhibitors are currently being overexpressed in certain transgenic plants to protect them against invaders. The current knowledge about plant protease inhibitors, their structure and their role in plant defense is briefly reviewed. Key words: Proteases, enzymes, protease inhibitors, serpins, cystatins, pathogens, defense. Table of content 1. -
Estimation of the Overall Kinetic Parameters of Enzyme Inactivation Using an Isoconversional Method D
Estimation of the overall kinetic parameters of enzyme inactivation using an isoconversional method D. Oancea, Alexandrina Stuparu, Madalina Nita, Mihaela Puiu, Adina Raducan To cite this version: D. Oancea, Alexandrina Stuparu, Madalina Nita, Mihaela Puiu, Adina Raducan. Estimation of the overall kinetic parameters of enzyme inactivation using an isoconversional method. Biophysical Chemistry, Elsevier, 2008, 138 (1-2), pp.50. 10.1016/j.bpc.2008.09.003. hal-00501718 HAL Id: hal-00501718 https://hal.archives-ouvertes.fr/hal-00501718 Submitted on 12 Jul 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ÔØ ÅÒÙ×Ö ÔØ Estimation of the overall kinetic parameters of enzyme inactivation using an isoconversional method D. Oancea, Alexandrina Stuparu, Madalina Nita, Mihaela Puiu, Adina Raducan PII: S0301-4622(08)00176-2 DOI: doi: 10.1016/j.bpc.2008.09.003 Reference: BIOCHE 5155 To appear in: Biophysical Chemistry Received date: 17 August 2008 Revised date: 2 September 2008 Accepted date: 3 September 2008 Please cite this article as: D. Oancea, Alexandrina Stuparu, Madalina Nita, Mi- haela Puiu, Adina Raducan, Estimation of the overall kinetic parameters of en- zyme inactivation using an isoconversional method, Biophysical Chemistry (2008), doi: 10.1016/j.bpc.2008.09.003 This is a PDF file of an unedited manuscript that has been accepted for publication. -
Determination of Enzyme Kinetics in the Eppendorf Biospectrometer® Kinetic Using a Hexokinase and Glucose-6- Phosphate-Dehydrogenase from Saccharomyces Cerevisiae
USERGUIDE No. 39 I Detection I September 2014 Determination of enzyme kinetics in the Eppendorf BioSpectrometer® kinetic using a hexokinase and glucose-6- phosphate-dehydrogenase from Saccharomyces cerevisiae Martin Armbrecht, Eppendorf AG, Hamburg, Germany Abstract This Userguide will demonstrate measurement of enzyme activity using the Eppendorf BioSpectrometer. In order to optimize the measurement process, preliminary measurements were performed using the method “single continuous”. The actual activity measurements were then performed based on these results. Enzyme activities were determined via linear regression. For the measurements, a coupled reaction of a hexokinase and a glucose-6-phosphate-dehydrogenase from the baker’s yeast ‚ was used. Since the Eppendorf BioSpectrometer kinetic is equipped with a temperature controlled cuvette chamber, temperature dependence of this reaction could also be demonstrated. The highest activity was detected at 37 °C. Introduction Metabolic significance of the hexokinase reaction Activation of glucose via the hexokinase reaction Glycolysis Glucose degradation thus sustains respiration of all living beings. One central area within the metabolism of nearly all living beings is Prior to degradation, glucose needs to be rendered susceptible for the enzymatic degradation of glucose in the cytosol, or cytoplasma, degradation, i.e., activated. This process involves phosphorylation by respectively, in their cells. This process is called glycolysis. Glucose a hexokinase; with the expenditure of ATP, glucose-6-phosphate is is converted to 2 molecules of pyruvate in a stepwise fashion. One produced. Thus, the latter is the actual initial substrate of glycolysis molecule of glucose yields 2 ATP and 2 redox equivalents in the form (fig.1). In addition, glucose-6-phosphate is the initial substrate of a further of NADPH2. -
Effect of Statins and ACE Inhibitors Alone and in Combination on Clinical Outcome in Patients with Coronary Heart Disease
Journal of Human Hypertension (2004) 18, 781–788 & 2004 Nature Publishing Group All rights reserved 0950-9240/04 $30.00 www.nature.com/jhh ORIGINAL ARTICLE Effect of statins and ACE inhibitors alone and in combination on clinical outcome in patients with coronary heart disease VG Athyros1, DP Mikhailidis3, AA Papageorgiou2, VI Bouloukos1, AN Pehlivanidis1, AN Symeonidis4 and M Elisaf5, for the GREACE Study Collaborative Group 1Atherosclerosis Unit, Aristotelian University, Hippocration Hospital, Thessaloniki, Greece; 2Department of Clinical Biochemistry, Royal Free Hospital, Royal Free and University College Medical School, Pond Street, London, UK; 32nd Propedeutic Department of Internal Medicine, Aristotelian University, Hippocration Hospital, Thessaloniki, Greece; 4Greek Society of General Practitioners, Thessaloniki, Greece; 5Department of Internal Medicine, Medical School, University of Ioannina, Greece We assessed the ‘synergy’ of statins and angiotensin- point in group A was 31%, (95% CI À48 to À6%, P ¼ 0.01) converting enzyme inhibitors (ACEI) in reducing vascu- in comparison to group B, 59% (95% CI À72 to À48%, lar events in patients with coronary heart disease (CHD). Po0.0001) to group C and 63% (95% CI À74 to À51%, The GREek Atorvastatin and CHD Evaluation (GREACE) Po0.0001) to group D. There was no significant Study, suggested that aggressive reduction of low difference in RRR between groups C and D (9%, CI density lipoprotein cholesterol to 2.59 mmol/l À27–10%, P ¼ 0.1). Other factors (eg the blood pressure) (o100 mg/dl) significantly reduces morbidity and mor- that can influence clinical outcome did not differ tality in CHD patients, in comparison to undertreated significantly between the four treatment groups. -
Detection of Errors of Interpretation in Experiments in Enzyme Kinetics
METHODS 24, 181±190 (2001) doi:10.1006/meth.2001.1179, available online at http://www.idealibrary.com on Detection of Errors of Interpretation in Experiments in Enzyme Kinetics Athel Cornish-Bowden1 Institut FeÂdeÂratif ªBiologie Structurale et Microbiologie,º BioeÂnergeÂtique et IngeÂnierie des ProteÂines, Centre National de la Recherche Scientifique, 31 chemin Joseph-Aiguier, B.P. 71, 13402 Marseille Cedex 20, France points necessary for modern users are to be aware (at Although modern statistical computing will often be the method least qualitatively) of the assumptions that underlie of choice for analyzing kinetic data, graphic methods provide an important supplement that ought not to be neglected. Residual the statistical analysis and to be aware also that they plots, or plots of differences between observed and calculated are not always true (2). By contrast we shall be con- values against variables not expected to be correlated with these cerned with methods that require no detailed mathe- differences, permit a rapid judgment of whether data have been matics and can readily be applied to published graphs correctly interpreted and analyzed. The rapid increase in the fre- (or even to graphs displayed on a screen during a lec- quency with which artificially modified or mutated enzymes are ture) without access to the numerical coordinates of studied is making it less and less safe to assume that enzymes the observations. More generally, this article argues for are stable under assay conditions, and there is thus an increased more reliance on common sense and graphic analysis need for methods to check for enzyme stability, and a method and less on automatic analysis performed by computer: for doing this is briefly described. -
Chromatographic Resolution and Kinetic Characterization Of
Proc. NatL Acad. Sci. USA Vol. 80, pp. 8589, January 1983 Biochemistry Chromatographic resolution and kinetic characterization of glucokinase from islets of Langerhans (hexoldnase/islet glucose metabolism /N-acetylglucosamine kdnase/Cibacron blue) MARTIN D. MEGLASSON, PAMELA TRUEHEART BURCH, DONNA K. BERNER, HABIBA NAJAFI, ALAN P. VOGIN, AND FRANZ M. MATSCHINSKY* Diabetes Research Center and Department of Biochemistry and Biophysics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 Communicated by Robert E. Forster, October 4, 1982' ABSTRACT Glucokinase (ATP:D-glucose 6-phosphotransfer- ofislets equals serum glucose and islet glucose 6-phosphate in- ase, EC 2.7.1.2) from rat islets of Langerhans was partially pu- creases in proportion to extracellular glucose (1, 13). Ninety rified by chromatography on DEAE-Cibacron blue F3GA agar- percent ofislet glucose utilization occurs with an apparent glu- ose. The enzyme eluted in two separate peaks. Sigmoidal rate cose affinity constant 11.1 mM (6). Also, mannoheptulose, an dependence was found with respect to glucose (Hill coefficient inhibitor of glucokinase (8), profoundly inhibits islet glucose = 1.5) for both enzyme fractions. K. values for glucose were 5.7 metabolism. (14). mM for the major fraction and 4.5 mM for the minor fraction. The chromatographic separation of glucokinase from other Neither fraction phosphorylated GlcNAc. A' GlcNAc kinase is the most direct (ATP. 2-acetamido-2-deoxy-D-glucose' 6-phosphotransferase; EC glucose phosphorylating enzymes approach 2.7.1.59)-enriched fraction; prepared by affinity chromatography to establish its presence in pancreatic islets. It is the purpose on Sepharose-N46-aminohexanoyl)-GlcNAc, had a K. -
Kinetics the Michaelis-Menten Model for Enzyme Kinetics Presumes A
Michaelis-Menten (steady-state) Kinetics The Michaelis-Menten model for enzyme kinetics presumes a simple 2-step reaction: Step 1 : Binding – the substrate binds to the enzyme k1 k2 Step 2 : Catalysis – the substrate is converted to product and released E + S ES E + P (Note that enzymes not matching this reaction scheme may still show similar kinetics.) k-1 k-2 The Michaelis-Menten equation shows how the initial rate of Binding Catalysis ͐(3 ʞSʟ this reaction, Vo, depends on the substrate concentration, [S]: ͐* = ͅ( + ʞSʟ Several simplifying assumptions allow for the derivation of the Michaelis-Menten equation: (1) The binding step ( E + S ES ) is fast, allowing the reaction to quickly reach equilibrium ratios of [E], [S], and [ES]. The catalytic step ( ES E + P ) is slower, and thus rate-limiting. (2) At early time points, where initial velocity ( Vo) is measured, [P] ≈ 0. (3) ES immediately comes to steady state, so [ES] is constant (throughout the measured portion of the reaction). (4) [S] >> [E T], so the fraction of S that binds to E (to form ES) is negligible, and [S] is constant at early time points. (5) The enzyme exists in only two forms: free (E), and substrate-bound (ES). Thus, the total enzyme concentration (E T) is the sum of the free and substrate-bound concentrations: [E T] = [E] + [ES] A derivation of the Michaelis-Menten equation shows how to use the above assumptions to describe the rate of the enzyme-catalyzed reaction in terms of measurable quantities: From (1), we know the overall rate of the reaction is determined by the rate of the catalytic step: ͐* = ͦ͟ʞES ʟ − ͯͦ͟ʞEʟʞPʟ From (2), the second term equals zero, so we are left with: ͐* = ͦ͟ʞES ʟ We want to describe Vo in measurable quantities, but [ES] is not easy to Rate of formation of ES = Rate of breakdown of ES measure. -
Swiveling Domain Mechanism in Pyruvate Phosphate Dikinase†,‡ Kap Lim,§ Randy J
Biochemistry 2007, 46, 14845-14853 14845 Swiveling Domain Mechanism in Pyruvate Phosphate Dikinase†,‡ Kap Lim,§ Randy J. Read,| Celia C. H. Chen,§ Aleksandra Tempczyk,§ Min Wei,⊥ Dongmei Ye,⊥ Chun Wu,⊥ Debra Dunaway-Mariano,⊥ and Osnat Herzberg*,§ Center for AdVanced Research in Biotechnology, UniVersity of Maryland Biotechnology Institute, RockVille, Maryland 20850, Department of Haematology, Cambridge Institute for Medical Research, UniVersity of Cambridge, Cambridge, United Kingdom, and Department of Chemistry, UniVersity of New Mexico, Albuquerque, New Mexico ReceiVed September 10, 2007; ReVised Manuscript ReceiVed October 17, 2007 ABSTRACT: Pyruvate phosphate dikinase (PPDK) catalyzes the reversible conversion of phosphoenolpyruvate (PEP), AMP, and Pi to pyruvate and ATP. The enzyme contains two remotely located reaction centers: the nucleotide partial reaction takes place at the N-terminal domain, and the PEP/pyruvate partial reaction takes place at the C-terminal domain. A central domain, tethered to the N- and C-terminal domains by two closely associated linkers, contains a phosphorylatable histidine residue (His455). The molecular architecture suggests a swiveling domain mechanism that shuttles a phosphoryl group between the two reaction centers. In an early structure of PPDK from Clostridium symbiosum, the His445-containing domain (His domain) was positioned close to the nucleotide binding domain and did not contact the PEP/pyruvate- binding domain. Here, we present the crystal structure of a second conformational state of C. symbiosum PPDK with the His domain adjacent to the PEP-binding domain. The structure was obtained by producing a three-residue mutant protein (R219E/E271R/S262D) that introduces repulsion between the His and nucleotide-binding domains but preserves viable interactions with the PEP/pyruvate-binding domain. -
The Accumulation and Molecular Effects of Trimethylamine N-Oxide on Metabolic Tissues: It’S Not All Bad
nutrients Review The Accumulation and Molecular Effects of Trimethylamine N-Oxide on Metabolic Tissues: It’s Not All Bad Emily S. Krueger 1 , Trevor S. Lloyd 1,2 and Jeffery S. Tessem 1,* 1 Department of Nutrition, Dietetics and Food Science, Brigham Young University, Provo, UT 84602, USA; [email protected] (E.S.K.); [email protected] (T.S.L.) 2 Medical Education Program, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA * Correspondence: [email protected]; Tel.: +1-801-422-9082 Abstract: Since elevated serum levels of trimethylamine N-oxide (TMAO) were first associated with increased risk of cardiovascular disease (CVD), TMAO research among chronic diseases has grown exponentially. We now know that serum TMAO accumulation begins with dietary choline metabolism across the microbiome-liver-kidney axis, which is typically dysregulated during pathogenesis. While CVD research links TMAO to atherosclerotic mechanisms in vascular tissue, its molecular effects on metabolic tissues are unclear. Here we report the current standing of TMAO research in metabolic disease contexts across relevant tissues including the liver, kidney, brain, adipose, and muscle. Since poor blood glucose management is a hallmark of metabolic diseases, we also explore the variable TMAO effects on insulin resistance and insulin production. Among metabolic tissues, hepatic TMAO research is the most common, whereas its effects on other tissues including the insulin producing pancreatic β-cells are largely unexplored. Studies on diseases including obesity, diabetes, liver diseases, chronic kidney disease, and cognitive diseases reveal that TMAO effects are unique under pathologic conditions compared to healthy controls. We conclude that molecular TMAO effects are highly context-dependent and call for further research to clarify the deleterious and beneficial Citation: Krueger, E.S.; Lloyd, T.S.; molecular effects observed in metabolic disease research.