Taylor, A. Winter 2012 Lab3:SolubleEnzymeKinetics
Introduction This lab will reinforce concepts addressed in BIOEN 335, Biotransport II. In particular, we will focus on enzyme kinetics. You have learned about enzymatic reactions in 335 lecture and required reading, and in this lab you will have the opportunity to set up an experiment involving a reaction catalyzed by the enzyme horseradish peroxidase. You will monitor the rate of reaction by recording the change in absorbance of the reaction product as a function of time. Ultimately, you will use this data to determine key kinetic parameters, including maximum reaction velocity, Michaelis constant, and maximum turnover number. Background Biological Relevance: Enzymes are present in virtually every tissue and fluid of the body. Comprised of proteins, RNA, and DNA, enzymes are catalysts responsible for supporting key biochemical reactions. Enzyme activity drives a significant portion of our life processes. Recall the enzyme hexokinase from our previous labs, which is involved in the conversion of glucose to lactate (Figure 1)! This is an example of how Intracellular enzymes catalyze the reactions of metabolic pathways. Additionally, plasma membrane enzymes regulate downstream reactions in cells in response to extracellular signals, and enzymes of the circulatory system are responsible for regulating hemostasis. Figure 1) Hexokinase enzyme with glucose. The hexokinase Enzymes are important because they greatly molecule (blue) has a molecule of glucose (red) bound to its increase the rate of biochemical reactions. Also, active site. The active site is the enzyme component which enzymes are specific to their particular substrates, binds to the substance involved in the reaction (substrate). which interact with the binding site on the enzyme, Hexokinase promotes the conversion (phosphorylation) of often with high affinity. This high‐affinity binding glucose into glucose 6‐phosphate in the body's cells. The cells allows enzymes to function effectively, even if the then use the glucose 6‐phosphate when needed (recall Fig. 1 from Lab 2: 1‐D Diffusion in Gel). Credit: Modified from substrate is present at low concentrations with a Nelson & Cox, Lehninger Principles of Biochemistry, 3rd ed., large number of other types of biological 2000 molecules. Enzyme activity can be controlled via multiple regulators, such as cofactors which bind to the enzyme. This helps ensure the appropriate amount of product is formed at the appropriate rate [1]. Experimental Overview: This procedure was adapted from: [2] and a previous BIOEN 357 lab exercise (Bryers). Horseradish peroxidase (HRP) is an enzyme derived from the root of the horseradish plant where its natural function is the oxidation of aromatic substrates. In this lab, we will study the oxidative coupling of o‐
diphenylenediamine (OPD) catalyzed by HRP to form 2,3 diaminophenazine (DAP) (Figure 2). BIOEN337
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TThe product, DAP, has an orange bro
own color aand absorbs strongly att 450nm. Thus, T
the kkinetics of this reaction can conven
niently be ffollowed by measuring the change
e in the aabsorbance of o the solution with a spectrometer aas a function o
of time. TThe initial reaction r
velocities can then be Figure 2) Forrmation of DA
AP product fro
om substrate ODP. You o
obtained fro
om this data, as a function of will follow thhis rate of reeaction by moonitoring the change in ssubstrate (OP
PD) concentration. By making m
the absorbance inntensity with time [2]. aappropriate straight‐line s
plots of varriations of tthe Michaeliss‐Menten equ
uation (i.e. a Lineweaver‐B
Burk plot) yoou will be ablle to determiine kinetic paarameters ssuch as Km, Vmax
m , etc. EExperimenta
al Procedure
e ***READ CAREEFULLY BEFOR
RE BEGINNIN
NG EXPERIMEENT, THESE STTEPS ARE VER
RY TIME‐SENSSITIVE W
Warning: OP
PD is a suspeccted carcinoggen. Gloves must be worrn at all timees for this lab
b. Lab safetyy must be m
monitored an
nd strictly enfforced for this lab, so you
ur cooperatioon is appreciaated. Waste from this exxperiment w
will be collectted in designaated waste bo
ottles. TThe following four stock so
olutions will b
be provided to
o you: aa) Solution 1 is 0.05 M MESS buffer, pH =
= 6.0 b
b) Solution 2 iis 0.0022 M O
OPD in 0.05M MES buffer, pH = 6 cc) Solution 3 is 3.5x10‐8 M HRP in 0.05 M MES buffer, pH = 6. d
d) Solution 4 iis 30% H2O2 ‐Set the spectrometer
s
r to follow
w the aabsorbance of o the solutio
on at 450 nm n and aacquire data of Absorban
nce vs. time, for 99 sscans at 5 se
econd intervals (your insstructor w
will show you how to set up the sspectrometerr). The instru
uctor will also
o blank tthe spectrom
meter for you
u, using cleaar MES b
buffer where
e the Abs re
eading needss to be zzero. Do thiss step before
e moving on. Note tthat only for the first testt tube do you need aan Amax reading and thus need to waait until tthe end of the reaction
n (Figure 3)). For ssubsequent test t
samples,, you only need n
to rrecord data for f about 10 scans, becau
use you aare just intere
ested in obtaining the inittial rate Figgure 3) Plot off absorbance vvs time in a kin
netic run of thee OPD–
(Figure 3). The initial reaaction velocitties will HR
RP system. Noote the initial rrate can be ob
btained from th
he slope of b
be determin
ned from the t
slope of o the thee linear portionn of this plot. Adapted from [2]. A
Absorbance vs. v time plo
ots for data points b
between abou
ut 20 and 35 seconds. Th
he mixing time and the nu mber of dataa points used to calculate the slope ccan vary, but it is very impo
ortant the po
oints used are
e within the li near portion of the plot! B
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*To make this faster, since we have to share a spectrometer: obtain initial Absorbance ratings for test tube 1, stop scanning after about 10 scans (take cuvette out). Then, run all the other samples while you are waiting for sample 1 to react and reach Amax. You can obtain the final reading for Amax after you’ve run your other 4 samples, and even let other groups go. It takes a while for the reaction to reach the full maximum Absorbance level (>> 5 minutes). Using pipettes, add the appropriate amount of each solution to a test tube (see Table 1). (We will use 15 mL Falcon tubes) You will add sol. 1 and sol. 2 first, then wait to add soln. 3 and 4 until immediately before you are ready to use the spectrometer. Mix and transfer the solution into the provided cuvettes (2/3 full).* Initiate data collection approximately 20 seconds after mixing the substrate and enzyme solutions. You will have to do your pipetting of sol. 3 and 4 very quickly, plus your mixing and your transferring to the cuvette. Record your data of Absorbance vs. time in your lab notebook. The Abs readings will be in the upper right hand corner of the spectrometer display screen. (When the data collection is complete, you will choose the initial portion of the Abs. vs time data and fit it to a straight line.) Also, for test tube 1, record the absorbance at the end of the experiment (Amax). Table 1. Amount of solution needed for each test tube sample.
*Important note: Do not add solution 3 and 4 till the very end, immediately before you initiate data collection. Notice that you need to add μL of H2O2, NOT mL! Data Analysis In your lab report, which will be individually‐executed for this lab, you will need to show clear calculations for steps in the Data Analysis section. Analysis should be conducted in MatLab whenever possible; MatLab annotated code should be provided in the lab report. 1. The initial slopes of the Abs. vs time plots yield an initial rate for the reaction in units of Abs/sec. You need to convert this to units of M/s (M = molarity of DAP). To convert the absorbance of DAP into its molarity you need the "ab" constant for DAP (Beer's law). The ab constant is also known as the extinction coefficient. You can determine the ab constant by assuming that all of OPD in test tube 1 is converted to DAP at the end of the reaction. Thus, by using the final absorbance of DAP and the initial concentration of OPD in test tube 1, you can BIOEN337
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determine the ab constant for DAP. Don't forget the stoichiometry of the reaction (Figure 2)! The initial reaction velocity (in Moles/s) is then computed from the slope divided by the ab constant [2]. 2. Calculate the concentration of the substrate, OPD, in each of the five runs 3. Make a plot of the initial rate of the reaction (units of M/s) vs. [OPD]. 4. Make a Lineweaver‐Burk plot and extract KM, Vmax, and k0 from the plot. A sample plot is provided in Figure 4. Figure 4) A sample Lineweaver‐Burk plot. This plot is used to determine kinetic parameters. Adapted from [2]. Refer to your BIOEN 335 text, section 10.4 for more information [1]. So now you’ve used your own group’s data to determine values for the kinetic parameters of this enzymatic reaction. You’ve characterized the system based on the experimental values your group obtained. 5. Now, you will determine how noise in data affects system analysis, by incorporating experimental data obtained by your classmates into your analysis. Use data from the other groups in your lab section to follow the same steps as in parts 1‐4 for each new data set. Provide a table showing initial rate and [OPD] for each of the distinct data sets you analyzed (everything in one table so reader can do a quick comparison of values obtained in each run). Statistically analyze the calculated values of KM, Vmax, and k0 obtained from each data set. What is the standard deviation for each kinetic parameter? Is there a large variation in parameter estimation? What does this indicate about using only one data set (just from your group) to characterize a system? Next, investigate the effect of combining the data before calculating parameter values. I.e. your Lineweaver‐
Burk plot will be constructed based off of multiple data points at one [OPD] value, instead of one point per [OPD] as seen in Figure 4. (Lumping data before regression) How does your calculation of the kinetic parameters with this method compare to those values calculated in part 4? What is the impact on the coefficient of determination (i.e. R2 value) of your regression line in the Lineweaver‐Burk plot? 6. This part involves using the trends of this original, experimentally‐derived data set to generate a synthetic data set. This will help you understand how noise in data can affect analysis of the system. Using Matlab and the known deviation of your data (as established in part 5), you will use that variation to generate a synthetic data set, and calculate values for Km and Vmax. You can generate synthetic data using a random number generator in MatLab, adding or subtracting a random deviation value from the mean of the real data set, in order to generate another (synthetic value). The constructed new data will be fed into MatLab Enzkin BIOEN337
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(http://www.mathworks.com/matlabcentral/fileexchange/26653‐enzkin), which takes the inputs of concentration and initial velocity and outputs values for Km and Vmax [3]. Hint: the synthetic data set should be constructed by adding or subtracting a number from the experimental mean, which is random but with a normal distribution. You may want to use the MatLab function randn (for a normal distribution). Randn generates a random number from a standard normal distribution, with a mean of 0 and σ = 1. (σ = standard deviation.) You may want to do something like the following, to account for the actual mean of your data plus its standard deviation: Example: Generate values from a normal distribution with mean 1 and standard deviation 2: r = 1 + 2.*randn(100,1); Using the σ obtained from real experimental data will mean that you’re covering most of the “likely” data points, at least as indicated by the distribution of your class’ data, and your synthetic data will be indiscernible from actual data. You should be able to use a MatLab loop to generate the new data, use Enzkin to fit that data and calculate your enzyme kinetic parameters for each data set. Then, at the end, do a statistical analysis on all those calculated parameters. How much do they vary (i.e. what is the standard deviation, or standard error of the mean (SEM))? You can generate as many synthetic data sets as you’d like, from 20 to 1,000 to 10,000! (But, we want to see at least 20). References 1. Truskey, G.A., F. Yuan, and D. Katz, Transport Phenomena in Biological Systems. 2004, Upper Saddle River, New Jersey: Pearson Prentice Hall. 2. Hamilton, T.M., A.A. Doble‐Galuska, and S.M. Wietstock, The o‐Phenylenediamine‐Horseradish Peroxidase System: Enzyme Kinetics in the General Chemistry Laboratory. Journal of Chemical Education, 1999. 76(5): p. 642‐644. 3. Cardillo, G. MatLab File Exchange: Enzkin. 2010 Feb 2010 [cited 2012 Feb. 6]; Available from: http://www.mathworks.com/matlabcentral/fileexchange/26653‐enzkin. BIOEN337
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