Ruby Haryanto
Introduction
In a biology class last year, I participated in an experiment focusing on enzyme inhibition. We utilized copper sulfate as an inhibitor and quantified changes in gas production as we varied the inhibitor's concentration. This practical experience piqued my interest in enzymatic reactions and inhibition dynamics. Inspired by this experiment, I decided to explore another facet of enzyme inhibition, specifically investigating the impact of pH levels on competitive inhibitors' interactions with amylase enzymes during starch digestion. By using a variety of levels of pH, I came up with the research question: How does the manipulation of pH levels (pH 4-8) affect the interactions between maltose and amylase enzymes during the hydrolysis of starch?
Background Information
Enzymes are biological catalysts that facilitate and accelerate chemical reactions within organisms without being altered in the process. One of them is the enzyme amylase, responsible for the digestion of starch, found in the salivary glands, small intestinal mucosa, and in the liver (Gurley). Amylase yields with maltose, which is later further broken down into two glucose molecules. By breaking the starches into smaller and simpler glucose molecules, it is easier to absorb into the body. However, since enzymes are responsible for the regulation and guidance of cell metabolism, they need to be carefully controlled. Enzyme activity can be turned down by specific activator and inhibitor proteins either competitive inhibitors, or noncompetitive inhibitors.
Maltose is a competitive inhibitor, a molecule that impedes enzyme activity by binding to the active site, the region of the enzyme where substrates typically bind to initiate a chemical reaction. This inhibition occurs when the inhibitor closely resembles the enzyme's natural substrate and competes with it for access to the active site. In the context of amylase, competitive inhibitors like maltose come into play. Maltose, a disaccharide composed of two glucose units linked together, structurally resembles a portion of
the starch molecule (National Library of Medicine). As a result, maltose can effectively bind to the active site of amylase, mimicking the substrate-starch interaction. This competition at the active site leads to a decreased rate of starch hydrolysis into simpler sugars, as maltose molecules occupy the active sites, limiting starch's access. While amylase's activity is not completely halted, it operates at a reduced rate in the presence of competitive inhibitors like maltose.
Starch indicator, a widely employed biochemical tool, plays a pivotal role in various analytical procedures, particularly in the determination of iodine levels and starch presence. Particularly relevant to investigations involving amylase activity and starch breakdown, the intensity of the purple coloration directly correlates with the concentration of starch present. Starch indicator solutions consist of a starch derivative which forms a complex with iodine to yield a purple coloration. This color change occurs due to the formation of a starch-iodine complex, wherein iodine molecules become entrapped within the starch molecules, resulting in the absorption of light in the visible spectrum. As amylase enzymes catalyze the hydrolysis of starch molecules, leading to their breakdown into smaller components, the reduction in starch concentration consequently results in a gradual lightening or clarification of the purple coloration.
Furthermore, the intensity of the coloration correlates with the concentration of starch present in the solution, enabling quantitative analyses of starch content with the use of a colorimeter. Starch indicator solutions exhibit optimal performance in neutral to slightly acidic environments, with pH levels typically below 8 (Ricca Chemical Company). At higher pH levels, particularly above pH 8, the starch indicator becomes ineffective, leading to a loss of coloration. Maltose has an optimal pH of 6.5, and are generally stable from pH 5 to pH 7. Deviations from optimal ranges can lead to reduced activity in organic molecules. Changes in pH can also influence the charge and conformation of both enzymes and inhibitors, impacting their binding affinity as the binding sites would change. As a result starch digestion might be less inhibited due to a reduced inhibitor effectiveness at pH levels out of the optimal range. With these considerations in mind, my hypothesis is that as the pH level increases within the experimental range, the absorbance values recorded by the colorimeter will increase as well. The aim of the experiment is to investigate the influence of pH levels on the effectiveness of maltose as a competitive inhibitor in the enzymatic breakdown of starch, aiming to elucidate the pH-dependent modulation of maltose functionality.
Variables
Independent Variable: pH level of the reactions mixture (pH 4, pH 5, pH 6, pH 7, pH 8).
Changes in the pH level directly impact enzyme activity by altering the charge and conformation of the enzyme and its
substrates. As the pH level deviates from the optimal range, enzyme activity may decrease, affecting the rate of starch hydrolysis. Therefore, variations in pH levels serve as indicators of enzyme activity, influencing the absorbance values recorded by the colorimeter.
Dependent Variable: Change in color, measured with a colorimeter.
The colorimeter detects alterations in the absorbance of light, which corresponds to changes in the concentration of the starch indicator present in the reaction mixture. As the amylase enzyme digests starch, the concentration of the starch indicator decreases, leading to a reduction in the purple color intensity observed in the reaction mixture. Consequently, the absorbance rate measured by the colorimeter decreases, reflecting the progress of starch
digestion and indicating enzyme activity.
Controlled variables:
Materials & Apparatus
● 100 ml of 0.1M Starch Solution
● 50 ml of 0.0004M Amylase Solution
● 25 ml of 0.01M Maltose Solution
● 100 ml of Buffer Solutions
● 500 ml Distilled Water
● 10 ml of Iodine Solution
● Pipette (± 0.01 mL)
● Weighing boat
● pH Meter (± 0.02 pH)
● Colorimeter
● Electric scale (± 0.1g)
● 250 ml beaker (± 5.0 ml)
● 3 100 ml beaker (± 5.0 ml)
● 100 cm3 graduated cylinder (± 5 ml)
Safety
Amylase, starch, and maltose are generally considered low-risk chemicals in a laboratory setting, however it is still important to be cautious. Wear safety goggles at all times to shield your eyes from chemical splashes, which could cause eye irritation, redness, or discomfort. In case of eye contact, rinse with water for at least 10 minutes and seek medical help in the rare occasion that irritation persists. In the event of skin contact, wash the area thoroughly with soap and water. If accidental ingestion occurs, seek
immediate medical attention and provide details about what was ingested. Post-experiment contents are safe to dispose of down the drain, but make sure to rinse the sink with water after to ensure cleanliness.
Procedure
Preparation of 1.00% (0.0004M) Amylase Solution:
1. Measure 1 gram of amylase powder using a laboratory balance.
2. Add the 1 gram of amylase powder to a clean 100 mL beaker.
3. Measure 100 mL of distilled water using a graduated cylinder.
4. Carefully pour the measured distilled water into the beaker containing the amylase powder.
5. Stir the mixture thoroughly with a glass stirring rod until the amylase is completely dissolved
Preparation of 1.62% (0.1M) Starch Solution:
6. Measure 1.62 grams of starch using a laboratory balance.
7. Add the measured starch to a clean 100 mL beaker.
8. Measure 100 mL of distilled water using a graduated cylinder.
9. Carefully pour the measured distilled water into the beaker containing the starch.
10. Stir the mixture with the use of a glass stirring rod until the starch is completely dissolved
Preparation of 0.342% (0.01M) Maltose Solution:
11. Weigh out 0.342 grams of maltose powder using a weighing boat and an electrical balance
12. Transfer the weighted maltose powder to a clean, dry 100 mL beaker.
13. Measure 100 mL of distilled water using a graduated cylinder and add it to the beaker with
maltose.
14. Stir the mixture until the maltose is completely dissolved, resulting in a 0.01 M maltose solution.
Preparation of Acidic and Basic Solutions:
15. Weigh 1.6 grams of acetic acid for each trial using a laboratory balance and transfer it to a clean,
dry 250 mL beaker.
16. Measure 100 mL of distilled water using a graduated cylinder and add it to the beaker with acetic
acid.
17. Stir until the acetic acid is completely dissolved.
18. Weigh 4.0 grams of sodium hydroxide for each trial using a laboratory balance and transfer it to a
clean, dry 250 mL beaker.
19. Measure 100 mL of distilled water using a graduated cylinder and add it to the beaker with
sodium hydroxide.
20. Stir until the sodium hydroxide is completely dissolved to create a basic solution
Calibrating the colorimeter:
21. Fill a cuvette with distilled water
22. Insert the cuvette into the cuvette slot within the colorimeter with the clear sides of the cuvette
facing the arrow
23. Set the wavelength to 430 nm
24. Tap the calibrate button for the calibration blank of 0 absorbance
Setting up Reaction Mixtures:
25. Get a test tube rack, and place 5 test tubes inside
26. Label each test tube with pH level and trial number (e.g., “#1 pH 4” “#2 pH 4” etc.)
27. Using a graduated cylinder, measure 3 ml of acidic solution and pour it into each test tube
28. Insert the pH probe into the first test tube
29. Add in the basic solution into the test tube dropwise until desired pH is acquired, or add more
acidic solution if necessary
30. Once desired pH is required, add 2 mL of the starch solution into each test tube, measured with
the use of a graduated cylinder
31. Measure 5 sets of 2 ml of the amylase solution, and the maltose solution
32. Add 2 ml of the amylase solution and add 2 ml of the maltose solution into each test tube
33. Start a timer for 10 minutes and occasionally swirl the test tubes
34. Using a graduated cylinder, measure 1 ml of iodine solution, and add to each test tube
35. Swirl the test tubes gently to ensure even distribution of iodine solution
36. Take a clean cuvette and fill will a sample from test tube 1
37. Put the cuvette filled with the reaction sample from test tube 1 into the colorimeter
38. Record the absorbance values for tube 1
39. Rinse the cuvette thoroughly and dry with paper towel
40. Repeat steps 36-39 until 5 total trials are recorded.
41. Repeat steps 15-38 for all 5 specific pHs until all 25 trials are complete
Data:
Raw data:
Processed data
Error bars were determined to depict the specific standard deviation for each absorbance value corresponding to their respective pH levels. These bars indicate the dispersion of data points relative to the average absorbance values. Smaller standard deviation error bars suggest greater reliability compared to datasets with larger error bars. This is because a smaller standard deviation signifies a lower dispersion within the data collected, meaning that the data is clustered around the mean. In the presented table, the error bars are smaller and also exhibit no overlap, suggesting significance in my data collected, and its
high reliability.
Anova:
Single Factor:
The analysis of variance (ANOVA) test is a statistical method used to compare the means of three or more groups to determine if there are statistically significant differences between them. The null hypothesis for ANOVA states that there are no significant differences among the group means. The ANOVA test assesses whether the observed variability between group means is greater than the variability within groups, considering the random variability inherent in the data. In the provided ANOVA results, the between-groups analysis demonstrates a significant sum of squares (SS = 0.5897256) with 4 degrees of freedom, indicating substantial differences among the groups. The F-statistic (F = 285.1560868), comparing the variability between groups to the variability within groups, significantly exceeds the critical F-value (Fcrit = 2.866081402), providing evidence against the null hypothesis. Additionally, the p-value associated with the F-statistic is extremely small (p=0 < 0.001), suggesting a highly significant difference between at least two of the groups. These results support rejecting the null hypothesis, indicating that the manipulation of pH levels significantly influences the interactions of competitive inhibitors with amylase enzymes during the breakdown of starch. Further analysis is warranted to identify the specific group differences and their implications within the context of this investigation.
Conclusion
Exploring the effects of pH on the color absorbance value in a sample of the maltose-inhibitor reaction mixture, the collected data conclusively demonstrates a consistent decrease in the pigmentation of the iodine attached to the starch, recorded through the colorimeter. As the pH of the solution increased the absorbance value of the pigment recorded by the colorimeter decreased, causing an inverse relationship between these two variables. Central to the experimental setup was the role of maltose as a
competitive inhibitor. By reversibly binding to the active site of the amylase enzyme, maltose competes with substrate molecules for starch binding and subsequent digestion. The observed decrease in absorbance values with increasing pH levels suggested a reduction in the inhibitory potency of maltose on amylase activity. This phenomenon implied that as pH increased, the effectiveness of maltose as a competitive inhibitor diminished, thereby allowing for greater starch digestion by amylase. At pH 4.00,
the average absorbance value peaked at 0.4524 ± 0.00005, contrasting sharply with the lowest value recorded at pH 8.00, measuring 0.0374 ± 0.00005. This difference resulted in a percentage difference of 91.7% between the lowest and highest pH values, indicating a significant impact of pH on absorbance values. Therefore, these findings show the pH-dependent modulation of enzymatic activity, wherein increasing pH diminishes the efficacy of maltose. In addition to the observed pH-dependent modulation of enzymatic activity, the results of the ANOVA test further support the conclusion drawn from the experimental data. The significant difference in absorbance values across varying pH levels, as indicated by the ANOVA results, supports the impact of pH on the effectiveness of maltose as a competitive inhibitor of amylase. Therefore, it can be concluded that pH does indeed have an effect on the activity of maltose as a competitive inhibitor. As the pH increases, the effectiveness of maltose increases, while conversely, as the pH level decreases, the effectiveness of maltose decreases.
Evaluation
The experiment's reliability is significantly enhanced by the precise utilization of small pipettes and graduated cylinders. Specifically, the graduated cylinder's intricate volume graduations allow for meticulous measurement and dispensing of acidic or basic solutions, ensuring precise pH adjustments within the reaction mixture. Additionally, by employing small pipettes to transfer liquids into the graduated cylinder, rather than pouring directly from the beaker, any potential errors due to imprecise pouring techniques are mitigated. This methodical approach allows for accurate pH levels critical for investigating the effects of pH variation on maltose activity. In the context of studying the influence of pH on maltose's inhibitory potency, such precision is paramount. Deviations in pH measurements could introduce inaccuracies, jeopardizing the validity of conclusions drawn regarding maltose's efficacy as a competitive inhibitor at different pH levels. Competitive inhibitors, which interfere with enzyme activity by binding to the active site, are susceptible to pH variations. Changes in pH can alter the ionization states and structural conformation of both the enzyme and the inhibitor molecule, thereby affecting their affinity for each other. This alteration in pH may disrupt the competitive binding process, influencing the inhibitor's ability to effectively block the enzyme's active site. Conversely, enzymes themselves are highly sensitive to pH changes, as variations in pH can affect their three-dimensional structure and the charge distribution within their active sites. This can directly influence enzyme-substrate interactions and subsequently impact reaction rates. Thus, the careful use of small pipettes and graduated cylinders not only ensures the integrity of experimental data but also allows for robust analysis of the pH-dependent modulation of maltose activity in competitive inhibition.
Additionally, it is essential to note the limitations encountered during the experimental design. While efforts were made to explore a wide spectrum of pH levels, the practical constraints imposed by the iodine indicator's working range necessitated a focus on lower and more acidic pH values. The decision to narrow the pH range was a response to significant challenges faced during the experimental phase. Initially, the experiment aimed to comprehensively explore a wide pH spectrum ranging from 4 to 12. However, as the experimentation progressed, it became increasingly apparent that the starch indicator posed unforeseen limitations. Specifically, at pH levels at 10 and above, the starch indicator exhibited a disconcerting tendency to rapidly lose its characteristic blue/purple coloration, observed even before the enzymatic reactions commenced. Upon further research, it was revealed that the purple color produced by the starch indicator is also prone to destruction by alkali and heat (Ricca Chemical Company). Therefore, experiments involving the starch indicator should ideally be conducted at a pH below 8 and at temperatures near room temperature. In response to these findings, adjustments were made to confine the pH range to a narrower span, typically represented by pH values of 4, 5, 6, 7, and 8. While narrowing the pH range for investigation facilitated the functionality of the starch indicator, it also limited the breadth of pH levels explored. As a consequence, the data exhibited minimal differences between adjacent pH values, constraining the depth of analysis across the pH spectrum.Having data points that exhibit minimal
differences poses a limitation because it reduces the granularity of the analysis and may obscure meaningful trends or patterns. In experimental research, data points with small variations limit the ability to discern significant effects or relationships between variables.This reduction in variability may have impacted the granularity of insights gleaned from the experiment. Therefore, consideration should be given to utilizing an alternative indicator that demonstrates stability and reliability across a wider range of pH levels, thereby enabling a more comprehensive analysis and understanding of pH-mediated effects on enzyme activity and inhibitor interactions.
The consistent decrease in absorbance levels with rising pH levels challenges the anticipated outcomes based on the optimal pH ranges for maltose and amylase. Traditionally, maltose exhibits peak efficiency at pH 6.5, while amylase functions optimally at pH 7. However, the observed consistent decrease in absorbance values with increasing pH, rather than an expected decrease followed by an increase after reaching the optimal pH, suggests potential sources of error within the experiment. During the experimental procedures, it was observed that many pH probes did not have the correct calibration. Standard practice dictates that pH probes should be stored in a solution with a pH of 2 for proper calibration, yet deviations from this standard were frequently noted. This inconsistency in calibration solutions could have led to inaccuracies in the recorded pH values. This limitation could be alleviated by ensuring the proper propagation of the pH probes prior to the experiment, as the realization of the miscalibrations occurred after the experiments. The regular verify and recalibrations of the pH probes at the pH 2 solution for calibration accuracy, therefore the accuracy of the data points measure. Additionally, the pH meters were consistently returned to their identical boxes after each use and placed into a pile at random, which, coupled with the aforementioned calibration discrepancies and the potential mixing up of which meters were used, could have further compromised the reliability of the pH measurements and impacted the accuracy of the experimental data. Furthermore, due to the simultaneous execution of five trials, it was often the case that while one trial was being measured, others surpassed their designated reaction times. This concurrent operation may have led to variations in reaction times among trials, potentially affecting the reliability and consistency of the experimental data. Therefore, the combination of these factors could have contributed to discrepancies in the measured pH values, consequently impacting the accuracy and validity of the experimental results. The lack of consistency could be improved through the development of protocol to ensure a decreased amount of mix-ups. Specific pH probes could be assigned to individual students to ensure consistency even if a probe is not calibrated properly. While the pH measurements may be inaccurate, having consistent calibration ensures that the trend remains clear. By pairing assigned probes with regular calibration, the reliability of the data can be improved, minimizing errors associated with varying calibrations. This approach enhances the accuracy and validity of experimental results, providing a more reliable basis for analysis and conclusions.
Another significant challenge encountered in the experiment is the inconsistency in temperature. Variations in temperature directly impact the kinetic energy of molecules, which in turn affects the frequency of collisions and enzymatic activity. Specifically, higher temperatures lead to increased kinetic energy, resulting in more frequent collisions between enzyme and substrate molecules. Conversely, lower temperatures reduce collision frequency, altering reaction rates and subsequently impacting the observed outcomes. Despite efforts to maintain consistency by conducting the experiment in the same room, the room's temperature exhibited considerable variability. This fluctuation was primarily attributed to the operation of the air conditioning system. Newton's Law of Cooling states that an object's temperature is influenced by that of its surroundings. Consequently, when the air conditioning was turned on or off, the room experienced fluctuations in temperature, affecting the solutions within. Consequently, solutions within the experiment were subjected to varying temperatures, potentially introducing bias and affecting the reliability of the data collected. To address this limitation, conducting experiments in a dedicated room
devoid of temperature fluctuations, ideally separated from regular teaching spaces, could provide a controlled environment with a constant temperature. Such an approach would minimize the influence of temperature-related variability on the experimental results, ensuring greater accuracy and reliability in data interpretation.
Acknowledging the encountered challenges and potential sources of error, it's noteworthy that the experiment still succeeded in yielding observable outcomes. Despite the noted inconsistencies in pH probe calibration and the potential mixing up of equipment, the experiment was able to provide valuable insights into the influence of pH on maltose activity and competitive inhibition. The observed trend of decreasing absorbance values with rising pH levels offers significant information about the pH-dependent modulation of maltose activity. Even though limitations, such as enzyme denaturation and practical constraints, were encountered, the experiment effectively addressed the research question and contributed to our understanding of competitive inhibition by maltose under varying pH conditions.
Extension
An extension to this experiment could involve investigating the effects of temperature on enzyme activity in the presence of competitive inhibitors, such as maltose. By varying the temperature of the reaction mixture, researchers can explore how changes in temperature affect the efficiency of enzyme inhibition by maltose and subsequent amylase activity.
Works Cited
Engelking, Larry. “Competitive Inhibition.” Science Direct, 2015, https://www.sciencedirect.com/topics/medicine-and-dentistry/competitive-inhibition. Accessed 2 October 2023.
Gurley, Bill. “Amylase | Definition, Function, & Facts.” Britannica, 2023, https://www.britannica.com/science/amylase. Accessed 2 October 2023.
National Library of Medicine. “Inhibitors of α‐amylase and α‐glucosidase: Potential linkage for whole cereal foods on prevention of hyperglycemia.” NCBI, 4 November 2020, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723208/. Accessed 2 October 2023.
Ricca Chemical Company. “Starch.” Ricca Chemical, 2018, https://www.riccachemical.com/pages/tech-tips/starch. Accessed 20 March 2024. Surtini, Rusty. “,.” , - YouTube, 6 May 2021, https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/maltose. Accessed 1 March 2024.
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