The experiment described below explored the same concepts as the one described in Figure 6.17 in the textbook. Read the description of the experiment and answer the questions below the description to practice interpreting data and understanding experimental design.
Mirror Experiment activities practice skills described in the brief Experiment and Data Analysis Primers, which can be found by clicking on the “Resources” button on the upper right of your LaunchPad homepage. Certain questions in this activity draw on concepts described in the Data and Data Presentation primer. Click on the “Key Terms” buttons to see definitions of terms used in the question, and click on the “Primer Section” button to pull up a relevant section from the primer.
Background
You may know the pancreas as the site of insulin secretion, but this organ also produces enzymes that play a role in digestion. As you have learned in Figures 6.17 and 6.18, spectral analyses and radiolabeling experiments have demonstrated that enzymes can bind to substrates; however, this work yielded few clues to the physical shape of the enzyme-substrate complexes that were studied. Do pancreatic enzymes also function by binding to protein substrates? If so, can researchers determine the shape of pancreatic enzyme-substrate complexes?
Hypothesis
Edgar Meyer and colleagues hypothesized that pancreatic enzymes are able to form complexes with protein substrates. They also sought to determine the three-dimensional conformation of the enzyme-substrate complexes formed by these enzymes.
Experiment
Researchers often use one of two methods to determine the shape of a protein: X-ray crystallography or nuclear magnetic resonance (NMR). As discussed in Figure 4.5, scientists using X-ray crystallography first produce a crystal of their target protein, then expose this crystal to X-rays. The crystalized protein generates a specific pattern of reflected X-rays, which can be visualized on film (in much the same way as your bones can be visualized in an X-ray). NMR is carried out in a similar manner although the protein does not have to be in crystalline form; however, the protein is exposed to magnetic fields instead of X-rays. In both cases, data collected from X-ray crystallography and NMR experiments help researchers predict the three-dimensional shape of a protein.
Edgar Meyer and colleagues chose to study pig elastase, an enzyme produced by the pancreas; they evaluated the ability of this enzyme to bind to a hexapeptide substrate, a short molecule composed of six amino acids. In their first experiment, an elastase crystal was generated and submerged in a hexapeptide solution; X-ray crystallography was performed on the crystal. In their second experiment, solutions of uncrystalized hexapeptide and uncrystalized elastase were mixed together and NMR was carried out (Figure 1).
Results
Using both X-ray crystallography and NMR techniques, Meyer and colleagues determined that elastase is able to bind a hexapeptide substrate. Furthermore, researchers were able to visualize the three-dimensional structure of this elastase-hexapeptide complex.
Source
Meyer, E. F., Jr., et al., 1988. Analysis of an enzyme-substrate complex by X-ray crystallography and transferred nuclear Overhauser enhancement measurements: porcine pancreatic elastase and a hexapeptide. Biochemistry. 27, 725-30.
Chemical bond lengths are typically measured in units known as
angstroms (Å), where one angstrom is equivalent to one tenth
of a billionth of a meter. Meyer and colleagues noted that the
amino acids of hexapeptide formed hydrogen bonds with amino
acids present in the active site of elastase.
Data and Data Presentation
Processing Data
Initially, we have raw data—our series of observations or measurements. Before we move to the next level of data analysis and presentation, we often need to process the raw data in some way. Sometimes, for example, this may entail transforming a long string of numbers into a data table. To do this, we may need to categorize the data. For example, in our forest example, imagine that over a 24-hour period in our forest patch, we count 108 sightings of mammals. The first step is to categorize the sightings according to species and put the data in table form. In this case, we generate a frequency table in which we specify the number of sightings of each of six mammal species, A–F:
Species | A | B | C | D | E | F |
Number of sightings | 43 | 47 | 3 | 5 | 7 | 3 |
This table illustrates the pitfalls of data collection and how we have to be very careful when we design our data collection protocol. How valid are these data? We have seen B’s many times, but maybe each sighting is of the same individual. It is possible that all 47 B sightings were the same individual, whereas perhaps the three F sightings were three different individuals. This suggests that the design of our sampling scheme was flawed. We should re-do the census, only this time using traps that can mark each individual. Imagine that the revised method results in the following numbers:
Species | A | B | C | D | E | F |
Number trapped | 17 | 29 | 5 | 2 | 5 | 3 |