This week you will complete a worksheet to prepare yourself for evaluating the most appropriate protein concentration method for the class to use for calculating specific activity. The worksheet and rubric are found on the D2L Main Page.

Evaluation of Protein Assays

To prepare for our evaluation of protein concentration methods, respond to the following prompts by number in the worksheet document found on the D2L Main Page. Simply answer the questions. You are not expected to write a Results and Discussion.

1. Present Figure 1, a scatter plot of the data from all three standard curves with trendlines.
• Please plot all three standard curves on the same graph to economize space. If you are unable to do this for technical reasons, we will accept three separate plots.
• Make sure that the caption clearly describes what is plotted by identifying the protein standard and wavelengths measured. The caption should also include the equations of the trendlines and the R² values (these should not be on the plot). 
• You should make each dataset a different shape in Excel so that your legend can be interpreted if the figure is printed in greyscale.

2. Briefly describe the quality of the trends in Figure 1. If all of the trends were good, then you only need to write one sentence. If one or more trendlines are significantly more correlated than the others, you may wish to compare the relative coefficients of correlation (R²). You must disclose and justify any outliers that were dropped. If the data were poor, provide a specific explanation that would generate such results. Read the section Omitting Data in the Writing Guidelines for more details.

3. Present Table 1, a compilation of your calculated values for the measured protein concentrations for all three colorimetric methods and your UV-Vis measurements in μg/μL. The table must include:
• Calculated concentration for each mushroom extract sample for each of the four methods.
• Below each set of replicates, include a final row for the mean of the concentrations with a standard deviation.
• Calcuated concentration results for the reference protein for each of the four methods.
• Below each set of replicates, include a final row for the mean of the concentrations with a standard deviation, if you measured replicates.

4. Compare the precision of the four protein concentration results in Table 1 . Judge the best method using the relative standard deviation (RSD)—the standard deviation divided by the value times 100%—as the basis of your criteria. You are free to define your own acceptance criteria. That said, <10% RSD is suitably precise for most applications in biochemistry. Read the section Precision in the Writing Guidelines for more details.
5. Compare the accuracy of the four methods. Evaluate which method(s) correctly measured the concentration of the papain reference protein (0.30 μg/μL). Establish your criteria and apply them to your results. If you were unable to obtain data on a reference protein, infer accuracy from the similarity of your four protein concentrations. For example, if three methods gave similar results and one gave a value that was significantly larger, you could argue that the outlier method is the probably the least accurate. When comparing statistically identical results, you may wish to consider precision. Read the section Accuracy in the Writing Guidelines for more details.
6. Define your criteria for selecting the ideal protein assay for our application. Integrate information about accuracy and precision from prompts 1–5 to evaluate the four methods against your criteria and make a judgement to select one.
• You should consider both the accuracy and precision of your results. 
• You should prioritze your critieria by ranking the parameters you are weighing based on their relative importance to the specific application. Some criteria may be more critical than others depending on the context. For example, in a clinical diagnostic setting, sensitivity followed by specificity might be paramount, while in a research context, ease of use and cost-effectiveness could be more crucial.
• You should also establish acceptance criteria by defining acceptable thresholds, ranges, or rankings (e.g. highest or lowest) for each performance parameter. These acceptance criteria will serve as benchmarks against which different assays will be evaluated.
• Keep in mind that a poor standard curve is going to provide a result with lower precision your standard deviation might suggest (we have not yet learned how to factor a poor R² into our experimental error value).
• There is no "correct" answer; you will be graded on your ability to define criteria that consider the performance requirements of the application and evaluate your data using those criteria to make a judgment.

 

Submit by next lab meeting

• Your Excel spreadsheet demonstrating your calculations of slope and intercept from the standard curves, protein concentration, and statistical analysis (mean and deviation). These calculations should be clearly organized and labeled. Highlight the cells for each requested calculation in yellow to make it easier to grade.

• A printed copy of Assignment 3 (responses to the six prompts on the worksheet) with the Rubric stapled to the top.

• A digital copy of Assignment 3 (responses to the six prompts on the worksheet) to the Submissions folder.

• Do not post any of your results to the online spreadsheet before the next lab meeting.