Proteins as Essential Nutrients - Lab Report Example

?Proteins, lab report Proteins are essential nutrients and are therefore significant to the body, a factor that identifies the need to understand their presence in substances for nutritional, and other, purposes. An understanding of protein levels in the body is also important in determining changes from equilibrium fluid compositions. There are many ways in which protein concentrations in substances, particularly solutions, can be determined. The most common approach is the application of a regression analysis model to develop a relationship between protein concentration in a solution and absorbance rate that can be experimentally determined. The developed relationship can then be used to determine concentrations of proteins in substances, under similar conditions, based on the absorption rates in proteins from those solutions. This paper explores the use Bradford approach to determination of protein concentration. In order to use the approach to determine the concentrations, the paper tests the null hypothesis that there is no significant relationship between concentration of protein solutions and absorption rates. Applied research method involved observation of experimental result from the Bradford approach to establish, analytically, existence of a relationship. The relationship is then used to determine concentration unknown proteins solutions. Analysis of the results identifies existence of a significant relationship. This is because of the small p value (p value= 0.000394) relative to the level of significance, 0.05. The paper therefore concludes validity of the method but identifies errors that lead to inconsistent concentrations for the unknown solutions. Introduction and background information Protein quantification is an important biological concept with a wide scope of applications. This is majorly because proteins form fundamental structures of living cells and organs. This also identifies the need for understanding their importance in the body as well as the need for protein based nutrients. Identification of a specific level of need for protein components is for example important to nutritionists who need to know the recommended amount of proteins that an individual should take, a scope that identifies the need to understand protein concentration of different substances for recommendation of appropriate quantities that can be consumed. Knowledge of protein concentration can similarly be applied in health care environments to determine presence of diseases in a body (Chem, p. 105). The Bradford’s approach to determination of protein concentration is one of the applicable techniques that is easy to apply and is time efficient (Mnstate, p. 1). It employs the spectroscopy technology that uses light waves to determine concentration. The basis of application of the approach is absorption of a dye into protein (Clarke, p. 1). The absorbed dye, whose concentration varies directly with the concentration of the absorbing protein then, forms the basis of determining the relationship between protein’s concentration and absorbance property (Cuntapay, p. 1). The basis further relies on change in visibility of the used dye to determine the level of absorbance (Ruf, p. 1). The method’s determination of the concentration of an unknown protein is further based on the principle that given similar conditions, such as the solvent and factors such as temperature, concentration of protein in a solution is the sole determinant of absorbance property. As a result, different proteins with similar concentrations will exhibit the same absorption rate if all other factors are constant. This further means that any other protein under the same conditions obeys a developed theory, in an equation of relationship between protein concentration and absorption rate (Thermo, p. 1). Such an equation is determined by regression analysis that establishes a relationship between protein concentration and absorption. The analysis, will in this case determine the rate of increase of absorption rate with each unit increase of protein concentration as well as the absorption rate when protein concentration is zero. With these two values, a determined absorption rate from and experimental set up of a protein solution can be used to determine protein concentration (Taylor, p. 1- 10). Effectiveness of the Bradford approach is established by its application in advanced research initiatives such as Ghawi et al research on effects of starvations on antithrombotic functionality (Ghawi et al, p. 1). This paper investigates the relationship between concentration of protein in solutions and absorption of dye. It aims at using the established relationship to determine protein concentration of unknown solutions. Determination of existence of such a relationship required a test of the following set of hypotheses. The null hypothesis, H0: There is no significant relationship between protein concentration and absorption indicator And the alternative hypothesis that H1: There exist a significant relationship between protein concentration in the solutions and their corresponding levels of absorption. Materials The experiment used the following materials: cuvettes 13*100 mm test tubes test tube rack vortex mixer pipettes spectrophotometer set to 595 nm BSA standard curve dilutions – using micropipettes, different BSA standard concentrations as indicated. Diluted dye reagent. Procedure Six test tubes were taken and labeled for identification. Different concentrations of Bovine serum albumin were then prepared by adding specified amount of distilled water. The test tubes, together with other three test tubes with substances of unknown protein concentration were then assembled and 5 millimeters of the standardized reagent added to each test tube. The components of each test tube were separately mixed, after identification of the dilution factors for the unidentified protein solutions. The tubes were then maintained at room temperature for about five minutes before starting the computer system for recording absorption. Care was taken to ensure that all components of the computer system were appropriately fixed. The system was then stopped after 10 seconds and absorption readings taken. Results The following table shows the results of the experiment for the six test tubes that contained solutions of known protein concentrations. test tubes final standard protein concentration Absorbance at 610 nm 1 900 1.863 2 699.84 1.561 3 499.68 1.49 4 349.92 1.318 5 200.16 1.164 6 0 0.827 The following table also shows the absorbance for the three substances with unknown concentration levels. Absorbance at 610 nm dilution factors 2%milk 1.365 1/40 Protein drink 1.318 1/80 Soy milk 1.561 1/20 Discussion The results identify a direct relationship between the protein concentrations and absorbance levels. This suggests a correlation factor that can be used to predict the unknown concentrations of the three solutions in the experiment. A clearer relationship can however be established through a regression analysis that test the experiment’s hypothesis. The table bellow shows the summary statistics for the regression analysis model. Regression Statistics Multiple R 0.983742 R Square 0.967749 Adjusted R Square 0.959686 Standard Error 0.071492 Observations 6 The high value of R square, 0.9677, shows that the regression model is consistent to, and therefore explains, a large percentage of the experimental data. The model is therefore suitable for predicting relationships between absorbance and protein concentrations, from the experiment. Such relationships can however be either due to chance or due to the experimental treatment. The following table, for analysis of variance, communicates significance of the relationship between the two variables, protein concentration, and absorbance. This is because of the high value of F, 120.027, as compared to the critical F value, 0.000394. ANOVA   df SS MS F Significance F Regression 1 0.613473 0.61347 120.0274 0.00039432 Residual 4 0.020444 0.00511 Total 5 0.633918       This leads to a rejection of the null hypothesis that there is no significant relationship. Results of the analysis of variance therefore concludes that a significant relationship exist between the variable. The exact relationship between the variables is however determined by the following table of coefficients.   Coefficients Standard Error t Stat P-value Intercept 0.90064 0.051876 17.3613 6.46E-05 X Variable 1 0.00106 9.71E-05 10.9557 0.000394 From the table, both values of the intercept and the x coefficient are significant because of their low probability values, 0.0000646 and 0.000394, relative to the experiment’s level of significance of 0.05. The relationship between protein concentration and absorbance therefore obeys the following regression analysis equation. y=0.9+ 0.001x, Where y is absorbance and x is the protein concentration. Therefore x= 1000y-900 The graph for the relationship is expressed bellow. From the above equation, the absorbance of the three substances with unknown concentrations can be used to determine the solution’s concentrations as shown bellow. 2% Milk Concentration= 1000(1.365)- 900 = 1365-900 =465 mg/ml Protein drink Concenttration= 1000(1.318)-900 =1318-900 =418 mg/ml Soy milk Comcentration= 1000(1.561)- 900 =1561-900 =661 mg/ml The table bellow compares the actual and experimental concentrations for the 2 % milk, protein drink and the soya milk. Actual (µg/ml) Experimental (µg/ml) 2 % milk 36000 465000 Drinking protein 140000 418000 Soy milk 27000 661000 The experimental values are very high, compared to the actual values of protein concentrations of the unknown solutions. The ordered magnitudes of the substances’ experimental concentrations are also inconsistence with those of the actual concentrations. This identifies possible experimental errors at different points of the experiment such aspreparation of specific protein concentrations for application, and inaccurate reading of absorbance values. These could also have led to determination and application of inappropriate regression model. Such errors can however be avoided through accurate implementation of research procedure and taking readings accurately. Reference list Chem. Quantification of protein concentration. N.d.Web. October 25, 2012. Cuntapay, G et al 2011, Bradford method for determination of protein concentration. N.d. Web. October 25, 2012. Ruf 2012, Bradford protein essay. N.d. Web. October 25, 2012. Taylor, Jonathan. Introduction to applied statistics, simple linear regression, Stanford University. January, 28, 2009. Web. October 25, 2012. Thermo. How to use a protein assay standard curve, Thermo Scientific. 2012. Web. October 25, 2012. Clarke, Christopher. Determination of protein concentration by the Bradford assay. The Medical University of South Carolina. November 16, 2009. Web. October 25, 2012. < http://www.musc.edu/BCMB/faculty/HOLab/Protocols/files/Bradford%20Protein%20Assay.pdf >. Mnstate. Bradford protein assay. Minnesota State University. October, 2006. Web. October. < http://web.mnstate.edu/provost/bradfordproteinassayprotocol.pdf >. Ghawi, Abbas, et al. Season variation period influence on the antithrombotic activity of leech saliva extract from the medicinal Malaysian leech, Hirudinaria manillensis. OMICS Group Conferences. March, 2012. Web. October 25, 2012. < http://www.omicsonline.org/0975-0851/0975-0851-S1.06-001.pdf >. Appendix The experimental data for known protein concentrations test tubes final standard protein concentration Absorbance at 610 nm 1 900 1.863 2 699.84 1.561 3 499.68 1.49 4 349.92 1.318 5 200.16 1.164 6 0 0.827 Experimental data for the unknown solutions dilution factors A(2%milk) 1.365 1/40 B(Protein drink) 1.318 1/80 c (Soy milk) 1.561 1/20 Read More