What Are the Flexibilities of a T Cell Receptor Protein at Its Binding Sites?

What Are the Flexibilities of a T cell Receptor Protein at its Binding Sites?

Edward Hunckler

Marian High School

1311 S. Logan St.

Mishawaka, IN, 46544

Acknowledgements

First, I would like to acknowledge the Baker Laboratory at Notre Dame for graciously providing lab space to accommodate my research. Also, Dr. Brian Baker, Ph.D., a professor at

Notre Dame, and Daniel Scott, a graduate student at the University of Notre Dame, have generously set aside valuable time to instruct and mentor my research while it was being conducted at Notre Dame. Furthermore, I would like to thank the Indiana Academy of Science for awarding a $227.71 Junior Research Grant to help defray the financial cost of the research.

In addition to the above mentioned, Mr. Andrzejewski and Dr. Sisk, my teachers at Marian High

School, have provided substantial encouragement and guidance throughout the duration of my research. Table of Contents

Introduction 1

Materials and Methods 4

Results 6

Discussion and Conclusion 8

References 10 List of Figures

Figure 1: T cell detecting p-MHC of target cell 1

Figure 2: Vertically polarized light is shot at the sample,

and light emitted is collected in vertical and horizontal intensities 6

Figure 3: Graph displaying the decay curves for rigid, moderate, and flexible sites 8

List of Tables

Table 1: Data for the curve fits of the three different samples 7 1

Introduction

The immune system protects the body from infections and illnesses. It recognizes foreign and natural cells in the body and attempts to destroy the foreign cells. One line of defense in the immune system involves the recognition of pathogens by T cells. As seen in Figure 1, T cell receptors (TCR) identify antigens that are presented by major histocompatibility complexes

(MHC) on the surface of pathogens and other cells of the body in order to identify the cell. The

TCR is a protein with two chains, α and β, and two regions, the constant and variable regions.

When the TCR binds to the peptide-MHC, the complementary determining region (CDR) on the

TCR bends in order to bind and recognize the unknown cell. If the identified cell is a pathogen, the “[r]ecognition of an antigenic peptide-MHC complex by an αβ T cell receptor (TCR) initiates an intracellular signaling cascade leading to a T cell response” (Borbulevych, et al. 885).

Scientist’s understanding of the immune system is improved by understanding the mechanics behind how the TCR binds with peptide-MHC.

Figure 1: T cell detecting p-MHC of target cell 2 The mechanics of this binding process includes the flexibility of the CDR loop. In order to measure this flexibility an indirect method must be used. Time resolved fluorescence anisotropy is one method that can be used to measure the flexibilities of these proteins. The flexibility of the CDR loop is revealed through the anisotropy measurements. “The anisotropy measurements reveal the average angular displacement of the fluorophore that occurs between absorption and subsequent emission of a photon” (Lakowicz 291). This process has several steps involved. The site that is going to be measured is tagged with a fluorophore through a mutation process. This fluorophore, a molecule that produces fluorescent light when excited with a different light source, is the probe used in the anisotropy measurements. A sample of these tagged TCRs is placed into a time domain spectrofluorometer which collects the data on the flexibilities. In the spectrofluorometer,

[t]he sample is excited with vertically polarized light. The electric vector of the

excitation light is oriented parallel to the vertical or z-axis. … [T]he intensity of the

emission [is measured] through a polarizer. When the emission polarizer is oriented

parallel (||) to the direction of the polarized excitation, the observed intensity is called I||.

Likewise, when the polarizer is perpendicular ( ) to the excitation, the intensity is called

I . These values are used to calculate the anisotropy (Lakowicz 291).

The difference of the intensities of light of the parallel and perpendicular divided by the total intensity is used to calculate the anisotropy of the data. If time is input into the function of the anisotropies, the equation formed is a double exponential decay function. This is because there are two factors present when the movement is being measured. The first is the fast movement (f) of the CDR loop flexing back and forth. The second source of motion is the tumbling of the entire TCR protein which is the slow movement (s). This function is modeled by

3 æ -t ö æ-t ö ç ÷ ç ÷ èq f ø èq s ø r(t) = b f e + b s e + r¥ . From this equation and its graph, the physical measurements of the protein’s flexibilities can be found. The correlation time q is represented for the fast and slow motions. Also the amplitudes of each are represented by b . In this data, researchers begin to better understand the mechanics of the TCR which is vital in new types of drug treatments.

The application of this TCR research can treat a range of diseases that can be treated through adoptive T cell transfer. “In adoptive T cell transfer, tumor Ag-specific T cells are activated ex vivo [outside of the body] and transplanted back into a lymphodepleated patient”

(Borbulevych, et al. 2453). Ag-specific means antigen specific. The flexibility research can provide a foundation for drugs in the future. Currently several studies are being conducted which would use information concerning the mechanics of TCRs. For example, to provide immunotherapy for someone with melanoma, TCRs can be altered and engineered to fight only the cancer cell. “The first TCRs used in cancer gene therapy, DMF4 and DMF5, recognize two distinct peptide epitopes of the melanoma-associated MART-1/Melan-A protein, both presented by the class I MHC protein HLA-A*0201” (Borbulevych, et al. 2453). The peptide epitopes are the surface portion of the antigen that is recognized by the TCR. When trials were conducted,

DMF5 was found to be more aggressive than DMF4, but there were side affects of autoimmune toxicities caused by the hyperactive TCR of the DMF5. By understanding how the flexibility of the TCR helps it bind to more antigens, this problem of the hyperactive TCR can be eliminated by altering the flexibility and a successful gene therapy can be created. This process and study of the TCRs can be applied to many diseases as a source of gene therapy. Another reason why TCR research is important is because TCR cross reactivity “is an underlying cause of transplant rejection” (Armstrong et al. 183). Once again, a comprehensive understanding of the flexibility

4 and mechanics of the CDR loops on the TCR can enhance the treatment of problems in the immune system.

In the current experiment, three TCRs are tested for their flexibility in order to determine the dynamics of their binding process. This research can assist the genetic engineering in the mechanics of TCRs by setting a foundation for comparison between these common TCRs and

TCRs involved in the recognition of specific pathogens for treatments. The question in this project is how do the flexibilities of the CDR loops on the TCR affect its binding capabilities.

Materials and Methods

In this research the technique of fluorescence anisotropy was used for the measurements of the flexibilities of the CDR loops. In order for these measurements to be made, several steps had to be taken. These include the preparation of purifying the protein, the data collection with fluorescence anisotropy, and the data analysis. Then once the final tests of the proteins are taken, an analysis software called Vinci fits the data to a double exponential decay function.

In order for the anisotropy process to work, the end of the CDR loop must be tagged with a fluorescing probe, a fluorophore. In these tests, three TCRs were tested in order to provide a range of results on how the CDR loop flexes and interacts with the other loops. The first protein was the A6C134 TCR with a fluorescein-5-maleimide (F5M) fluorophore bound to the serine on the alpha chain. The second was a 2C TCR with a BODIPY fluorophore bound to a phenylalanine on the alpha chain. The third was an A6 TCR with a BODIPY fluorophore bound to an alanine on the beta chain. The reason a different fluorophore was used was based on the binding capabilities of the fluorophore to the amino acid. The first two proteins mentioned are

5 located in the same position but in two separate TCRs. The third is located on the beta chain towards the edge. The site where the fluorophore is attached is the site where the flexibility measurements will be taken.

The fluorophore is the tool by which the anisotropies will be measured. The proteins that were used in this experiment were tagged and refolded into their natural shape. The first step in the process was to purify the proteins. Size exclusion chromatography is used to purify the proteins by their size. This way, only the protein itself is tested. The proteins are separated into different test tubes based on the size of the molecule. The collection tube with the highest concentration of the sample in the tube is selected. In order to determine if the correct protein is purified, a gel-electrophoresis is run as a way to measure how well it separated and if the correct protein separated. Then a picture is taken of the gel in UV light to determine if the proteins are labeled correctly. By analyzing the gel, distinct separation is found between the samples.

Finally, the concentration of the protein in the solution and the percent of the protein in the solution are measured by using the NanoDrop 2000c machine. A small sample of the solution is placed on a pedestal using a pipet, and then the lid is closed for the computer to get a reading of the concentrations by measuring the light absorption and using Beer’s Law, the relationship between absorption and concentrations, to calculate the concentrations.

The next part of the procedure is to collect the data concerning the anisotropies of the proteins in solution. This is done using the ISS ChronosBH time-domain spectrofluorometer.

The tagged protein is placed in the Quartz Fluorometer Cell. This is placed in the ChronosBH.

Data is collected using the Vinci software on the computer connected to the ChronosBH. For each sample, parallel and perpendicular intensities are collected. In Figure 2, the schematic of how a spectrofluorometer works is shown. The intensities are collected in the time-correlated

6 single photon counter (TCSPC). This data is then analyzed on the computer using the Vinci software.

Figure 2: Vertically polarized light is shot at the sample, and light emitted is collected in vertical and horizontal intensities.

The final part of the procedure is the data analysis. First, the anisotropies are calculated

(Ill - I ^) for each value using the intensity (I) of the light. The equation r = displays this (Ill + 2I ^) relationship. The next step is the data fit on the computer using the equation of time as a function

æ -t ö æ-t ö ç ÷ ç ÷ of anisotropy: èq f ø èqs ø . The amplitude of the individual exponential r(t) = b f e + b s e + r¥ functions in the decay function is represented by “β”. The correlation time of each exponential in the decay function is represented by “θ”. Time is represented by “t”. This equation is the one that is needed to interpret accurate results for the data. Using the Vinci software, the values are outputted for each variable.

Results The results gathered have several similar qualitative similarities. The theta1 value, or the

fast correlation time, is always shorter than the theta2 value, the slow correlation time. The beta1

value, the amplitude of the fast correlation time, is always larger than the beta2 value, the

amplitude of the slow correlation time. In the fit produced by the Vinci software, the data from a

single sample is inconsistent from one fit to another. This inconsistency is shown in two out of

the three samples collected. In order to find out how reliable these fits were, the data was fit to

different software for comparison and analysis purposes. Below are the results gathered from the

Vinci analysis software. An analysis of the Vinci software is underway and revised results will

be gathered.

Sample theta1 beta1 theta2 beta2 r0 tau x^2 A6C134 S100Calpha F5M 0.92 0.30 27.90 0.08 0.38 3.53 30.20 1.78 0.22 1940000.00 0.05 0.28 3.46 39.60 1.11 0.26 24.90 0.08 0.33 3.45 38.80 1.45 0.25 4530000.00 0.06 0.31 3.41 36.00 1.38 0.26 87200.00 0.06 0.31 3.39 37.80 (Average) 1.33 0.26 1311450.56 0.06 0.32 3.45 36.48 (Standard Deviation) 0.33 0.03 1980693.75 0.01 0.04 0.05 3.76 2C F100Calpha BDY 1.63 0.24 13700000.00 0.08 0.32 5.13 24.30 1.60 0.26 11800000.00 0.08 0.33 5.17 27.20 1.44 0.26 57.80 0.08 0.35 5.14 26.60 1.41 0.27 60.10 0.08 0.35 5.18 26.30 6.45 0.25 - - - 5.52 28.60 (Average) 2.51 0.25 6375029.48 0.08 0.34 5.23 26.60 (Standard Deviation) 2.21 0.01 7401936.61 0.00 0.01 0.16 1.56 A6 A52Cbeta BDY 0.65 0.17 18.90 0.13 0.30 6.56 11.70 0.55 0.18 21.00 0.13 0.31 6.52 8.50 0.44 0.27 25.20 0.04 0.31 6.52 7.60 0.53 0.21 24.20 0.13 0.33 6.58 7.21 0.85 0.17 31.30 0.12 0.29 6.56 7.62 (Average) 0.61 0.20 24.12 0.11 0.31 6.55 8.53 (Standard Deviation) 0.16 0.04 4.74 0.04 0.02 0.03 1.84 Table 1: Data for the curve fits of the three different samples. When the data is plotted on a graph, the results that are received can be easily visually analyzed. By understanding the anisotropy equation, conclusions about the flexibility of the site

8 can be easily analyzed. In figure 3, a diagram of how rigid, moderate, and flexible sites’ plots look is displayed.

Figure 3: Graph displaying the decay curves for rigid, moderate, and flexible sites.

Discussion and Conclusions

As of now, the hypothesis that the flexibility of the CDR loops allow the protein to better adapt to diverse surfaces to increase the recognition of ligands cannot be conclusively answered since the data fits have not been satisfactory. Once the data is gathered, by knowing how flexible the CDR loop is, the adaptability of the CDR loop will be able to be determined. One source of error is the Vinci software. One of the main goals of this project has been to reduce the error of the software. Many attempts to fix the software through user adjustments have been made, and currently, the problems have been addressed to the company, and more accurate fits will be received. Aside from the troubles with the software, many qualitative analyses can be made about the data that are helpful to the understanding of TCR dynamics. Between all three of the sites tested, the A6 A52C with F5M has the shortest theta1 value. The theta1 value is the correlation

9 time for the fast movement of the CDR loop. Since the A6 A52C site has the fastest moving probe and CDR loop, it is observed that that loop is moving faster. The CDR loop that the site is on is the CDR2β. This loop is near the edge of the TCR. Possible conclusions that could be made are that this loop has to move faster in order to be able to reach and recognize the different antigens that are presented. The other two TCR sites are near the center of the TCR on the

CDR3α loop. The times for these are longer. This may show that the inside loops need to be more rigid because they need to provide a foundation when they bond to the p-MHC. When more accurate data is brought in, the results’ application becomes self-evident. The knowledge gained from the research on the mechanics of the TCRs gives rise to a possible drug therapy from the acquired knowledge about TCR. 10

References

Armstrong, Kathryn M., Kurt H. Piepenbrink, and Brian M. Baker. “Conformational changes

and flexibility in T-cell receptor recognition of peptide-MHC complexes.”

Biochemical Journal. 2008: 183-196. Print.

Borbulevych, Oleg Y., Kurt H. Piepenbrink, Brian E. Gloor, Daniel R. Scott, Ruth F. Sommese,

David K. Cole, Andrew K. Sewell, and Brian M. Baker. “T Cell Receptor Cross-

reactivity Directed by Antigen-Dependent Tuning of Peptide-MHC Molecular

Flexibility.” Immunity. Volume 31. 2009: 885-896. Print.

Borbulevych, Oleg Y., Sujatha M. Santhanagopolan, Moushumi Hossain, and Brian M. Baker.

“TCRs Used in Cancer Gene Therapy Cross-React with MART-1/Melan-A Tumor

Antigens via Distinct Mechanisms.” The Journal of Immunology. 2011: 2453-2463.

Print.

Lakowicz, Joseph R. Principles of Fluorescence Spectroscopy. Third Edition. Singapore:

Springer, 2006.