20.109(S08):Data analysis (Day7)

Introduction

This is it, folks! Moment of truth. Time to find out how the proteins that you worked so hard to make, purify, and test really behave. Although you should be able to produce reasonable binding curves by following the example of Nagai, the introduction/review of binding constants below may help contextualize your analysis.

skeleton:

Let’s start by considering the simple case of a receptor-ligand pair that are exclusive to each other, and in which the receptor is monovalent. The ligand (L) and receptor (R) form a complex (C), which reaction can be written

$ R + L {k_f}<below sym = rarr> C $

At equilibrium, the rates of the forward and reverse reactions must be equivalent. Thus, an equilibrium dissociation constant K_D may be defined as kr/kf, or [R][L]/[C], where brackets indicate the molar concentration of a species. The fraction of receptors that bind ligand, often called y, is C/R_TOT, where R_TOT indicates total (both bound and unbound) receptors. R_TOT = [C] (ligand-bound receptors) + [R] (unbound receptors). Thus,

$ \qquad y = {[C] \over [C] + [R]} \qquad = \qquad {[L] \over [L] + [K_D]} \qquad $

where the right-hand equation was derived by simple algebraic substitution. If the ligand concentration is in excess of that of the receptor, [L] may be approximated as a constant, [Lo]. Let’s focus on the implications of this result.

What happens when [L]>> K_D? In this case y ~L/ K_D, so the receptors bind ligand in a first order fashion, directly proportional to L.

What happens when [L]<< K_D? Then y = 1/ K_D, and the binding fraction becomes approximately constant (i.e., the saturation concentration is reached).

What happens when [L]= K_D? Then y = 0.5, and the fraction of receptors that are bound to ligand is 50%. This is why you can read K_D directly off of the plots in Nagai’s paper. When y = 0.5, the concentration of free calcium (our [L]) is equal to K_D. This is a great rule of thumb to know.

Of course, inverse pericam has multiple binding sites, and thus IPC-calcium binding is actually more complicated than in the example above. The K_D reported by Nagai is called an ‘apparent K_D’ because it reflects the overall avidity of multiple calcium binding sites, not their individual affinities for calcium. Normally, calmodulin has a low affinity (N-terminus) and a high affinity (C-terminus) pair of calcium binding sites. However, the E104Q mutant, which is the version of CaM used in inverse pericam, displays only low-affinity binding. Moreover, the Hill coefficient, which quantifies cooperativity of binding in the case of multiple sites, is reported to be 1.0 for inverse pericam. This indicates that calcium binds to the multiple binding sites of calmodulin indepedently of each other (in contrast to hemoglobin, for example, whose affinity for the next oxygen increases once one oxygen is bound). Thus, IPC is well-described by a single apparent K_D.

Have them try Hill plot in addition to the semilog binding curve? How much detail to give? Would be nice to also discuss the Green paper on rational protein design based on free energy modeling, here or on day 4.

Protocols

(Sequence only now after following both samples through all steps? Or sequence on 4 or 5 prior to induction or purification, respectively?)

(PAGE gel analysis as a FNT assignment, or also today?)

Part X: Titration curve

Today you will analyze the fluorescence data that you got last time. Begin by analyzing the wild-type protein as a check on your work, then move on to your mutant sample(s?).

1. Open an Excel file for your data analysis. Begin by making a column of the free calcium concentrations present in your twelve test solutions. Assuming a 1:1 dilution of protein with calcium, the concentrations are: 0.1 nM, 1 nM, 10 nM, 100 nM, 250 nM, 500 nM, 750 nM, 1 μM, 10 μM, 50 μM, 100 μM, 1000 μM. Be sure to convert all concentrations to the same units. (WILL ALREADY HAVE DONE THIS CONVERSION ON DAY 3?)
2. Now open the text file containing your raw data as an Excel file. Convert the row-wise data to column-wise data (using Paste Special → Transpose), and transfer each column to your analysis file. Add column headers to indicate which protein is which. Also include a column of your control samples that did not contain protein.
3. Begin by calculating the average of your blank samples. Bold this number for easy reference. It is the background fluorescence present in the calcium solutions and should be quite low. Subtract this background value from each of your raw data values. It may help to have a 4-column series called “RAW”, and another called “SUBTRACTED.”
4. To get a sense of your data, you can plot the subtracted data as is and have a quick look at it. Note the approximate inflection points of the curves. The inflection point is equal to the overall observed calcium affinity of IPC, or apparent KD.
   • If your two replicate values for a different protein are wildly different, you may want to plot each curve separately. On the other hand, if your replicates are similar (as we expect), you will want to average them prior to plotting – this will improve the signal:noise of your dataset. When you do this, be sure to add error bars to your final plot.
5. To get a more precise value of KD, you should normalize your data to resemble Figure 3 in the Nagai paper. The maximum and minimum fluorescence values for a given protein should be defined as 100% and 0% fluorescence, respectively, and every other fluorescence value should expressed as a percentage in between.
6. Once you have analyzed the data you obtained from the fluorescence plate reader in this way, load your old Nanodrop data for the wild-type protein, and analyze it as above. How does the benchtop, single replicate assay compare to the plate reader data?
7. Today or on your own time, prepare representative fluorescence curves for both your wild-type and mutant protein. These will be included in the data analysis portion of your portfolio.

For next time

test

<below sym=rarr>X + Y</below>