Difference between revisions of "20.109(F21):M2D6"

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==Introduction==
 
==Introduction==
Interactions between low molecular weight ligands and proteins have been shown to increase the thermostability of proteins.  This means that proteins bound to ligand are able to maintain tertiary structure, or resist denaturation, at higher temperatures than unbound proteins.  Today we will use differential scanning fluorimetry (DSF) to examine the potential FKBP12 binders identified in our SMM screen.
 
  
DSF is a method used to identify low molecular weight ligands that bind and stabilize a protein of interest.  In this assay, protein denaturation is measured via a fluorescent dye that has an affinity for hydrophobic regions.  When the protein is folded the hydrophobic pockets are inaccessible to the dye and the fluorescent signal is quenched by water in the solution. As the protein unfolds, the dye interacts with the hydrophobic regions and emits a fluorescent signal that can be detected. 
+
Today you will begin to assess the data collected from the IPC experiment by assessing the K<sub>D</sub> and the association / dissociation rates.  As a review of the key concepts, consider 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 can be written
  
When a protein is bound to a ligand, the stability can be increased such that the temperature at which the protein denatures is increased.  In the DSF assay, this is measured as a shift in the T<sub>m</sub>, or melting temperature; which is defined as the temperature at which 50% of the protein is unfolded.  This value represents the midpoint of the transition from structured (folded) to denatured (unfolded).
+
<center>
 +
<math> R + L  \rightleftharpoons\ ^{k_f}_{k_r}      C </math>
 +
</center>
  
The &Delta;T<sub>m</sub> is the difference between the T<sub>m</sub> of the unbound protein sample, or protein sample without added ligand, and the bound protein sample, protein sample with added ligand. If the tested ligand binds the protein of interest, the &Delta;T<sub>m</sub> can be observed as a shift in the plotted DSF data.  For example, the data below show results of a pilot experiment completed in preparation for this module.  In this graph the T<sub>m</sub> of FKBP12 (blue curve) is ~50 &deg;C. With the addition of rapamycin (red curve) the T<sub>m</sub> is shifted to ~78 &deg;C resulting in a &Delta;T<sub>m</sub> of ~20 degrees. Data in this plot was obtained by Becky Leifer from the Koehler lab.
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At equilibrium, the rates of the forward reaction (rate constant = <math>k_f</math>) and reverse reaction (rate constant = <math>k_r</math>) must be equivalent. Solving this equivalence yields an equilibrium dissociation constant <math>K_D</math>, which may be defined either as <math>k_r/k_f</math>, or as <math>[R][L]/[C]</math>, where brackets indicate the molar concentration of a species. Meanwhile, the fraction of receptors that are bound to ligand at equilibrium, often called ''y'' or &theta;, is <math>C/R_{TOT}</math>, where <math>R_{TOT}</math> indicates total (both bound and unbound) receptors. Note that the position of the equilibrium (''i.e.'', ''y'') depends on the starting concentrations of the reactants; however, <math>K_D</math> is always the same value. The total number of receptors <math>R_{TOT}</math>= [''C''] (ligand-bound receptors) + [''R''] (unbound receptors). Thus,
[[Image:Sp18 20.109 M1D6 DSF expample data.png|thumb|center|550px]]
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==Protocols==
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<center>
 +
<math>\qquad y = {[C] \over R_{TOT}} \qquad = \qquad {[C] \over [C] + [R]} \qquad = \qquad {[L] \over [L] + [K_D]} \qquad</math>
 +
</center>
  
 +
where the right-hand equation was derived by algebraic substitution. If the ligand concentration is in excess of the concentration of the receptor, [''L''] may be approximated as a constant, ''L'', for any given equilibrium. Let’s explore the implications of this result:
  
===Part 3: Examine binding shifts===
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*What happens when ''L'' << <math>K_D</math>?
You will receive two Excel sheets containing raw data from each well of a 384 well plate over the specified range of temperatures. The Excel sheet with "Melt Curve RFU Results" in the file name will contain raw fluorescence intensity data, while the other sheet with "Melt Curve Derivative Results" in its name will have the values for the first derivative of the melt curve. The teaching faculty will inform you which wells correspond to which conditions for your group.
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::&rarr;Then ''y'' ~ <math>L/K_D</math>, and the binding fraction increases in a first-order fashion, directly proportional to ''L''.  
  
One basic way to determine the "melting temperature," or T<sub>m</sub> of the protein is to determine temperature at the inflection point of the melting curve. This inflection point would occur at the maximum value of the first derivative. The BioRad CFX machine we use actually exports the negative of the first derivative in the Excel file, so we will find the minimum value in the first derivative Excel file, and take the corresponding temperature to be the T<sub>m</sub> of FKBP12 in each condition.
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*What happens when ''L'' >> <math>K_D</math>?
#Open the Excel file corresponding to the first derivative data
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::&rarr;In this case ''y'' ~1, so the binding fraction becomes approximately constant, and the receptors are saturated.
#Column B should contain temperature information in Celsius.
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#At a row on the bottom of column C, type in the following command: =INDEX($B$''FirstRow'':$B$''LastRow'', MATCH(MIN(C''FirstRow'':C''LastRow''),C''FirstRow'':C''LastRow'',0)), where ''FirstRow'' corresponds to the row number of the first row containing data, and ''LastRow'' contains the row number of the last row containing data.
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#Press enter, and double check that the listed temperature occurs at the minimum value of the first derivative.
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#Then, drag the bottom left corner of the cell across all relevant columns to apply the formula to those columns of interest.
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#Plot the columns relevant to your data set by making a scatter plot ("straight marked scatter"), having the temperature (values in column B) on the x-axis, and the first derivative values on the y-axis.
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#Double check by eye that the values you calculated to be the melting temperatures correspond to the minimum values on the curves. (See example plot in the introduction section of this wiki page)
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#Next, you may also check to see what the melting curves look like in terms of raw fluorescence by plotting fluorescence intensity vs. temperature in the "Melt Curve RFU Results" file. Again, validate the results you found by eye to see if the T<sub>m</sub>s correspond to the inflection point of the raw fluorescence melt curves.
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#Check to see if the T<sub>m</sub> of the control protein shifted when its ligand was added. Quantify the shift.
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#Check to see if the T<sub>m</sub> of FKBP12 shifted when Rapamycin or other compounds were added. Quantify the shifts.
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#By varying the concentration of Rapamycin, you will be able to determine an apparent dissociation constant of Rapamycin and FKBP12. Here is the reference for finding the apparent dissociation constant that was mentioned in lecture: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692391/
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#*You may use this MATLAB script ([[media: ApparentKd2.m|ApparentKd.m]]) to help fit the Tm vs. Rapamycin concentration curve to a single binding site model and find a value for the apparent K<sub>D</sub>. The function uses a nonlinear regression.
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#**Based on the given article linked above, the single binding site model is as follows, where the fit parameters include Tm_min (minimum Tm at no ligand concentration), Tm_max (maximum Tm at infinite ligand concentration), and K<sub>D</sub> is the apparent K<sub>D</sub> value. For our experiment, the concentration of FKBP12 (written as [FKBP12]) was 8.5 uM and the concentration of Rapamycin (written as [Rap]) was variable.
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#**[[File:SingleSiteBindingModelEquation.png|700px|center]]
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#*Create an array of Rapamycin concentrations by typing ''RapConc = [A, B, C, D, E, etc.]'' at the MATLAB command prompt, where A, B, C are the various Rapamycin concentrations in units of uM.
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#*The 10 concentrations of Rapamycin are: 20uM, 10uM, 5uM, 1uM, 0.1uM, 0.05uM, 0.01uM, 0.005uM, 0.001uM, and 0.0001uM
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#*Create an array of T<sub>m</sub>s by typing ''Tm = [A2, B2, C2, D2, E2, et c.]'' at the MATLAB command prompt, where A2, B2, C2 are T<sub>m</sub>s corresponding to concentrations A, B, and C in the previous array.
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#*Making sure the MATLAB function ApparentKd.m is in your current working directory, type in ''ApparentKd(RapConc, Tm)'' at the command prompt and press enter
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#*The function performs a nonlinear regression of your data with a single binding site model, and will return an apparent K<sub>D</sub> value in units of uM from the best fit.
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#*If this regression does not work well, you may use alternative methods to estimate an apparent K<sub>D</sub> value
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#**You can find the EC50 value by fitting with the following formula in this matlab function [[media: EC50.m|EC50.m]]
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#**Making sure the MATLAB function EC50.m is in your current working directory, type in ''EC50(RapConc, Tm)'' at the command prompt and press enter
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#**This file performs nonlinear regression using the following equation, where the fit parameters include Tm_min (minimum Tm at no ligand concentration, Tm_max (maximum Tm at infinite ligand concentration, and EC<sub>50</sub> is the EC<sub>50</sub> or apparent K<sub>D</sub> value, and a Hill coefficient which should not be meaningful in this context.
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#**[[File:EC50equation.png|300px|center]]
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==Reagents==
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*What happens when ''L'' = <math>K_D</math>?
 +
::&rarr;Then ''y'' = 0.5, and the fraction of receptors that are bound to ligand is 50%. When ''y'' = 0.5, the concentration of free calcium (our [''L'']) is equal to <math>K_D</math>.
  
==Introduction==
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In the BLI experiment, the association rate constant (<math>k_a</math> or <math>k_{on}</math>) and dissociation rate constant (<math>k_d</math> or <math>k_{off}</math>) are measured for each concentration of ligand.  The <math>K_D</math> is calculated from these values using the following equation:
 +
 
 +
<center>
 +
<math> K_D = k_d/k_a </math>
 +
</center>
 +
 
 +
In the graph below this relationship is measured as response over time where A = the protein immobilized to the probe and B = small molecule in solution.
 +
 
 +
[[Image:Fa21 M2D6 association dissociation graph.png|thumb|center|600px|'''Association and dissociation over time.''' Formation of complex AB over time (x-axis) is detected kinetically by the probe and captured by a change in response (y-axis).]]
  
 
==Protocols==
 
==Protocols==
  
==Reagent list==
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===Part 1: Complete data analysis for BLI assay===
 +
 
 +
Review the short tutorial videos on processing BLI data sets (available [https://www.youtube.com/watch?v=EBBdHmcFOxk here]) and kinetic analysis (available [https://www.youtube.com/watch?v=6f9Zsqj68F8 here]).
 +
#Open your file in the Data Analysis software.
 +
#Save the raw file by clicking the ‘raw file’ button at the bottom.
 +
#Align the baseline along the Y axis by clicking ‘Y-axis alignment’.
 +
#Select the row and indicate it as ‘reference sample.’
 +
#We need to subtract the compound only curve from the (protein + compound) curves at during the experimental analysis. Select the rows (A1-B1) for subtraction and click the ‘subtraction’ button.
 +
#Save the processed file by clicking the ‘process’ button.
 +
#To obtain the kinetic binding values, we will do curve fitting to the preset binding equations. For example, there are set parameters as 1:1 vs 2:1 binding. 1:1 and 2:1 fit means 1 protein to 1 compound binding and 1 protein to 2 compound binding, respectively.
 +
#*You will perform the 1:1 fitting operation and save the file.
 +
#You will later analyse these data to see which fitting model is suitable for our kinetic experiment.
 +
 
 +
===Part 2: Review journal article===
 +
Read and discuss the following journal article with your laboratory partner:
 +
 
 +
Amberg-Johnson ''et al.'' "[[Media:Fa20 M2D5 paper discussion.pdf |Small molecule inhibition of apicomplexan FtsH1 disrupts plastid biogenesis in human pathogens.]]" ''eLife''. (2017) 6:e29865.
 +
 
 +
The initial experiment presented by Amberg-Johnson ''et. al.'' shows the effect of actinonin on apicoplast biogenesis.  The apicoplast is an essential plastid organ that is a key target for drug development in research focused on malaria treatment.  Actinonin was identified in large-scale screen of compounds known to inhibit growth of parasite.  The subsequent experiments completed in this research served to uncover the mechanism-of-action of actinonin is it pertains to disruption of the apicoplast.
 +
 
 +
In the context of your research, this article focuses on the next step experiments that can be performed after a drug candidate is discovered from a screen.  Though you can use this article as guidance as you consider the experiments that could follow your screen, remember that the specific next step experiments should be related to the protein target and drug candidate(s) identified in your project.  For this exercise, the focus in on how the data are organized and presented.
 +
 
 +
<font color =  #4a9152 >'''In your laboratory notebook,'''</font color> complete the following with your partner:
 +
*Why is the apicoplast a promising target for anti-malarial drug development?
 +
*Why have attempts at developing broadly effective drugs that target the apicoplast been unsuccessful?
 +
*Why is the approach used by the researchers in this article more promising?
 +
*List the figures that are included in the article. For each figure:
 +
**What is the main conclusion / finding in each figure?
 +
**Which panel best supports the main conclusion / finding?  Is more than one panel needed to fully support the main conclusion?
 +
**Are you convinced by the data?  Do you agree with the main conclusion?
 +
*Are the figures organized in a coherent story?
 +
**Write transition statement that connect each figure to the next.  A transition statement should very briefly summarize the findings of a figure and state what those findings motivated the research to do next (ie what is the next experiment?).
  
 
==Navigation links==
 
==Navigation links==
Next day: [[20.109(F21):M2D7 |Complete data analysis]] <br>
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Next day: [[20.109(F21):M2D7 |Plot and interpret data from secondary assay]] <br>
Previous day: [[20.109(F21):M2D5 |Prepare for secondary assay to test putative small molecule binders]] <br>
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Previous day: [[20.109(F21):M2D5 |Perform secondary assay to test putative small molecule binders]] <br>

Latest revision as of 17:13, 3 November 2021

20.109(F21): Laboratory Fundamentals of Biological Engineering
Drawing provided by Marissa A., 20.109 student in Sp21 term.  Schematic generated using BioRender.

Fall 2021 schedule        FYI        Assignments        Homework        Class data        Communication        Accessibility

       Module 1: Genomic instability                          Module 2: Drug discovery       


Introduction

Today you will begin to assess the data collected from the IPC experiment by assessing the KD and the association / dissociation rates. As a review of the key concepts, consider 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 can be written

$ R + L \rightleftharpoons\ ^{k_f}_{k_r} C $

At equilibrium, the rates of the forward reaction (rate constant = $ k_f $) and reverse reaction (rate constant = $ k_r $) must be equivalent. Solving this equivalence yields an equilibrium dissociation constant $ K_D $, which may be defined either as $ k_r/k_f $, or as $ [R][L]/[C] $, where brackets indicate the molar concentration of a species. Meanwhile, the fraction of receptors that are bound to ligand at equilibrium, often called y or θ, is $ C/R_{TOT} $, where $ R_{TOT} $ indicates total (both bound and unbound) receptors. Note that the position of the equilibrium (i.e., y) depends on the starting concentrations of the reactants; however, $ K_D $ is always the same value. The total number of receptors $ R_{TOT} $= [C] (ligand-bound receptors) + [R] (unbound receptors). Thus,

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

where the right-hand equation was derived by algebraic substitution. If the ligand concentration is in excess of the concentration of the receptor, [L] may be approximated as a constant, L, for any given equilibrium. Let’s explore the implications of this result:

  • What happens when L << $ K_D $?
→Then y ~ $ L/K_D $, and the binding fraction increases in a first-order fashion, directly proportional to L.
  • What happens when L >> $ K_D $?
→In this case y ~1, so the binding fraction becomes approximately constant, and the receptors are saturated.
  • What happens when L = $ K_D $?
→Then y = 0.5, and the fraction of receptors that are bound to ligand is 50%. When y = 0.5, the concentration of free calcium (our [L]) is equal to $ K_D $.

In the BLI experiment, the association rate constant ($ k_a $ or $ k_{on} $) and dissociation rate constant ($ k_d $ or $ k_{off} $) are measured for each concentration of ligand. The $ K_D $ is calculated from these values using the following equation:

$ K_D = k_d/k_a $

In the graph below this relationship is measured as response over time where A = the protein immobilized to the probe and B = small molecule in solution.

Association and dissociation over time. Formation of complex AB over time (x-axis) is detected kinetically by the probe and captured by a change in response (y-axis).

Protocols

Part 1: Complete data analysis for BLI assay

Review the short tutorial videos on processing BLI data sets (available here) and kinetic analysis (available here).

  1. Open your file in the Data Analysis software.
  2. Save the raw file by clicking the ‘raw file’ button at the bottom.
  3. Align the baseline along the Y axis by clicking ‘Y-axis alignment’.
  4. Select the row and indicate it as ‘reference sample.’
  5. We need to subtract the compound only curve from the (protein + compound) curves at during the experimental analysis. Select the rows (A1-B1) for subtraction and click the ‘subtraction’ button.
  6. Save the processed file by clicking the ‘process’ button.
  7. To obtain the kinetic binding values, we will do curve fitting to the preset binding equations. For example, there are set parameters as 1:1 vs 2:1 binding. 1:1 and 2:1 fit means 1 protein to 1 compound binding and 1 protein to 2 compound binding, respectively.
    • You will perform the 1:1 fitting operation and save the file.
  8. You will later analyse these data to see which fitting model is suitable for our kinetic experiment.

Part 2: Review journal article

Read and discuss the following journal article with your laboratory partner:

Amberg-Johnson et al. "Small molecule inhibition of apicomplexan FtsH1 disrupts plastid biogenesis in human pathogens." eLife. (2017) 6:e29865.

The initial experiment presented by Amberg-Johnson et. al. shows the effect of actinonin on apicoplast biogenesis. The apicoplast is an essential plastid organ that is a key target for drug development in research focused on malaria treatment. Actinonin was identified in large-scale screen of compounds known to inhibit growth of parasite. The subsequent experiments completed in this research served to uncover the mechanism-of-action of actinonin is it pertains to disruption of the apicoplast.

In the context of your research, this article focuses on the next step experiments that can be performed after a drug candidate is discovered from a screen. Though you can use this article as guidance as you consider the experiments that could follow your screen, remember that the specific next step experiments should be related to the protein target and drug candidate(s) identified in your project. For this exercise, the focus in on how the data are organized and presented.

In your laboratory notebook, complete the following with your partner:

  • Why is the apicoplast a promising target for anti-malarial drug development?
  • Why have attempts at developing broadly effective drugs that target the apicoplast been unsuccessful?
  • Why is the approach used by the researchers in this article more promising?
  • List the figures that are included in the article. For each figure:
    • What is the main conclusion / finding in each figure?
    • Which panel best supports the main conclusion / finding? Is more than one panel needed to fully support the main conclusion?
    • Are you convinced by the data? Do you agree with the main conclusion?
  • Are the figures organized in a coherent story?
    • Write transition statement that connect each figure to the next. A transition statement should very briefly summarize the findings of a figure and state what those findings motivated the research to do next (ie what is the next experiment?).

Navigation links

Next day: Plot and interpret data from secondary assay

Previous day: Perform secondary assay to test putative small molecule binders