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

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(Protocols)
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===Part 3: Examine binding shifts===
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===Part 3: Review journal article===
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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Read and discuss the following journal article with your laboratory partner:
  
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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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.
#Open the Excel file corresponding to the first derivative data
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#Column B should contain temperature information in Celsius.
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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.
#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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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.
#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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<font color =  #4a9152 >'''In your laboratory notebook,'''</font color> complete the following with your partner:
#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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*Why is the apicoplast a promising target for anti-malarial drug development?
#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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*Why have attempts at developing broadly effective drugs that target the apicoplast been unsuccessful?
#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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*Why is the approach used by the researchers in this article more promising?
#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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*List the figures that are included in the article. For each figure:
#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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**What is the main conclusion / finding in each figure?
#*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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**Which panel best supports the main conclusion / finding?  Is more than one panel needed to fully support the main conclusion?
#**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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**Are you convinced by the data?  Do you agree with the main conclusion?
#**[[File:SingleSiteBindingModelEquation.png|700px|center]]
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*Are the figures organized in a coherent story?
#*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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**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?).
#*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==
 
==Reagents==

Revision as of 04:17, 30 October 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

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.

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 Tm, 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).

The ΔTm is the difference between the Tm 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 ΔTm 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 Tm of FKBP12 (blue curve) is ~50 °C. With the addition of rapamycin (red curve) the Tm is shifted to ~78 °C resulting in a ΔTm of ~20 degrees. Data in this plot was obtained by Becky Leifer from the Koehler lab.

Sp18 20.109 M1D6 DSF expample data.png

Protocols

Part 3: 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?).

Reagents

Introduction

Protocols

Reagent list

Navigation links

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