20.109(S13):Assess protein function (Day8) - Revision history

AgiStachowiak: /* Part 1: Titration curve in Excel and first estimate of KD */

2013-04-11T01:43:37Z

Part 1: Titration curve in Excel and first estimate of KD

←Older revision		Revision as of 01:43, 11 April 2013
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	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 (your curve should resemble Nagai's Figure 3L), and then move on to your mutant samples. If you are not familiar with manipulations in Excel, use the ''Help'' menu or ask the teaching faculty for assistance.		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 (your curve should resemble Nagai's Figure 3L), and then move on to your mutant samples. If you are not familiar with manipulations in Excel, use the ''Help'' menu or ask the teaching faculty for assistance.

-	#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: 10 nM, 50 nM, 100 nM, 200 nM, 400 nM, 500 nM, 600 nM, 800 nM, 1 μM, 2.5 μM, 10 μM, 100 μM. Be sure to convert all concentrations to the same units.	+	#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: 10 nM, 50 nM, 100 nM, 200 nM, 400 nM, 500 nM, 600 nM, 800 nM, 1 μM, 2.5 μM, 10 μM, 100 μM. Be sure to convert all concentrations to the same units.<br><font color=red>With apologies, this year's concentrations are: 1 nM, 10 nM, 50 nM, 100 nM, 200 nM, 300 nM, 400 nM, 600 nM, 800nM, 1 μM, 2.5 μM, and 10 μM.</font color>
	#Now open the text file containing your raw data as a tab-delimited file in Excel (you can download the file from today's [[Talk:20.109%28S13%29:Assess_protein_function_%28Day8%29 \| Talk]] page). 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, and analyze each replicate separately for now. Also include a column of your control samples that did not contain protein.		#Now open the text file containing your raw data as a tab-delimited file in Excel (you can download the file from today's [[Talk:20.109%28S13%29:Assess_protein_function_%28Day8%29 \| Talk]] page). 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, and analyze each replicate separately for now. Also include a column of your control samples that did not contain protein.
	#Begin by calculating the average of your blank samples, and bold this number for easy reference. It is the background fluorescence present in the calcium solutions and should be quite low. If necessary, subtract this background value from each of your raw data values. It may help to have a 6-column series called “RAW”, and another called “SUBTRACTED.”		#Begin by calculating the average of your blank samples, and bold this number for easy reference. It is the background fluorescence present in the calcium solutions and should be quite low. If necessary, subtract this background value from each of your raw data values. It may help to have a 6-column series called “RAW”, and another called “SUBTRACTED.”

AgiStachowiak: /* For next time */

2013-04-10T01:32:35Z

For next time

←Older revision		Revision as of 01:32, 10 April 2013
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	#Now that you have completed all of the assays for Module 2 (except for analysis), prepare a high-level outline of your results section. Specifically, you should write sub-section titles, and beneath each title write a few short phrases indicating what content will be included in that sub-section. You don't need to include any specific numbers or even specific findings (except perhaps in the titles), just the ''type'' of content that you will have. Do, however, include some sense of your narrative logic -- topic sentences and transitions may be phrases rather than complete sentences, but in any case these may be the best way to clarify your plans. '''Please feel free to turn this assignment in early, in order to get it back early, to improve usefulness as you write your reports.'''		#Now that you have completed all of the assays for Module 2 (except for analysis), prepare a high-level outline of your results section. Specifically, you should write sub-section titles, and beneath each title write a few short phrases indicating what content will be included in that sub-section. You don't need to include any specific numbers or even specific findings (except perhaps in the titles), just the ''type'' of content that you will have. Do, however, include some sense of your narrative logic -- topic sentences and transitions may be phrases rather than complete sentences, but in any case these may be the best way to clarify your plans. '''Please feel free to turn this assignment in early, in order to get it back early, to improve usefulness as you write your reports.'''
		+	#Please post your findings to the table on today's Talk page by 5 pm on Wednesday, April 17th -- but ideally much sooner!

AgiStachowiak: /* For next time */

2013-04-09T20:46:14Z

For next time

←Older revision		Revision as of 20:46, 9 April 2013
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	==For next time==		==For next time==

-	#Now that you have completed all of the assays for Module 2 (except for analysis), prepare a high-level outline of your results section. Specifically, you should write sub-section titles, and beneath each title write a few short phrases indicating what content will be included in that sub-section. You don't need to include any specific numbers or even specific findings (except perhaps in the titles), just the ''type'' of content that you will have. Do, however, include some sense of your narrative logic -- topic sentences and transitions may be phrases rather than complete sentences, but in any case may be the best way to clarify your plans. '''Please feel free to turn this assignment in early, in order to get it back early, to improve usefulness as you write your reports.'''	+	#Now that you have completed all of the assays for Module 2 (except for analysis), prepare a high-level outline of your results section. Specifically, you should write sub-section titles, and beneath each title write a few short phrases indicating what content will be included in that sub-section. You don't need to include any specific numbers or even specific findings (except perhaps in the titles), just the ''type'' of content that you will have. Do, however, include some sense of your narrative logic -- topic sentences and transitions may be phrases rather than complete sentences, but in any case these may be the best way to clarify your plans. '''Please feel free to turn this assignment in early, in order to get it back early, to improve usefulness as you write your reports.'''

AgiStachowiak: /* For next time */

2013-04-09T20:32:20Z

For next time

←Older revision		Revision as of 20:32, 9 April 2013
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	==For next time==		==For next time==

-	#~~Results outline~~	+	#Now that you have completed all of the assays for Module 2 (except for analysis), prepare a high-level outline of your results section. Specifically, you should write sub-section titles, and beneath each title write a few short phrases indicating what content will be included in that sub-section. You don't need to include any specific numbers or even specific findings (except perhaps in the titles), just the ''type'' of content that you will have. Do, however, include some sense of your narrative logic -- topic sentences and transitions may be phrases rather than complete sentences, but in any case may be the best way to clarify your plans. '''Please feel free to turn this assignment in early, in order to get it back early, to improve usefulness as you write your reports.'''
-	~~#Extra credit, OR REQUIRED THIS YEAR?: Prepare a figure and caption~~ for ~~your SDS-PAGE~~, ~~along with~~ a ~~paragraph~~ of ~~text for the associated~~ results section. ~~Look up the expected molecular weight using the IPC sequence document~~ and ~~[http://www.bioinformatics.org/sms/prot_mw.html this] or~~ a ~~similar website~~. ~~Be sure~~ to ~~add ~ 3 KDa for~~ the ~~size of~~ the ~~N-terminus~~ of ~~pRSET (His tag~~, ~~etc)~~. ~~If you see two strong bands~~, ~~what do~~ you ~~think the second one is?~~	+

AgiStachowiak: /* Note */

2013-04-09T20:17:48Z

Note

←Older revision		Revision as of 20:17, 9 April 2013
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	==Note==		==Note==

-	<font color=red>~~doing a final pass for typos~~, ~~etc~~. now</font color>	+	<font color=red>A few additions were made to the end of the introduction, as well as to the protocol, on Tuesday afternoon. Should be all set now!</font color>

	==Introduction==		==Introduction==

AgiStachowiak: /* Preparation */

2013-04-09T20:16:01Z

Preparation

←Older revision		Revision as of 20:16, 9 April 2013
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	====Preparation====		====Preparation====

-	# Download these three files: [[Media:S12_Fit_Main.m \| S12_Fit_Main]], [[Media:Fit_SingleKD.m\| Fit_SingleKD]], and [[Media:Fit_KDn.m\| Fit_KDn]]~~. If using a computer in 16-336, the files will be available in the ''Downloads'' folder under your username~~. Move them to the username/Documents/MATLAB folder on your PC.	+	#On the computers in 16-336, use the "Learning Biologic Genie" account. We will provide the password during class.
		+	#Download these three files: [[Media:S12_Fit_Main.m \| S12_Fit_Main]], [[Media:Fit_SingleKD.m\| Fit_SingleKD]], and [[Media:Fit_KDn.m\| Fit_KDn]]. Move them to the username/Documents/MATLAB folder on your PC.
	# Double-click on the <small>MATLAB</small> icon to start up this software.		# Double-click on the <small>MATLAB</small> icon to start up this software.
	# The main window that opens is called the command window: here is where you run programs (or directly input commands) and view outputs. You can also see and access the command history, workspace, and current directory windows, but you likely won’t need to today.		# The main window that opens is called the command window: here is where you run programs (or directly input commands) and view outputs. You can also see and access the command history, workspace, and current directory windows, but you likely won’t need to today.

AgiStachowiak: /* Protocols */

2013-04-09T20:11:02Z

Protocols

←Older revision		Revision as of 20:11, 9 April 2013
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	==Protocols==		==Protocols==

-	~~Ultimately, you~~ will analyze your calcium titration assay data in two steps. First, you will get a rough feel for how your mutants changed (or didn't) compared to wild-type IPC by plotting the two replicate values and their average, in both raw and processed form. Second, you will take the average processed values and plug them into some MATLAB code that will more precisely tell you the affinity and cooperativity of each protein with respect to calcium.	+	You will analyze your calcium titration assay data in two steps. First, you will get a rough feel for how your mutants changed (or didn't) compared to wild-type IPC by plotting the two replicate values and their average, in both raw and processed form. Second, you will take the average processed values and plug them into some MATLAB code that will more precisely tell you the affinity and cooperativity of each protein with respect to calcium.

	===Part 1: Titration curve in Excel and first estimate of K<sub>D</sub>===		===Part 1: Titration curve in Excel and first estimate of K<sub>D</sub>===

AgiStachowiak: /* Protocols */

2013-04-09T20:10:44Z

Protocols

←Older revision		Revision as of 20:10, 9 April 2013
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	==Protocols==		==Protocols==

-	~~Begin by applying the practice analysis from [[20.109%28S12%29:Bacterial_amplification_of_DNA_%28Day3%29#Part_5:_Practice_analysis_.28optional_-_but_a_good_idea.21.29 \| Day 3~~, ~~Part 5]] of this module to~~ your ~~real~~ data. ~~Recall that here~~ you ~~plot titration curves in Excel and make~~ a ~~first crude estimate of K<sub>D</sub>~~ values. ~~Next~~, you will ~~use <small>MATLAB</small> to get improved estimates of K<sub>D</sub>s~~ and ~~also assess cooperativity. <font color=red>'''The <small>~~MATLAB~~</small>~~ code ~~is now up~~ to ~~date~~.~~'''</font color>~~	+	Ultimately, you will analyze your calcium titration assay data in two steps. First, you will get a rough feel for how your mutants changed (or didn't) compared to wild-type IPC by plotting the two replicate values and their average, in both raw and processed form. Second, you will take the average processed values and plug them into some MATLAB code that will more precisely tell you the affinity and cooperativity of each protein with respect to calcium.

	===Part 1: Titration curve in Excel and first estimate of K<sub>D</sub>===		===Part 1: Titration curve in Excel and first estimate of K<sub>D</sub>===

-	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 (your curve should resemble Nagai's Figure 3L), then move on to your mutant samples. If you are not familiar with manipulations in Excel, use the ''Help'' menu or ask the teaching faculty for assistance.	+	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 (your curve should resemble Nagai's Figure 3L), and then move on to your mutant samples. If you are not familiar with manipulations in Excel, use the ''Help'' menu or ask the teaching faculty for assistance.

	#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: 10 nM, 50 nM, 100 nM, 200 nM, 400 nM, 500 nM, 600 nM, 800 nM, 1 μM, 2.5 μM, 10 μM, 100 μM. Be sure to convert all concentrations to the same units.		#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: 10 nM, 50 nM, 100 nM, 200 nM, 400 nM, 500 nM, 600 nM, 800 nM, 1 μM, 2.5 μM, 10 μM, 100 μM. Be sure to convert all concentrations to the same units.
-	#Now open the text file containing your raw data as a tab-delimited file in Excel (you can download the file from today's [[Talk:20.109%~~28S10~~%29:~~Data_analysis_~~%28Day8%29 \| Talk]] page). 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, and analyze each replicate separately for now. Also include a column of your control samples that did not contain protein.	+	#Now open the text file containing your raw data as a tab-delimited file in Excel (you can download the file from today's [[Talk:20.109%28S13%29:Assess_protein_function_%28Day8%29 \| Talk]] page). 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, and analyze each replicate separately for now. Also include a column of your control samples that did not contain protein.
	#Begin by calculating the average of your blank samples, and bold this number for easy reference. It is the background fluorescence present in the calcium solutions and should be quite low. If necessary, subtract this background value from each of your raw data values. It may help to have a 6-column series called “RAW”, and another called “SUBTRACTED.”		#Begin by calculating the average of your blank samples, and bold this number for easy reference. It is the background fluorescence present in the calcium solutions and should be quite low. If necessary, subtract this background value from each of your raw data values. It may help to have a 6-column series called “RAW”, and another called “SUBTRACTED.”
	#Next you should normalize your data. The maximum and minimum fluorescence values for a given titration series should be defined as 100% and 0% fluorescence, respectively, and every other fluorescence value should be expressed as a percentage in between. Think about how to mathematically express these conditions.		#Next you should normalize your data. The maximum and minimum fluorescence values for a given titration series should be defined as 100% and 0% fluorescence, respectively, and every other fluorescence value should be expressed as a percentage in between. Think about how to mathematically express these conditions.

AgiStachowiak: /* Protocols */

2013-04-09T20:05:44Z

Protocols

←Older revision		Revision as of 20:05, 9 April 2013
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	Begin by applying the practice analysis from [[20.109%28S12%29:Bacterial_amplification_of_DNA_%28Day3%29#Part_5:_Practice_analysis_.28optional_-_but_a_good_idea.21.29 \| Day 3, Part 5]] of this module to your real data. Recall that here you plot titration curves in Excel and make a first crude estimate of K<sub>D</sub> values. Next, you will use <small>MATLAB</small> to get improved estimates of K<sub>D</sub>s and also assess cooperativity. <font color=red>'''The <small>MATLAB</small> code is now up to date.'''</font color>		Begin by applying the practice analysis from [[20.109%28S12%29:Bacterial_amplification_of_DNA_%28Day3%29#Part_5:_Practice_analysis_.28optional_-_but_a_good_idea.21.29 \| Day 3, Part 5]] of this module to your real data. Recall that here you plot titration curves in Excel and make a first crude estimate of K<sub>D</sub> values. Next, you will use <small>MATLAB</small> to get improved estimates of K<sub>D</sub>s and also assess cooperativity. <font color=red>'''The <small>MATLAB</small> code is now up to date.'''</font color>
		+
		+	===Part 1: Titration curve in Excel and first estimate of K<sub>D</sub>===
		+
		+	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 (your curve should resemble Nagai's Figure 3L), then move on to your mutant samples. If you are not familiar with manipulations in Excel, use the ''Help'' menu or ask the teaching faculty for assistance.
		+
		+	#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: 10 nM, 50 nM, 100 nM, 200 nM, 400 nM, 500 nM, 600 nM, 800 nM, 1 μM, 2.5 μM, 10 μM, 100 μM. Be sure to convert all concentrations to the same units.
		+	#Now open the text file containing your raw data as a tab-delimited file in Excel (you can download the file from today's [[Talk:20.109%28S10%29:Data_analysis_%28Day8%29 \| Talk]] page). 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, and analyze each replicate separately for now. Also include a column of your control samples that did not contain protein.
		+	#Begin by calculating the average of your blank samples, and bold this number for easy reference. It is the background fluorescence present in the calcium solutions and should be quite low. If necessary, subtract this background value from each of your raw data values. It may help to have a 6-column series called “RAW”, and another called “SUBTRACTED.”
		+	#Next you should normalize your data. The maximum and minimum fluorescence values for a given titration series should be defined as 100% and 0% fluorescence, respectively, and every other fluorescence value should be expressed as a percentage in between. Think about how to mathematically express these conditions.
		+	#*First calculate the percent fluorescence for both replicates. Then make a new column and calculate the average percentage as well. Alternatively, average your data first, and then normalize the average data.
		+	#*If one data point seems really off from the other replicate and from the expected trend, you might consider it an outlier and delete it, especially if you have good reason to believe that there was a reason (error in pipetting, air bubble in that well) for the anomaly. Otherwise, you might be losing valuable information, and/or misleading anyone who tries to interpret your data.
		+	#For each protein, plot this normalized data versus calcium concentration. Save these plots in case you want to include them in your report.
		+	#*You might plot the two replicates as points and their average value as a dashed line (see front page of this module).
		+	#Note down the approximate inflection points of the curves, which should occur at half-saturation: these indicate the approximate values of the apparent <math>K_D</math> for each sample.
		+
		+	===Part 2: Improved estimate of K<sub>D</sub> using <small>MATLAB</small> modeling===

	====Preparation====		====Preparation====

AgiStachowiak: /* Introduction */

2013-04-09T20:04:24Z

Introduction

←Older revision		Revision as of 20:04, 9 April 2013
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	For any given mutant, things may be more complicated. Keep in mind that we are not directly measuring calcium binding, but instead are indirectly inferring it based on fluorescence (for both mutant and wild-type IPC). Said change in fluorescence requires the participation not only of calcium, but also of M13. So besides the four separate calcium binding sites in calmodulin, the M13 binding site influences apparent affinity and apparent cooperativity. In short, be careful about how you describe the meanings of our binding parameters in your reports.		For any given mutant, things may be more complicated. Keep in mind that we are not directly measuring calcium binding, but instead are indirectly inferring it based on fluorescence (for both mutant and wild-type IPC). Said change in fluorescence requires the participation not only of calcium, but also of M13. So besides the four separate calcium binding sites in calmodulin, the M13 binding site influences apparent affinity and apparent cooperativity. In short, be careful about how you describe the meanings of our binding parameters in your reports.

-	~~When~~ you write your research article, be sure to consider how changes in both binding affinity and cooperativity (and even potentially raw fluorescence differences) can affect the practical utility of a sensor.	+	Returning to the big picture: when you write your research article, be sure to consider how changes in both binding affinity and cooperativity (and even potentially raw fluorescence differences) can affect the practical utility of a sensor.

	==Protocols==		==Protocols==