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==Introduction==
 
==Introduction==
  
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As evidenced by Nagai’s work, wild-type inverse pericam is not toxic to BL21(DE3)pLysS cells. Although it is unlikely that your point mutation will dramatically change this fact, in general a novel protein may turn out to be toxic. If this is the case, only very small amounts of protein are produced before the bacteria die. Keep in mind that overexpressing a single protein may come at the expense of producing proteins needed for survival, and will most likely cause cell death eventually; however, toxic proteins hasten this demise. Aberrant toxicity can sometimes be alleviated by reducing the culture temperature (e.g., to 30 °C).
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Congratulations on reaching your final (virtual) laboratory day in 20.109!  To complete your experience and training, the goal for today to synthesize the data that you collected and analyzed throughout this module and refine the approach such that an improved hypothesis can be tested. By pulling all of this information together you will, hopefully, be able to use the data generated by previous 109ers to make more informed mutations that alter affinity and / or cooperativity in IPC.  This module highlights the basis of scientific research as an iterative process that consists of four stages: designing experiments, collecting data, analyzing results, and refining the approach. This rigorous cycle is how we ensure our results are accurate and reproducible!
  
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Based on its fluorescence activity, wild-type inverse pericam allows proper folding of (cp)EYFP, and based on its response to calcium, it also allows calmodulin to fold. One problem you may encounter is that your mutant proteins will no longer fold correctly. Since you made mutations in the calcium sensor part of IPC, rather than the fluorescent part, it is unlikely that your protein will destroy EYFP fluorescence. However, a common problem with misfolded proteins is the formation of insoluble aggregates, due for instance to improperly exposed hydrophobic surfaces. Proteins can be purified from these aggregates – called inclusion bodies – but the process is more labor-intensive than for soluble proteins. (The proteins must be extracted under more harsh conditions than you will use next time, then purified under denaturing conditions, before finally attempting to renature the proteins.) Inclusion bodies sometimes form simply due to very high expression of the protein of interest, causing it to pass its solubility limit. This outcome can be prevented by lowering the culture temperature, the induction duration, the amount of IPTG, or the growth phase of the bacteria.
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When considering results that will be used to refine a research approach, it is important to recognize that not all data are created equal. For several reasons, including technical error and reagent failure, it often happens that an experiment does not work as expected. By including controls, researchers are able to identify these issues and rectify them in follow-up experiments. In addition to using controls that validate the results, researchers use replicates and repeat experiments to ensure the data are robust. All of these internal checks allow researchers to be confident about the results they report.
  
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One final point to keep in mind is that not all proteins can be produced in bacteria. Eukaryotic proteins that require post-translational modifications (such as glycosylation) for activity require eukaryotic hosts (such as yeast, or the commonly used CHO – Chinese hamster ovary – cells). Sometimes eukaryote-derived proteins will be truncated or otherwise mistranslated by E. coli due to differential codon bias; errors in translation can be prevented by providing additional tRNAs to the culture or directly to the bacteria via plasmids. Despite all this complexity, prokaryotic hosts have been plenty good enough to produce proteins for certain therapies, notably the cytokine G-CSF. This cytokine is taken by patients needing to replenish their white blood cells (e.g., after chemotherapy), and sold as Neupogen by the company Amgen.
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Today you will critically think about the data that you analyzed in this module and rationally design an IPC with altered affinity / cooperativity given what you learned in your research. Though considering the results of the current Variant IPC is important in your goal for today, it is just as important to decide which results are relevant or valid to your design strategy.
  
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Though you will not have the opportunity to test your Variant IPC this semester, you are more than welcome to complete the calcium titration experiment with your protein as soon as you are invited back to campus!  We will be more than happy to host you in the laboratory for some actual benchwork!!
  
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This is it, folks! Moment of truth. Time to find out how the proteins that you worked so hard to express, purify, and test really behave. Although you should be able to produce reasonable titration curves by following the example of Nagai, the introduction/review of binding constants below may help contextualize your analysis.
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==Protocols==
  
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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 can be written
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===Part 1: Review site-directed mutagenesis===
  
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<center>
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Site-directed mutagenesis (SDM) refers to the a method used to incorporate specific and targeted sequence changes, or mutations, into double-stranded plasmid DNA.  There are several experimental questions that can be answered by incorporating specific mutations, for example:
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<math> R + L  \rightleftharpoons\ ^{k_f}_{k_r}      C </math>
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*How do amino acid substitutions alter protein / enzyme activity?
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</center>
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*How do basepair changes alter binding activity / partners at promoter sequences?
  
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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,
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To perform SDM, custom designed oligonucleotides, or primers, are used to incorporate mutations into double-stranded DNA plasmid as a specific location in the sequence.  One approach is to use primers that align to the sequence in the plasmid in a back-to-back orientation.  As shown in top left of the schematic below, the primers (forward primer = black arrow and reverse primer = red arrow) anneal to the plasmid such that the 5' ends of the primers anneal to the DNA in a back-to-back orientation. In Step #1 of the schematic, the forward primer is used to replicate the top strand (outside circle of the plasmid) and the reverse primer is used to replicate the bottom strand (inside circle of the plasmid). The resulting single-stranded products (extension from each primer generates a single-stranded product) are able to anneal due to sequence homology, as shown in the first quadrant of the zoom-in for Step #2.  In Step #2A the 5' ends of the linear, single-stranded amplification products are phosphorylated to prepare for ligation (Step #2B). Remember that a 5' phosphate is required for 3' OH nucleophilic attack, this results in circular plasmids. 
  
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<center>
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Thus far in this description of SDM, one very important detail has not been mentioned.  How specifically are the mutations coded in the primers incorporated into the plasmid sequence?  In the top left of the schematic, the forward primer contains a hash mark that represents the desired mutation.  The single-stranded product that results from extension from this primer will contain the desired mutation and therefore be incorporated into the products generated in Step #1.  Lastly, in Step #2C the plasmid template that contains the unmutated parental sequence is destroyed so that only the plasmids with the desired mutation are present at the end of the procedure.
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<math>\qquad y = {[C] \over R_{TOT}} \qquad = \qquad {[C] \over [C] + [R]} \qquad = \qquad {[L] \over [L] + [K_d]} \qquad</math>
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</center>
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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:
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[[Image:Sp16 M1D3 SDM schematic.png|thumb|center|650px|'''Schematic of NEB Q5 Site Directed Mutagenesis procedure.''' Image modified from Q5 Site-Directed Mutagenesis Kit Manual published by NEB.]]
  
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*What happens when ''L'' << <math>K_d</math>?
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===Part 2: Identify amino acid substitution target for new IPC design===
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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''.  
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Your first task for today is to review the data analysis you completed on M3D3 and decide which Variant IPC data you will consider when designing your Variant IPC. After you choose which amino acid you think is the best target for altering affinity / cooperativity, consider what amino acid you want to include instead.
  
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*What happens when ''L'' >> <math>K_d</math>?
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In Part 3, you will generate the primers that can be used to incorporate a specific amino acid substitution to create your Variant IPC! 
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::&rarr;In this case ''y'' ~1, so the binding fraction becomes approximately constant, and the receptors are saturated.
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*What happens when ''L'' = <math>K_d</math>?
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::&rarr;Then ''y'' = 0.5, and the fraction of receptors that are bound to ligand is 50%. This is why you can read <math>K_d</math> directly off of the plots in Nagai’s paper (compare Figure 3 and Table 1). When ''y'' = 0.5, the concentration of free calcium (our [''L'']) is equal to <math>K_d</math>. '''This is a great rule of thumb to know.'''
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The figures below demonstrate how to read <math>K_d</math> from binding curves. You will find semilog plots (right) particularly useful today, but the linear plot (left) can be a helpful visualization as well. Keep in mind that every ''L'' value is associated with a particular equilbrium value of ''y'', while the curve as a whole gives information on the global equilibrium constant <math>K_d</math>.
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[[Image:20109 Fa15 M2D7 figure.png|thumb|300px|left|'''Simple binding curve.''' The binding fraction ''y'' at first increases linearly as the starting ligand concentration is increased, then asymptotically approaches full saturation (''y''=1). The dissociation constant <math>K_d</math> is equal to the ligand concentration [''L''] for which ''y'' = 1/2.]]
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[[Image:20109 Fa15 M2D7 figure2.png|thumb|300px|center|'''Semilog binding curves.''' By converting ligand concentrations to logspace, the dissociation constant is readily determined from the inflection point of the sigmoidal curve. The three curves each represent different ligand species. The middle curve has a <math>K_d</math> close to 10 nM, while the right-hand curve has a higher <math>K_d</math> and therefore lower affinity between ligand and receptor (vice-versa for the left-hand curve).]]
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<br style="clear:both;"/>
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Of course, inverse pericam has multiple binding sites, and thus IPC-calcium binding is actually more complicated than the example above. The <math>K_d</math> reported by Nagai is called an ‘apparent <math>K_d</math>’ 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 low affinity binding at both termini. 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 inverse pericam behaves as if it were binding only a single calcium ion per molecule. Thus, wild-type IPC is well-described by a single apparent <math>K_d</math>.
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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). A change in fluorescence requires the participation not only of calcium, but also of M13. In addition to 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.
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Returning to the big picture: when you write your Protein engineering summary, 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.
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==Protocols==
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===Part 2: Prepare samples for titration curve===
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<font color = #4a9152 >'''In your laboratory notebook,'''</font color> complete the following:
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*What amino acid will you target using SDM?  At what position is this amino acid located in the protein sequence?  What amino acid will be incorporated in its place?
 +
*Provide the rational for your design choice.
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**Why do you think the target amino acid you selected will alter affinity / cooperativity?
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**How do you think the amino acid substitution will alter affinity / cooperativity?
  
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==== Tips for success====
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===Part 3: Design primers for site-directed mutagenesis===
  
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Take great care today to limit the introduction of bubbles in your samples. When expelling fluid, pipet '''''slowly''''' while touching the pipet tip against the bottom or side of the well.
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It is not experimentally efficient, or entirely plausible, to pick out and modify a single amino acid residue in inverse pericam post-translationally. Instead researchers genetically encode for amino acid substitutions by incorporating mutations in the DNA sequence. This is accomplished by making changes to the basepairs of a gene of interest that was cloned into a plasmid. Then the plasmid with the mutated gene is amplified using bacterial cells.
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<!--
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When using the multichannel pipet, always check to make sure all tips are getting filled - sometimes one tip may not be on all the way, and will pull up less volume than the others. If this happens, release the fluid, adjust the tip, and try again.
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-->
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====Protocol====
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[[Image:Sp16 M1D2 primer design schematic.png|thumb|right|300px| '''Schematic for mutating gene sequences in plasmids using SDM technique.''' Image modified from Q5 Site-Directed Mutagenesis Kit Manual published by NEB.]]To incorporate a mutation at a specific location in the DNA sequence, synthetic primers can be used in a technique referred to site-direction mutagenesis (see figure on the right). Primer design for site-directed mutagenesis, or SDM, is quite straightforward: the forward primer introduces a mutation into the coding strand. Both non-mutagenic and mutagenic amplification require cycles of DNA melting, annealing, and extension.
  
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[[File:Fa15 Protein Ca assay plate map.png|thumb|450px|right|Titration plate map]]
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Primers used in SDM must meet several design criteria to ensure specificity and efficiency. Consider the following design guidelines for mutagenesis primers:
  
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#Take a black 96-well plate, and familiarize yourself with the plate map scheme at right: top two rows are to be loaded with wild-type IPC, next two rows are to be loaded with your X#Z mutant IPC, and the final row is to be loaded with  water/BSA to serve as a blank/background row.  
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*Desired mutation (1-2 bp) must be present in the middle of the forward primer.
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#*The dark sides of the plate reduce "cross-talk" (''i.e.'', light leakage) between samples in adjacent wells, another potential contribution to error.
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*Forward and reverse primers should 'face' away from the mutation and be 'back-to-back' when annealed to the template.
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#Aliquot your wild-type protein to your plate. Use your P200 pipet to add 30 μL of protein (per well) to rows A and B of your plate.
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*Primers should be 25-45 bp long.
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#Aliquot you X#Z mutant IPC to your plate.  Use your P200 pipet and add 30 μL of protein (per well) to rows C and D of your plate.
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*G/C content of > 40% is desired.
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#Finally, add 30 μL of water with only 0.1% BSA (no IPC) to row 5(E) of your plate.
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*Both primers should terminate in at least one G or C base.
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#The calcium solutions are at the front bench in shared reservoirs. '''Carefully''' carry your plate to the front bench to add these solutions with the  multi-channel pipet. 
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*Melting temperature should exceed 78&deg;C, according to:
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#Using shared reservoir #1 (lowest calcium concentration - actually 0 nM), add 30 μL to the top five rows in the first column of the plate. Discard the pipet tips.
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**T<sub>m</sub> = 81.5 + 0.41 (%GC) – 675/N - %mismatch
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#Now work your way from reservoirs #2 to #12 (highest calcium concentration), and from the left-hand to the right-hand columns on your plate. Be sure to use fresh pipet tips each time! If you do contaminate a solution, let the teaching faculty know so they can put out some fresh solution. Honesty about a mistake is far preferred here to affecting every downstream experiment.
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**where N is primer length and the two percentages should be integers
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#When you are done, alert the teaching faculty and you will be taken in small groups to measure the fluorescence values for your samples.
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===Part 3: Fluorescence assay===
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To demonstrate primer design, the illustration below uses S101L, which is an uninteresting mutation but a helpful example:
  
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#The BMC (BioMicro Center) has graciously agreed to let us use their plate reader. Walk over to building 68 with a member of the teaching staff.
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Residue 101 of calmodulin is serine, encoded by the AGC codon. This is residue 379 with respect to the entire inverse pericam construct,
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#You will be shown how to set the excitation (485 nm) and emission (515 nm) wavelength on the plate reader to assay your protein.
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and we can find it and some flanking code in the DNA sequence from Part 2:
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#Your raw data will be posted on today's [[Talk:20.109(S16):Characterize protein expression (Day7)|Discussion page]] and emailed to you as a .txt file so you can begin your analysis.
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You will analyze your calcium titration assay data in two steps. First, you will get a rough feel for how your mutant 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 <small>MATLAB</small> code that will more precisely tell you the affinity and cooperativity of each protein with respect to calcium.
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<font face="courier">
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<small>
  
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===Part 1: Titration curve in Excel and first estimate of ''K<sub>d</sub>''===
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361 (5') GAG GAA ATC CGA GAA GCA TTC CGT GTT TTT GAC AAG GAT GGG AAC GGC TAC ATC AGC GCT (3')
  
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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.
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381 (5') GCT CAG TTA CGT CAC GTC ATG ACA AAC CTC GGG GAG AAG TTA ACA GAT GAA GAA GTT GAT (3')
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</small>
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</font>
  
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#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 final concentrations are: 0 nM, 8.5 nM, 19 nM, 32.5 nM, 50 nM, 75 nM, 112.5 nM, 175 nM, 301 nM, 675 nM, 1.505 &mu;M, 19.5 &mu;M. Be sure to convert all concentrations to the same units.<br>
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To change from serine to leucine, one might choose TTA, TTG, or CTN (wherer N = T, A, G, or C). Because CTC requires only two mutations (rather than three as for the other options), we choose this codon.  
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#Now open the text file containing your raw data as a tab-delimited file in Excel (you can download the file from the  [[Talk:20.109(S16):Characterize protein expression (Day7) | M1D7 Discussion]] page). Convert the row-wise data to column-wise data (using ''Paste Special'' &rarr; ''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.
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#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.”
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#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.
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#*First calculate the percent fluorescence for both replicates. Then make a new column and calculate the average percentage as well.
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#*Alternatively, average your data first, and then normalize the average data.  How do the "average then normalize" and the "normalize then average" curves compare?  Which one will you include in your Protein engineering summary?
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#*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.
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#For each protein, plot this normalized data versus calcium concentration. Save these plots in case you want to include them in your report.
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#*You might plot the two replicates as points and their average value as a dashed line (see [http://engineerbiology.org/wiki/20.109%28S16%29:Module_1 front page] of this module).
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#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.
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===Part 2: Improved estimate of ''K<sub>d</sub>'' using <small>MATLAB</small> modeling===
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Now we must keep >10 bp of sequence on each side in a way that meets all our requirements. To quickly find G/C content and see secondary structures, look at the [https://www.idtdna.com/pages/tools/oligoanalyzer IDT website]. (Note that the T<sub>m</sub> listed at this site is '''''not''''' one that is relevant for mutagenesis.)
  
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====Preparation====
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Ultimately, your forward primer might look like the following, which has a T<sub>m</sub> of almost 81&deg;C, and a G/C content of ~58%.
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#Download these three files: [[Media:F15 Fit Main.m | F15_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 computer.
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# Double-click on the <small>MATLAB</small> icon to start up this software.
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# 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.
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# In the command window, type ''more on''; this command allows you to scroll through multi-page output (using the spacebar), such as help files.
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# In addition to the command area, <small>MATLAB</small> comes with an editor. Click ''File'' &rarr; ''Open'' and select the program '''F15_Fit_Main'''. It has the .m extension and thus is executable by <small>MATLAB</small>. Read the introductory comments (the beginning of a comment is indicated by a % sign), and then input your fluorescence data.
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# Read through the program, and as you encounter unfamiliar terms, return to the workspace and type ''help functionname''. Feel free to ask questions of the teaching faculty as well.
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#* You might read about such built-in functions as ''logspace'' and ''nlinfit''.
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#* You will also want to open and read '''Fit_SingleKD''' – a user-defined function called by '''F15_Fit_Main''' – in the <small>MATLAB</small> editor.
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#* If you type ''help function'' you will learn the syntax for a function header.
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#* Note that a dot preceeding an operator (such as A ./ B or A .* B) is a way of telling <small>MATLAB</small> to perform element-by-element rather than matrix algebra.
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#* Also note that when a line of code is ''not'' followed by a semi-colon, the value(s) resulting from the operation will be displayed in the command window.
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====Analysis====
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<font face="courier">
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5’ GG AAC GGC TAC ATC CTC GCT GCT CAG TTA CGT CAC G 3'
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</font><br>
  
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# Once you more-or-less follow Part 1 of the program, type '''F15_Fit_Main''' in the workspace, hit return to run the program, and consider the following questions:
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The reverse primer is the inverse complement of a sequence just preceding the forward primer in the IPC gene. The forward and reverse primers are set up back-to-back.
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#* Why must the fluorescence data be transformed (from ''S'' to ''Y'') prior to using in the model?
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#* What <math>K_d</math> values are output in the command window, and how do they compare to the values you estimated from your Excel plots?
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#* Figure 1 should display your wild type and mutant data points and model curves. How do they look in comparison to the curves you plotted in Excel?
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#* Figure 2 should display the residuals (difference between data and model) for your three proteins. If the absolute values are low, this indicates good agreement between the model and the data numerically. Whether or not this is the case, another thing to look for is whether the residuals are evenly and randomly distributed about the zero-line. If there is a pattern to the errors, likely there is a systematic difference between the data and the model, and thus the model does not reflect the actual binding process well. What are the residuals like for each of your modeled proteins?
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# Now move on to Part 2 of the '''F15_Fit_Main''' program. Part 2 also fits the data to a model with a single, ‘apparent’ value of <math>K_d</math>, but it allows for multiple binding sites and tests for cooperativity among them. The parameter used to measure cooperativity is called the Hill coefficient. A Hill coefficient of 1 indicates independent binding sites, while greater or lesser values reflect positive or negative cooperativity, respectively. Let the following questions guide you as you proceed:
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#*Visually, which model appears to fit your wild-type data better (Fig. 3 ''vs.'' Fig. 1)? Your mutant data?
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#*Do the respective residuals support your qualitative assessment (Fig. 4 ''vs.'' Fig. 2)?
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#*Numerically, how do the values of <math>K_d</math> compare for the two models? How does the value of ''n'' compare to the implicitly assumed value of 1 in Part 1?
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#*Do you see changes in binding affinity and/or cooperativity between the wild-type and X#Z samples? Do they match your ''a priori'' predictions?
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#*'''Don't forget to save any figures you want to use in your report!''' If the legends are covering up your data, you can simply move them over with your mouse.
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# Finally, you can skim Part 3 of the '''F15_Fit_Main''' program. Make sure you update the range of the linear transition region for each IPC sample, but beyond this, don’t worry too much about the coding details; rather do read through the comments.  
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#* Look at Part 1 of Figure 5: are the binding curves asymptotic, sigmoidal, or other? What does this shape indicate? You can use the zoom button to get a closer look at part of the plot, or the ''axis'' command present in the code. (Don't worry too much about this question if it is unclear.)
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#* Now look in the command window. What values of <math>K_d</math> and Hill coefficient (''n'') do you get for your three proteins? How do the <math>K_d</math>’s from Part 3 compare to the ones from Parts 1 and 2? Don’t be discouraged if your wild-type values do not exactly match Nagai’s work, or if there is variation between Parts 1, 2, and 3.
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#*Comparing the model and data points by eye (Part 2 of Figure 5), do you think it is a good model for any of your proteins? If so, which ones? What experimental limitations might prevent Hill analysis from working well, especially for some mutants?
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#*Why should only the transition region be analyzed in a Hill plot?
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#* What is the relationship between slope and <math>K_d</math> and/or ''n'', and intercept and <math>K_d</math> and/or ''n''?
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#If your mutant proteins are not well-described by any of the models so far, what kind of model(s) (qualitatively speaking) do you think might be useful?
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#*Optional: If your data might be well-described by a model with two <math>K_d</math>'s (or if you are interesting in exploring some sample data that is), download and run [[Media:Fit_TwoKD.m | Fit_TwoKD]] and [[Media:Fit_TwoKD_Func.m | Fit_TwoKD_Func]].
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==Reagent list==
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Luckily, online tools are available to assist with SDM primer design.  Today you will use NEBaseChanger (provided by NEB) to design your mutagenic primers.
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#Go to the [http://nebasechanger.neb.com/ NEBaseChanger] site and click 'Please enter a new sequence to begin.'
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#*A new window will open. 
 +
#Copy and paste the WT IPC sequence.
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#*This sequence should be saved in SnapGene from the M3D1 exercise.  Alternatively, you can copy the sequence from the word document attached to the M3D1 wiki page.
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#Confirm that the 'Substitution' option is selected.
 +
#Highlight the basepairs you want to mutate using by scrolling through the sequence, or you can search the sequence by typing the basepairs into the 'Find' box.
 +
#Type the new DNA sequence (the basepair(s) you want your forward mutagenic primer to incorporate into the IPC sequence) in the 'Desired Sequence' box.
 +
#*Under the Result header, a diagram showing where your primers will anneal is provided.
 +
#*Under the Required Primers header, the sequences for your forward primer and reverse primer are shown with the characteristics for each.
 +
#<font color = #4a9152 >'''In your laboratory notebook,'''</font color> complete the following:
 +
#*Include a screen capture of the information provided in the Result and Required Primers sections.
 +
#*Use the guidelines provided above to examine the mutagenesis primers designed by NEBaseChanger.  Do the primers meet the design criteria?
  
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===Part 4: Start Mini-report assignment===
  
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*Calcium calibration kit from Life Technologies
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The final writing assignment in Module 3 is the Mini-report. In this assignment you will provide a description of the data analysis you completed to study the effects of mutating residues in IPC.  To help you organize your thoughts and to ensure you ready to prepare this assignment in the next laboratory session, '''work with your laboratory partner(s)''' to draft an outline of your Mini-report. Before you prepare your outline, review the [[20.109(S21):Mini-report |Mini-report assignment page]] for guidance!
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**Zero free calcium buffer: 10 mM EGTA in 100 mM KCl, 30 mM MOPS, pH 7.2
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**39 &mu;M free calcium buffer: 10 mM CaEGTA in 100 mM KCl, 30 mM MOPS, pH 7.2
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*Thermo Scientific Varioskan Flash Spectral Scanning Multimode Reader
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<font color =  #4a9152 >'''In your laboratory notebook,'''</font color> complete the following:
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*List the key topics that will be addressed / explained in the Background and Approach section.
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*List the figures that will be included and provide a brief description of how the data will be presented in the figures.
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**Not all of the figures that were generated are required for this assignment!  Carefully consider which data are useful / important in the rational design of your new Variant IPC.
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*Include a statement concerning the interpretation of the data represented in each figure.
  
 
==Navigation links==
 
==Navigation links==
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Next day:  [[20.109(S21):M3D5 |Design new IPC variant ]] <br>
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Previous day: [[20.109(S21):M3D3 |Evaluate effect of mutations on IPC variants ]] <br>
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Previous day: [[20.109(S21):M3D3 |Prepare expression system and purify IPC variants ]] <br>
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Latest revision as of 18:30, 11 May 2021

20.109(S21): Laboratory Fundamentals of Biological Engineering

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Spring 2021 schedule        FYI        Assignments        Homework        Communication |        Accessibility

       M1: Antibody engineering        M2: Drug discovery        M3: Protein engineering       


Introduction

Congratulations on reaching your final (virtual) laboratory day in 20.109! To complete your experience and training, the goal for today to synthesize the data that you collected and analyzed throughout this module and refine the approach such that an improved hypothesis can be tested. By pulling all of this information together you will, hopefully, be able to use the data generated by previous 109ers to make more informed mutations that alter affinity and / or cooperativity in IPC. This module highlights the basis of scientific research as an iterative process that consists of four stages: designing experiments, collecting data, analyzing results, and refining the approach. This rigorous cycle is how we ensure our results are accurate and reproducible!

When considering results that will be used to refine a research approach, it is important to recognize that not all data are created equal. For several reasons, including technical error and reagent failure, it often happens that an experiment does not work as expected. By including controls, researchers are able to identify these issues and rectify them in follow-up experiments. In addition to using controls that validate the results, researchers use replicates and repeat experiments to ensure the data are robust. All of these internal checks allow researchers to be confident about the results they report.

Today you will critically think about the data that you analyzed in this module and rationally design an IPC with altered affinity / cooperativity given what you learned in your research. Though considering the results of the current Variant IPC is important in your goal for today, it is just as important to decide which results are relevant or valid to your design strategy.

Though you will not have the opportunity to test your Variant IPC this semester, you are more than welcome to complete the calcium titration experiment with your protein as soon as you are invited back to campus! We will be more than happy to host you in the laboratory for some actual benchwork!!

Protocols

Part 1: Review site-directed mutagenesis

Site-directed mutagenesis (SDM) refers to the a method used to incorporate specific and targeted sequence changes, or mutations, into double-stranded plasmid DNA. There are several experimental questions that can be answered by incorporating specific mutations, for example:

  • How do amino acid substitutions alter protein / enzyme activity?
  • How do basepair changes alter binding activity / partners at promoter sequences?

To perform SDM, custom designed oligonucleotides, or primers, are used to incorporate mutations into double-stranded DNA plasmid as a specific location in the sequence. One approach is to use primers that align to the sequence in the plasmid in a back-to-back orientation. As shown in top left of the schematic below, the primers (forward primer = black arrow and reverse primer = red arrow) anneal to the plasmid such that the 5' ends of the primers anneal to the DNA in a back-to-back orientation. In Step #1 of the schematic, the forward primer is used to replicate the top strand (outside circle of the plasmid) and the reverse primer is used to replicate the bottom strand (inside circle of the plasmid). The resulting single-stranded products (extension from each primer generates a single-stranded product) are able to anneal due to sequence homology, as shown in the first quadrant of the zoom-in for Step #2. In Step #2A the 5' ends of the linear, single-stranded amplification products are phosphorylated to prepare for ligation (Step #2B). Remember that a 5' phosphate is required for 3' OH nucleophilic attack, this results in circular plasmids.

Thus far in this description of SDM, one very important detail has not been mentioned. How specifically are the mutations coded in the primers incorporated into the plasmid sequence? In the top left of the schematic, the forward primer contains a hash mark that represents the desired mutation. The single-stranded product that results from extension from this primer will contain the desired mutation and therefore be incorporated into the products generated in Step #1. Lastly, in Step #2C the plasmid template that contains the unmutated parental sequence is destroyed so that only the plasmids with the desired mutation are present at the end of the procedure.

Schematic of NEB Q5 Site Directed Mutagenesis procedure. Image modified from Q5 Site-Directed Mutagenesis Kit Manual published by NEB.

Part 2: Identify amino acid substitution target for new IPC design

Your first task for today is to review the data analysis you completed on M3D3 and decide which Variant IPC data you will consider when designing your Variant IPC. After you choose which amino acid you think is the best target for altering affinity / cooperativity, consider what amino acid you want to include instead.

In Part 3, you will generate the primers that can be used to incorporate a specific amino acid substitution to create your Variant IPC!

In your laboratory notebook, complete the following:

  • What amino acid will you target using SDM? At what position is this amino acid located in the protein sequence? What amino acid will be incorporated in its place?
  • Provide the rational for your design choice.
    • Why do you think the target amino acid you selected will alter affinity / cooperativity?
    • How do you think the amino acid substitution will alter affinity / cooperativity?

Part 3: Design primers for site-directed mutagenesis

It is not experimentally efficient, or entirely plausible, to pick out and modify a single amino acid residue in inverse pericam post-translationally. Instead researchers genetically encode for amino acid substitutions by incorporating mutations in the DNA sequence. This is accomplished by making changes to the basepairs of a gene of interest that was cloned into a plasmid. Then the plasmid with the mutated gene is amplified using bacterial cells.

Schematic for mutating gene sequences in plasmids using SDM technique. Image modified from Q5 Site-Directed Mutagenesis Kit Manual published by NEB.
To incorporate a mutation at a specific location in the DNA sequence, synthetic primers can be used in a technique referred to site-direction mutagenesis (see figure on the right). Primer design for site-directed mutagenesis, or SDM, is quite straightforward: the forward primer introduces a mutation into the coding strand. Both non-mutagenic and mutagenic amplification require cycles of DNA melting, annealing, and extension.

Primers used in SDM must meet several design criteria to ensure specificity and efficiency. Consider the following design guidelines for mutagenesis primers:

  • Desired mutation (1-2 bp) must be present in the middle of the forward primer.
  • Forward and reverse primers should 'face' away from the mutation and be 'back-to-back' when annealed to the template.
  • Primers should be 25-45 bp long.
  • G/C content of > 40% is desired.
  • Both primers should terminate in at least one G or C base.
  • Melting temperature should exceed 78°C, according to:
    • Tm = 81.5 + 0.41 (%GC) – 675/N - %mismatch
    • where N is primer length and the two percentages should be integers

To demonstrate primer design, the illustration below uses S101L, which is an uninteresting mutation but a helpful example:

Residue 101 of calmodulin is serine, encoded by the AGC codon. This is residue 379 with respect to the entire inverse pericam construct, and we can find it and some flanking code in the DNA sequence from Part 2:

361 (5') GAG GAA ATC CGA GAA GCA TTC CGT GTT TTT GAC AAG GAT GGG AAC GGC TAC ATC AGC GCT (3')

381 (5') GCT CAG TTA CGT CAC GTC ATG ACA AAC CTC GGG GAG AAG TTA ACA GAT GAA GAA GTT GAT (3')

To change from serine to leucine, one might choose TTA, TTG, or CTN (wherer N = T, A, G, or C). Because CTC requires only two mutations (rather than three as for the other options), we choose this codon.

Now we must keep >10 bp of sequence on each side in a way that meets all our requirements. To quickly find G/C content and see secondary structures, look at the IDT website. (Note that the Tm listed at this site is not one that is relevant for mutagenesis.)

Ultimately, your forward primer might look like the following, which has a Tm of almost 81°C, and a G/C content of ~58%.

5’ GG AAC GGC TAC ATC CTC GCT GCT CAG TTA CGT CAC G 3'

The reverse primer is the inverse complement of a sequence just preceding the forward primer in the IPC gene. The forward and reverse primers are set up back-to-back.

Luckily, online tools are available to assist with SDM primer design. Today you will use NEBaseChanger (provided by NEB) to design your mutagenic primers.

  1. Go to the NEBaseChanger site and click 'Please enter a new sequence to begin.'
    • A new window will open.
  2. Copy and paste the WT IPC sequence.
    • This sequence should be saved in SnapGene from the M3D1 exercise. Alternatively, you can copy the sequence from the word document attached to the M3D1 wiki page.
  3. Confirm that the 'Substitution' option is selected.
  4. Highlight the basepairs you want to mutate using by scrolling through the sequence, or you can search the sequence by typing the basepairs into the 'Find' box.
  5. Type the new DNA sequence (the basepair(s) you want your forward mutagenic primer to incorporate into the IPC sequence) in the 'Desired Sequence' box.
    • Under the Result header, a diagram showing where your primers will anneal is provided.
    • Under the Required Primers header, the sequences for your forward primer and reverse primer are shown with the characteristics for each.
  6. In your laboratory notebook, complete the following:
    • Include a screen capture of the information provided in the Result and Required Primers sections.
    • Use the guidelines provided above to examine the mutagenesis primers designed by NEBaseChanger. Do the primers meet the design criteria?

Part 4: Start Mini-report assignment

The final writing assignment in Module 3 is the Mini-report. In this assignment you will provide a description of the data analysis you completed to study the effects of mutating residues in IPC. To help you organize your thoughts and to ensure you ready to prepare this assignment in the next laboratory session, work with your laboratory partner(s) to draft an outline of your Mini-report. Before you prepare your outline, review the Mini-report assignment page for guidance!

In your laboratory notebook, complete the following:

  • List the key topics that will be addressed / explained in the Background and Approach section.
  • List the figures that will be included and provide a brief description of how the data will be presented in the figures.
    • Not all of the figures that were generated are required for this assignment! Carefully consider which data are useful / important in the rational design of your new Variant IPC.
  • Include a statement concerning the interpretation of the data represented in each figure.

Navigation links

Previous day: Evaluate effect of mutations on IPC variants
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