Difference between revisions of "Team:Hong Kong-CUHK/Model"

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<p style="font-family: quicksand;font-size:150%;">Assumptions</p><br>
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<p style="font-family: quicksand;font-size:150%;">Assumptions</p>
 
<p style="font-family: roboto;font-size:125%;"><u><b>Assumption: Switch MFE (Minimum Free Energy) correlates with the expression leakage</p></b></u>
 
<p style="font-family: roboto;font-size:125%;"><u><b>Assumption: Switch MFE (Minimum Free Energy) correlates with the expression leakage</p></b></u>
 
<p style="font-family: roboto;font-size:115%;">
 
<p style="font-family: roboto;font-size:115%;">
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K=equilibrium constant<br>
 
K=equilibrium constant<br>
 
<center>Switch RNA+Trigger RNA↔Duplex RNA</center><br>
 
<center>Switch RNA+Trigger RNA↔Duplex RNA</center><br>
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Therefore, we assume that the higher the MFE difference, the higher the switch-trigger duplex RNA concentration compared to that of the switch RNA when in equilibrium.<br>
+
Therefore, we assume that the higher the ΔMFE, the higher the switch-trigger duplex RNA concentration compared to that of the switch RNA when in equilibrium.<br>
 
Consequently, increased equilibrium concentrations of the switch-trigger duplex RNA would provide an increased number of active mRNAs for the translation of the reporter RFP.
 
Consequently, increased equilibrium concentrations of the switch-trigger duplex RNA would provide an increased number of active mRNAs for the translation of the reporter RFP.
 
</p>
 
</p>
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<p style="font-family: quicksand;font-size:150%;">Screening by our software</p>
 
<p style="font-family: quicksand;font-size:150%;">Screening by our software</p>
 
<p style="font-family: roboto;font-size:115%;">
 
<p style="font-family: roboto;font-size:115%;">
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To minimize the manpower on screening of the switches, we constructed an online toehold switch design program. Apart from basic thermodynamic parameters, it also screens for rare codon, stop codon and RFC illegal sites along the sequence. In addition, the built- in BLAST function also automatically screen for nonspecific region to avoid false positive detection. Ultimately, the program generated a list of possible Toehold Switch sequence according to their free energy using the embedded function of “Vienna RNA” (8). We ranked the &#8710; G RBS- Linker as the most important parameter since it had already proven that it correlates with the dynamic range of switch. Below graph shows 394 possible H5 toehold switches generated by our software. We first chose the switches that with the highest &#8710; G RBS- Linker (-3.8kcal/mol). Among those switches, we chose the 3 switches with low switch MFE and high MFE difference.
+
To minimize the manpower on screening of the switches, we constructed an online toehold switch design program. Apart from basic thermodynamic parameters, it also screens for other factors(link to the software page of the wiki). Ultimately, the program generated a list of possible toehold switch sequences according to many different free energy parameters using the ViennaRNA library[2]. The graph below shows 394 possible H5 toehold switches generated by our software. The assumptions motioned earlier stated 3 very important parameters for the selection of switch candidates with the greatest possible performances: Switch MFE, ΔG<sub>RBS-linker</sub>, and ΔMFE. We applied these parameters to our switch selection process: We first chose the switches that with the highest ΔG<sub>RBS-linker</sub> (-3.8 kcal/mol). Among those switches, we chose the 3 switches with the lowest switch MFE and the highest ΔMFE.
 
</p>
 
</p>
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<p>Figure 2: ΔG<sub>RBS-linker</sub> of switch candidates generated from an example RNA sequence input by our software</p>
  
 
<p><center><img src="https://static.igem.org/mediawiki/2017/d/df/CuhkSWITHcandidate.PNG"  style="width:700px;height:200px;" ></center></p>
 
<p><center><img src="https://static.igem.org/mediawiki/2017/d/df/CuhkSWITHcandidate.PNG"  style="width:700px;height:200px;" ></center></p>
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<th>Switch Candidate</th>
 
<th>Switch Candidate</th>
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<th>MFE RBS-Linker</th>
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<th>ΔG<sub>RBS-linker</sub></th>
 
<th>MFE Switch</th>
 
<th>MFE Switch</th>
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<th>MFE Difference</th>
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<th>ΔMFE</th>
 
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<th>Switch Candidate</th>
 
<th>Switch Candidate</th>
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<th>MFE RBS-Linker</th>
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<th>ΔG<sub>RBS-linker</sub></th>
 
<th>MFE Switch</th>
 
<th>MFE Switch</th>
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<th>MFE Difference</th>
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<th>ΔMFE</th>
 
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<br>
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<br>
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<div class="column full_size">
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<p style="font-family: quicksand;font-size:150%;">Suboptimal Structure Modelling</p>
 +
<p style="font-family: roboto;font-size:115%;">
 +
The toehold domain (first 15 nucleotides) of the switch RNA is crucial for the binding of the trigger RNA to the switch RNA: this domain must have minimal paired bases in the switch RNA to ensure the successful binding of this domain with the complementary sequence in the trigger RNA, which allows the unwinding of the switch RNA and permits translation of the reporter protein, RFP, to occur. </p>
 +
<p style="font-family: roboto;font-size:115%;">
 +
Our program can only calculate the minimal free energy structure (MFE) for each target RNA region to reduce calculation workload. In reality, different conformations of RNAs with the same sequence coexist in solution, whose and the concentrations of those populations are determined by their structures and free energy. Therefore, we manually checked the predicted structures and equilibrium concentrations of the ten suboptimal structures of each influenza switches with the lowest MFEs on the web tool developed by ViennaRNA package(http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/barriers.cgi)[2]. Then we predicted the performance of each influenza switches and compared with experimental results.</p>
 +
<p style="font-family: roboto;font-size:115%;">
 +
The table below shows the different suboptimal structures of each switch RNA sequence:</p>
 +
 +
 +
<u><b><p style="font-family: roboto;font-size:125%;">Assumption: ΔMFE correlates with the duplex expression</b></u></p>
 +
>> ΔMFE is defined as the difference between (switch RNA MFE + trigger RNA MFE) and MFE of the switch-trigger duplex RNA.<br>
 +
Since ΔMFE = –RTlnK, where:<br>
 +
R=gas constant<br>
 +
T=temperature<br>
 +
K=equilibrium constant<br>
 +
<center>Switch RNA+Trigger RNA↔Duplex RNA</center><br>
 +
Therefore, we assume that the higher the ΔMFE, the higher the switch-trigger duplex RNA concentration compared to that of the switch RNA when in equilibrium.<br>
 +
Consequently, increased equilibrium concentrations of the switch-trigger duplex RNA would provide an increased number of active mRNAs for the translation of the reporter RFP.
 +
</p>
 +
 
</div>
 
</div>
  

Revision as of 06:52, 1 November 2017


RNA thermodynamic modeling:
Designing Toehold Switch


Background

According to Green et al., the optimal length of RNA to be detected by a toehold switch is around 30 bp. In other words, a target RNA with 1000 bp in length can have 970 possible switches. However, the performances of each possible switch will be different, since switches that target different region will have different thermodynamic characteristic and structure, which can affect the performance of the switch. Therefore, we modeled the thermodynamic and structure of our toehold switch during designing stage and simulate the expression of activated switch in silico. Our modelling helped us a lot in gaining insight.



Toehold switch structure:

We adopt the toehold switch design from the original paper. Our toehold switch contains 15nts “toehold domain”, 21nts stem-loop that contains a start codon and a RBS B0034 loop at that toop. A 21nts linker and mRFP reporter sequence is present downstream the toehold switch. The linker is used to separate the coding sequence in the toehold switch and the reporter to prevent interference of protein folding.



Assumptions

Assumption: Switch MFE (Minimum Free Energy) correlates with the expression leakage

>> Switch MFE is the minimum Gibbs free energy that a toehold switch could have among all the possible structures.
>> Expression leakage is a phenomenon where the reporter (i.e. Red Fluorescent Protein in our project) is expressed in the absence of trigger RNA. The level of leakage can be measured as:
To activate toehold switch, an amount of energy is needed to open the toehold switch hairpin. The Switch MFE reflects the difficulty for the toehold switch unwinding process. We assume that the more negative the Switch MFE, the harder for the unwinding to take place, and hence a lower leakage.


Assumption: ΔGRBS-linker correlates with the duplex expression

 >> ΔGRBS-linker is the Gibbs free energy of the RNA sequence starting from the RBS to the linker in the switch-trigger duplex (Figure).
>> The duplex expression is the reporter expression of the switch-trigger dimer RNA.
After the switch RNA hairpin is unwound after binding to the trigger RNA, a switch-trigger dimer RNA would be formed. The RBS-linker region of the MFE structure of this dimer RNA should have minimal base pairs. This makes it easier to unwind the RNA for this region, allowing ribosomes to bind to the RBS and move along the RNA for translation of the RFP reporter gene to occur. ΔGRBS-linker reflects the difficulty for the unwinding process of the RBS-linker region. It is assumed that the more negative the ΔGRBS-linker, the harder it is for the unwinding to take place, leading to lower translation rates. Thus, the duplex expression would be reduced.

Assumption: ΔMFE correlates with the duplex expression

>> ΔMFE is defined as the difference between (switch RNA MFE + trigger RNA MFE) and MFE of the switch-trigger duplex RNA.
Since ΔMFE = –RTlnK, where:
R=gas constant
T=temperature
K=equilibrium constant
Switch RNA+Trigger RNA↔Duplex RNA

Therefore, we assume that the higher the ΔMFE, the higher the switch-trigger duplex RNA concentration compared to that of the switch RNA when in equilibrium.
Consequently, increased equilibrium concentrations of the switch-trigger duplex RNA would provide an increased number of active mRNAs for the translation of the reporter RFP.



Screening by our software

To minimize the manpower on screening of the switches, we constructed an online toehold switch design program. Apart from basic thermodynamic parameters, it also screens for other factors(link to the software page of the wiki). Ultimately, the program generated a list of possible toehold switch sequences according to many different free energy parameters using the ViennaRNA library[2]. The graph below shows 394 possible H5 toehold switches generated by our software. The assumptions motioned earlier stated 3 very important parameters for the selection of switch candidates with the greatest possible performances: Switch MFE, ΔGRBS-linker, and ΔMFE. We applied these parameters to our switch selection process: We first chose the switches that with the highest ΔGRBS-linker (-3.8 kcal/mol). Among those switches, we chose the 3 switches with the lowest switch MFE and the highest ΔMFE.

Figure 2: ΔGRBS-linker of switch candidates generated from an example RNA sequence input by our software




    Switch Candidate ΔGRBS-linker MFE Switch ΔMFE
    H5-1 -3.8 -19.1 +32.1
    H5-2 -3.8 -21.1 +34.8
    H5-3 -3.8 -24 +37.9
    H7-1 -3.8 -24.5 +41.4
    H7-2 -3.8 -17.8 +34.2
    H7-3 -3.8 -16.3 +26.3
    N1-1 -3.8 -17.1 +34.4
    N1-2 -3.8 -17.1 +24
    N1-3 -3.8 -19.4 +25.9
    N9-1 -3.8 -22.2 +34.5
    N9-2 -3.8 -17.9 +30.8
    N9-3 -3.8 -21.6 +27
    PB2-1 -3.8 -12.5 +34.6
    PB2-2 -3.8 -24.7 +38
    PB2-3 -3.8 -16 +28.2

To improve CGU’s oral cancer toehold switch, we employed the screening function of our program. A toehold switch was chosen that is predicted to outperform the CGU’s switch according to the MFE RBS-Linker.

    Switch Candidate ΔGRBS-linker MFE Switch ΔMFE
    CGU’s SAT switch -9.2 -17.4 +46.8
    New SAT switch -4.1 -24.5 +23.1


Suboptimal Structure Modelling

The toehold domain (first 15 nucleotides) of the switch RNA is crucial for the binding of the trigger RNA to the switch RNA: this domain must have minimal paired bases in the switch RNA to ensure the successful binding of this domain with the complementary sequence in the trigger RNA, which allows the unwinding of the switch RNA and permits translation of the reporter protein, RFP, to occur.

Our program can only calculate the minimal free energy structure (MFE) for each target RNA region to reduce calculation workload. In reality, different conformations of RNAs with the same sequence coexist in solution, whose and the concentrations of those populations are determined by their structures and free energy. Therefore, we manually checked the predicted structures and equilibrium concentrations of the ten suboptimal structures of each influenza switches with the lowest MFEs on the web tool developed by ViennaRNA package(http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/barriers.cgi)[2]. Then we predicted the performance of each influenza switches and compared with experimental results.

The table below shows the different suboptimal structures of each switch RNA sequence:

Assumption: ΔMFE correlates with the duplex expression

>> ΔMFE is defined as the difference between (switch RNA MFE + trigger RNA MFE) and MFE of the switch-trigger duplex RNA.
Since ΔMFE = –RTlnK, where:
R=gas constant
T=temperature
K=equilibrium constant
Switch RNA+Trigger RNA↔Duplex RNA

Therefore, we assume that the higher the ΔMFE, the higher the switch-trigger duplex RNA concentration compared to that of the switch RNA when in equilibrium.
Consequently, increased equilibrium concentrations of the switch-trigger duplex RNA would provide an increased number of active mRNAs for the translation of the reporter RFP.

     

Email: igemcuhk1617@gmail.com