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

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The table below shows the different suboptimal structures of each switch RNA sequence:</p>
 
The table below shows the different suboptimal structures of each switch RNA sequence:</p>
 
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<p style="font-family: roboto;font-size:115%;">
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Each suboptimal structure is labelled by numbers 1 to 10, where structure 1 has the lowest free energy(the MFE structure), and structure 10 has the highest free energy. In the "Suboptimal structures" column, only suboptimal structures with non-negligible equilibrium concentrations are considered and shown. (The equilibrium concentrations were calculated by the ViennaRNA webserver[2]: example for H5-3 switch{1}) We checked the status of the toehold domain for each suboptimal structure from the ViennaRNA webserver output[2] to see if it was open or closed.(example for H5-3 switch{2}) The structures with an open toehold domain are shown in green, and ones with a closed toehold domain are shown in red.</p>
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Each suboptimal structure is labelled by numbers 1 to 10, where structure 1 has the lowest free energy(the MFE structure), and structure 10 has the highest free energy. In the "Suboptimal structures" column, only suboptimal structures with non-negligible equilibrium concentrations are considered and shown. (The equilibrium concentrations were calculated by the ViennaRNA webserver[2]: example for H5-3 switch{1}) We checked the status of the toehold domain for each suboptimal structure from the ViennaRNA webserver output[2] to see if it was open or closed.(example for H5-3 switch<a href="#Bookmark_2">{2}</a>) The structures with an open toehold domain are shown in green, and ones with a closed toehold domain are shown in red.</p>
 
<p style="font-family: roboto;font-size:115%;">
 
<p style="font-family: roboto;font-size:115%;">
 
The expected fluorescence(determined by the ratio of suboptimal structures with open/closed toehold domains) exhibited by E. coli cotransformed with the switch and trigger RNA after 12 hours was compared with the actual observed fluorescence during the experiment.</p>
 
The expected fluorescence(determined by the ratio of suboptimal structures with open/closed toehold domains) exhibited by E. coli cotransformed with the switch and trigger RNA after 12 hours was compared with the actual observed fluorescence during the experiment.</p>
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<td>H5-3</td>
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<td>H5-3<h4 id="Bookmark_2">{2}</h4></td>
 
<td><font color="red">1, 2, 4,</font> <font color="green">3</font></td>
 
<td><font color="red">1, 2, 4,</font> <font color="green">3</font></td>
 
<td>low</td>
 
<td>low</td>

Revision as of 07:42, 1 November 2017


RNA thermodynamic modeling:
Designing Toehold Switch


Background

According to Green et al. (2014)[1], the optimal length of RNA to be detected by a toehold switch is around 30 bp (complementary region at below figure). In other words, a target RNA with 1000 bp in length can have 970 possible switches with different performance, which is governed by their structures and thermodynamic parameters. We aim at providing a workflow and platform of modeling to help users design the switches by reducing the manual processing and increasing the hit-rate of finding a good switch.

We adopt the toehold switch design from the original paper(below figure). Our toehold switch contains 15 nts “toehold domain”, 18nts stem and a loop that contains the RBS B0034. A 21 nts linker sequence plus an 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 MFE of the switch-trigger duplex RNA minus (switch RNA MFE + trigger RNA MFE).
Since ΔMFE = –RTlnK, where:
R=gas constant
T=temperature
K=equilibrium constant
Therefore, we assume that the more negative the MFE difference is, the higher the switch-trigger duplex RNA concentration compared to that of the switch RNA when in equilibrium.
Switch RNA+Trigger RNA↔Duplex RNA

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.

(THE toehold domain base pairing modelling would be covered by the switch RNA suboptimal structure modelling part)



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 Chang Gung University(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:

Each suboptimal structure is labelled by numbers 1 to 10, where structure 1 has the lowest free energy(the MFE structure), and structure 10 has the highest free energy. In the "Suboptimal structures" column, only suboptimal structures with non-negligible equilibrium concentrations are considered and shown. (The equilibrium concentrations were calculated by the ViennaRNA webserver[2]: example for H5-3 switch{1}) We checked the status of the toehold domain for each suboptimal structure from the ViennaRNA webserver output[2] to see if it was open or closed.(example for H5-3 switch{2}) The structures with an open toehold domain are shown in green, and ones with a closed toehold domain are shown in red.

The expected fluorescence(determined by the ratio of suboptimal structures with open/closed toehold domains) exhibited by E. coli cotransformed with the switch and trigger RNA after 12 hours was compared with the actual observed fluorescence during the experiment.

    Switch Candidate Suboptimal structures Expected fluorescence Experimental fluorescence(relative) Conclusion
    H5-1 1, 6, 7, 2 low 11.676366 (low) true negative
    H5-2 1, 2, 3, 4, 5 low 38.24207 (low) true negative
    H5-3

    {2}

    		 1, 2, 4, 3 low 13.34770467 (low) true negative
    H7-1 1, 2, 3, 4 low 10.82038167 (low) true negative
    H7-2 1, 3, 2, 7 low 8.577606333 (low) true negative
    H7-3 1, 2 high 637.066 (high) true positive
    N1-1 1, 2 high 204.7757333 (high) true positive
    N1-2 1, 2 low 9.325934 (low) true negative
    N1-3 1, 2 high 9.148810667 (low) false positive
    N9-1 1, 2, 3 high 166.49639 (high) true positive
    N9-2 1, 2, 6 high 604.2225333 (high) true positive
    N9-3 1 high 10.587811 (low) false positive
    PB2-1 1, 2 high 8.756591333 (low) false positive
    PB2-2 1, 3 low 24.68932333 (low) true negative
    PB2-3 1, 2, 3, 5 high 368.0475667 (high) true positive

From the table above, we observed that only 3 out of 15 of our predictions were incorrect, showing promising accuracy of this prediction method. None of the 3 incorrect predictions were false negatives, indicating that the suboptimal structure prediction method should be mostly applied to filtering out switch sequences with low predicted performances.

     

Email: igemcuhk1617@gmail.com