Team:Uppsala University/Modelling

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Team Uppsala University – iGEM 2012

Modelling of small RNA interacting with the AAC(6)’5-UTR

The modelling of our constructed sRNA is a vital part for evaluating hypotheses of how sRNA molecules interact with their target. One canonical approach for sRNA stability is to evaluate secondary structures by minimum free energy (MFE) approaches. This gives a pointer for approximating the probability of different kind of interactions.

Stabile structures prevents translation A thermodynamic scheme of RNA interactions at different stages were made to show how an interaction between mRNA and sRNA could possibly work. So called toeholds are belived to play an important role in the interaction between sRNA and mRNA. [1] Understanding the interaction between rna with different structures is a key to learn how to design a sRNA. Often the hybridization reaction between the sRNA and the mRNA starts with the unpaired sequences, the so called toeholds. To start a basepairing reaction, the sRNA needs a few basepairs to start hybridize to. The sRNA and the mRNA can then create a more stable secondary complex, hindering translation.

Reaction diagram sRNA

The sRNA 17 and mRNA for the AAC(6)-UTR were first calculated in CLC main workbench separately, after that we calculated the interaction structure between them .
This is an idea of how a secondary complex can be created between the sRNA and mRNA.

Using a native scaffold to stabilize the interactions
The wildtype small RNA of E.coli K-12 MG1655 that we used as a template for constructing our own smallRNA is named Spot42 and has been shown to be interacting with the Hfq protein.

Spot42 has like many other smallRNA, two distinctive parts. One that binds to a mRNA sequence, and another sequence that interacts with the Hfq-rna binding protein that is belived to play an important role in the function of smallRNA. (Holmquist, 2012)

Figure above. Panel A shows the secondary structure of scaffold
Spot42 and panel B shows a MMB generated three dimensional of
the Spot42 scaffold. HP1 and HP2 shows the positions of the two hairpins.

The secondary structure of the scaffold of Spot42 (figA) is known. However, no 3D structure of spot42 has been solved to date. We have generated a 3D structure from the information in the secondary structure with molecular dynamics software MacroMoleculeBuilder (MMB) [1] and the 3D structure of Spot42 scaffold is presented in figure B. MMB allows the user to have full control of the biomolecule (DNA, RNA and/or protein) by applying forces between atoms, constraints and flexibility to different parts of the biomolecule. [3]

To gain better understanding of the mechanisms of small RNA translation inhibition we have made 2D models of all our found smallRNAs. The 2D models were calculated in CLC main workbench which uses adapted versions of maximum free energy algorithms developed by [2] and thermodynamical parameters of Mfold version 3. IntaRNA, an RNA-interaction prediction software adapted for sRNA and ncRNA interactions [5] was used to predict the sRNA-mRNA interactions of the candidate sRNAs.

The sRNA were isolated, sequenced and analyzed to find the hybridizing base pairs. The sequencing of the sRNAs that showed downregulation of YFP also had a matching sequence in the 5’UTR of our target mRNA.
Altough, a few of the sRNA that downregulated YFP was shown to hybridize at the YFP mRNA region. Two of these were further studied and modelled. At last, a prediction of the structure between the sRNA UU17 and AAC(6’)UTR mRNA were modelled.

Candidate sRNAs

As you can see below, our candidate sRNA that downregulate both the fluorescence and the antibiotic resistance gene also shows to hybridize at the region close to the RBS and the start codon. This supports the idea that many sRNA prevents the ribosome from binding to the RBS, and thereby preventing translation. [4]
Also our predictions data from CLC shows that the hybridizing area have a strong secondary structure, with small hairpin loops.

sRNA UU17


∆G = -31.2 kcal/mol	Length in base pairs = 82 bp Number of hybridizing base pairs = 17 Maximum number of hybridizing bases in a row = 13 Result on Etest > 256 µg/ml ∆G = -14.3kcal/mol SYFP2 downregulation = 78%

sRNA UU37


∆G = -31.9 kcal/mol	Length in base pairs = 83 bp Number of hybridizing base pairs = 24 Maximum number of hybridizing bases in a row = 7 Result on Etest = 64 µg/ml ∆G = -15.7 kcal/mol SYFP2 downregulation: 82%

sRNA UU46


∆G = -28.7 kcal/mol	Length in base pairs = 65 bp Number of hybridizing base pairs = 8 bp Maximum number of hybridizing bases in a row = 8 bp Result on Etest > 256µg/ml SYFP2 downregulation: 83% ∆G = -11.2kcal/mol

sRNA UU55


∆G = -31.2 kcal/mol	Length in base pairs = 83 Number of hybridizing base pairs = 8 Maximum number of hybridizing bases in a row = 8 Result on Etest = 124 µg/ml SYFP2 downregulation= 58% ∆G = -10.2kcal/mol

sRNAs matching SYFP2

Below are two examples of structures that were found to match regions in the coding sequence of the SYFP2. It is apparent that false positives will appear when using a reporter system that itself can be repressed, but by modelling interactions with the target mRNA, we save much time knowing which sRNAs are not of direct interest.

sRNA UU52


∆G = -28.3kcal/mol	Length in base pairs =82 Number of hybridizing base pairs =10 Maximum number of hybridizing bases in a row = 6 ∆G = -5.6 kcal/mol

sRNA UU53


∆G = -34.5 kcal/mol	Length in base pairs =85 Number of hybridizing base pairs =21 Maximum number of hybridizing bases in a row =9 ∆G = -10.9kcal/mol

sRNA UU01


∆G = -31.2 kcal/mol	Length in base pairs = 83 Number of hybridizing base pairs = 8 Maximum number of hybridizing bases in a row = 8 Result on Etest = 124 µg/ml ∆G = -10.2kcal/mol

AAC(6')-SYFP2 + sRNA UU17 interaction


∆G = -23,4 kcal/mol	∆G = -60,8 kcal/mol kcal/mol

[1] Rodrigo, G., Landrain, T.E., Jaramillo, A., 2012. De novo automated design of small
RNA circuits for engineering synthetic riboregulation in living cells.
Proc. Natl. Acad. Sci. U.S.A. 109, 15271–15276.
[2] Zuker, M., 1989b. The use of dynamic programming algorithms in
RNA secondary structure prediction, 159–184. In Waterman, M., ed. Mathematical Methods for DNA Sequences, CRC Press, Boca Rato
[3]. Samuel C Flores and Russ B Altman. 2010 Turning limited
experimental information intio 3D models of RNA. RNA 16(9):1769-78.
Notes about CLC workbench: Uses thermodynamical parameters of Mfold
version 3 found on http://www.bioinfo.rpi.edu/zukerm/rna/energy/
[4] Erik Holmquist Macromolecular Matchmaking Mechanisms and Biology of
Bacterial Small RNAs
2012

@@ Line 39: / Line 39: @@
 <p>
 <b>Stabile structures prevents translation</b>
-A thermodynamic scheme of RNA interactions at different stages were made to show how an interaction between mRNA and sRNA could possibly work. So called toeholds are belived to play an important role in the interaction between sRNA and mRNA. (G. Rodrigo, T.E.Landrain,A.Jaramillo, 2012)
+A thermodynamic scheme of RNA interactions at different stages were made to show how an interaction between mRNA and sRNA could possibly work. So called toeholds are belived to play an important role in the interaction between sRNA and mRNA. [1]
 Understanding the interaction between rna with different structures is a key to learn how to design a sRNA. Often the hybridization reaction between the sRNA and the mRNA starts with the unpaired sequences, the so called toeholds. To start a basepairing reaction, the sRNA needs a few basepairs to start hybridize to. The sRNA and the mRNA can then create a more stable secondary complex, hindering translation. </p>
 <br>
@@ Line 80: / Line 80: @@
 <br>
 <br>
-To gain better understanding of the mechanisms of smallRNA translation inhibition we have made 2D models of all our found smallRNAs. The 2D models were calculated in CLC main workbench.<br>
+To gain better understanding of the mechanisms of small RNA translation inhibition we have made 2D models of all our found smallRNAs. The 2D models were calculated in CLC main workbench which uses adapted versions of maximum free energy algorithms developed by [2] and thermodynamical parameters of Mfold version 3.
-IntaRNA, an RNA-interaction prediction software adapted for sRNA and ncRNA interactions (Smith et. al., 2010) was used  to predict the sRNA-mRNA interactions of the candidate sRNAs.
+IntaRNA, an RNA-interaction prediction software adapted for sRNA and ncRNA interactions [5] was used  to predict the sRNA-mRNA interactions of the candidate sRNAs.
 </p>
 <br>
@@ Line 106: / Line 106: @@
 <table id="ptable">
 <p>
-As you can see below, our candidate sRNA that downregulate both the fluorescence and the antibiotic resistance gene also shows to hybridize at the region close to the RBS and the start codon. This supports the idea that many sRNA prevents the ribosome from binding to the RBS, and thereby preventing translation. (Erik Holmquist, 2012)<br>
+As you can see below, our candidate sRNA that downregulate both the fluorescence and the antibiotic resistance gene also shows to hybridize at the region close to the RBS and the start codon. This supports the idea that many sRNA prevents the ribosome from binding to the RBS, and thereby preventing translation. [4]<br>
 Also our predictions data from CLC shows that the hybridizing area have a strong secondary structure, with small hairpin loops.
 </p>
@@ Line 360: / Line 360: @@
 [3]. Samuel C Flores and Russ B Altman. 2010 Turning limited <br>
 experimental information intio 3D models of RNA. RNA 16(9):1769-78.
-<br>Notes about CLC workbench: Uses thermodynamical parameters of Mfold<b>
+<br>Notes about CLC workbench: Uses thermodynamical parameters of Mfold<br>
-  version 3 found on http://www.bioinfo.rpi.edu/zukerm/rna/energy/</p>
+  version 3 found on http://www.bioinfo.rpi.edu/zukerm/rna/energy/
+<br>[4] Erik Holmquist
+Macromolecular Matchmaking
+Mechanisms and Biology of <br>
+Bacterial Small RNAs<br>
+<br>
+</p>
 </html>
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