Team:Uppsala University/Modelling

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.

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)

Here you can see the native spot42 with the scaffold marked in blue.

Picture of native spot42
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.
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.

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.

As you can see below, our sRNA that downregulates the YFP 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)
Also our predictions data from CLC shows that the hybridizing area have a strong secondary structure, with small hairpin loops.

[nBp] = Total number of base pairs in sRNA
[nBpHyb] = Number of bases hybridizing (calculated from freiburg) intaR
[dG] = delta G; Predicted free energy for secondary structure and mRNA-sRNA interaction.
[nInRowBp] = Maximum number of hybridizing bases in a row

sRNA UU17

dG = -31.2 kcal/mol	Length in base pairs = 82 Number of hybridizing base pairs = 17 Maximum number of hybridizing bases in a row = 13 Result on Etest > 256 µg/ml Gibbs free energy = -14.3kcal/mol

dG = -31.9 kcal/mol	Length in base pairs = 83 Number of hybridizing base pairs = 24 Maximum number of hybridizing bases in a row = 7 Result on Etest = 64 µg/ml Gibbs free energy = -15.7 kcal/mol

dG = -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 Etest >256 µg/ml Gibbs free energy = -11.2 kcal/mol

dG = -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 Gibbs free energy = -10.2kcal/mol