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(Yoo et al., 2013) propose a simple design, considering as the only criterion the union of the sRNA within the TIR region, this region is where the ribosome joins, with extension from the SD sequence up to the next 30 nt. It is worth mentioning that this proposal contemplates the binding energy of sRNA with mRNA, to achieve the purpose of altering the efficiency of translation, it also considers the size of 20 to 30 nts in length, since the greater the length, the greater the possibility of off-target repression, the binding energy of -30 to -40 kcal/mol is also considered.
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<h4>sRNA design considering Hfq recruting and mRNA TIR targetting</h4>
<p>(Zhu et al., 2021) designed a synthetic sRNA system based on the MicC scaffold and the chaperone Hfq to control gene expression in Methylorubrum extorquens. The criteria that they used for designing the asRNA were length, location and binding free energy. Their paper also cites (Na et al. 2013), which says that an asRNA 24 nucleotides long in the translation initiation region (TIR) of the target mRNA shows high suppression activity (>90%). Their sRNA was designed accordingly. The online service DINAMelt was used to calculate the binding free energy between the asRNA and its target mRNA.</p>
<p>(Zhu et al., 2021) designed a synthetic sRNA system based on the MicC scaffold and the chaperone Hfq to control gene expression in Methylorubrum extorquens. The criteria that they used for designing the asRNA were length, location and binding free energy. Their paper <strong>also</strong> cites (Na et al. 2013), which says that an asRNA 24 nucleotides long in the translation initiation region (TIR) of the target mRNA shows high suppression activity (>90%). Their sRNA was designed accordingly. The online service DINAMelt was used to calculate the binding free energy between the asRNA and its target mRNA.</p>
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For our model’s design, the sequences were first preprocessed since many of them were not candidates and had to be dropped. The positions of hybridization were found and used to calculate training parameters. The following features, as well as their squared value, were calculated with the help of NuPack and ViennaRNA:
<pstyle="text-align:center;"><h6style="text-align: center;"><strong>Table 1.</strong> Considered features</h6></p>
<p>Thus, our final training dataset consisted of 557 observations with 40 features. The data preprocessing was in big part done with the aid of scikit learn functionalities (such as StandardScaler, LabelEncoder and train_test_split), which was then fed to a TensorFlow neural network for training.</p>