Week One

Digestion and Isolation of pSB1C3 Backbone.

The purpose of digesting the pSB1C3/PArsRGFP construct was to separate the backbone from the former GFP insert. Tacoma RAINmakers sought to isolate the pSB1C3 backbone employed in their 2017 construct, as the GFP reporter complex was no longer desired. Apparent disadvantages of GFP indication in biosensors are ultraviolet readings. The RAINmakers prefered a chromoprotein that produces color in the visible spectrum. Enzymes XbaI and SpeI were used to cleave the terminator sites of the vector, freeing the pSB1C3 backbone. NEB resources confirmed that the Cutsmart Buffer 2.1 is the most compatible with these particular enzymes, providing an optimized environment for digestion. Combining the reagents listed in Table 1.0, the reaction was set at 37ºC in a water bath for 1 hour and 45 minutes. This reaction was completed in duplicate to increase statistical probability of desired backbone DNA.

Step 2

Use RNADuplex^[2] to calculate binding energies for all 4096 pairs of SD/ASD sequences.

We then use RNAduplex from the ViennaRNA package to calculate binding energies for all 4096 pairs of SD/ ASD sequences. Details

Step 3

Narrow library by eliminating mutant pairs with binding energy more than 0.5 kcal/mol away from wild-type value.

Again, we use RNAduplex to calculate binding energies, but this time between the candidate ASD sequences with the wild-type SD sequences. Details

Figure S2. Narrowing down based on wild-type ASD/wild-type SD binding energy.

Step 4

Use RNADuplex to calculate binding energies between all remaining mutant ASDs with wildtype SD.

The library is narrowed down again by discarding those candidates with a binding energy less than -1 kcal/mol with the wild type SD sequences. This prevents the orthogonal ribosomes developed from the candidate ASDs from binding with wild type SD sequences over orthogonal mRNA, which ensures orthogonality of the engineered ribosomes. Details

Step 5

Narrow library by eliminating mutant ASD/Wildtype SD pairs with binding energy <-1.0 kcal/mol.

The library is narrowed down again by discarding those candidates with a binding energy less than -1 kcal/mol with the wild type SD sequences. This prevents the orthogonal ribosomes developed from the candidate ASDs from binding with wild type SD sequences over orthogonal mRNA, which ensures orthogonality of the engineered ribosomes. Details

Figure S3. Ensure orthogonality of chosen sequences.

Step 6

Use RNAFold to estimate secondary structure formation of 16s rRNAs containing mutant ASD sequences.

We use RNAfold from the ViennaRNA package to calculate the secondary structure for the full 16s rRNA. Details

Step 7

Narrow library by eliminating sequences that lead to 16s rRNA having secondary structure formed at the ASD region.

Those candidates with secondary structure in the ASD regions are discarded, as this would impair their ability to carry out translation. Details

Figure S4. Elimination of sequences that lead to secondary structure complications.

Step 8

Obtain all translation initiation regions (TIRs) from the chosen bacteria's genome.

Next, we obtain all the translation initiation regions (TIRs) from the genome. Details

Figure S5. Overview: Obtaining all translation initiation regions from the Coding Region.

Figure S6. Extracting the TIRs in different situations.

Step 9

Use RNADuplex^[3] to calculate binding energy of each remaining mutant ASD with those TIRs.

We use these TIRs in conjunction with RNAduplex once again to calculate the binding energies of the remaining candidate ASDs with those TIRs. Details

Step 10

Rank candidate mutant ASDs based on number of strong interactions between the ASD sequence and the TIRs from the bacteria genome.

Candidates are then ranked based on their binding energies with the host TIRs. Candidates with higher binding energies (which are less likely to bind with host TIRs) are given preference, since they are more likely to remain orthogonal to the host processes. Details

Reference

[1] Darlington, A.P.S., Kim, J., Jiménez, J.I., & Bates, D.G. (2018). Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes. Nature Communications, 9, 695.

[2] Ding, Y., Chan, C. Y., & Lawrence, C. E. (2005). RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. RNA, 11(8), 1157–1166. http://doi.org/10.1261/rna.2500605

[3]“TBI - RNAduplex - Manpage.” Accessed September 12, 2018. https://www.tbi.univie.ac.at/RNA/RNAduplex.1.html.