Quick start¶

This is a walkthrough of finding a toehold switch’s specificity. If you don’t need full customizability, the autoconfig() function can simplify the simulation parameters.

First import ToeholdTools:

import thtools as tt

Now define the toehold switch and ribosome binding site used. The example used is team City of London UK (2021)’s switch for hsa-miR-210-3p:

ths = "UUAGCCGCUGUCACACGCACAGGGAUUUACAAAAAGAGGAGAGUAAAAUGCUGUGCGUGCACCAUAAAACGAACAUAGAC"
rbs = "AGAGGAGA"

Then load the RNA sequences you want test against your toehold switch. We’ll use the a subsection of the Homo sapiens mature miRNA entries in miRBase, which ships pre-packaged with ToeholdTools and sorted by species:

my_fasta = tt.FParser.fromspecies("Homo sapiens")[295:305] # slice a small chunk of the database
my_rna_triggers = my_fasta.seqs
my_rna_names = my_fasta.ids # optional argument

Now setup the ToeholdTest simulation and run it:

my_test = tt.autoconfig(ths, rbs, my_rna_triggers, names=my_rna_names)
my_result = my_test.run(
   max_size=3, # the largest size of RNA complex to consider
   n_samples=100, # the number of Boltzmann samples to take of each complex's secondary structure
)
print(my_result.tabulate(
dp=5 # number of decimal places to show (optional argument)
))

And here is the output:

+-----------------+-------------------------+--------------+-----------------+-----------------+----------------------+----------------+
| Name            | Sequence                | Activation % | RBS unbinding % | AUG unbinding % | Post-AUG unbinding % | Standard Error |
+-----------------+-------------------------+--------------+-----------------+-----------------+----------------------+----------------+
| hsa-miR-210-3p  | CUGUGCGUGUGACAGCGGCUGA  | 74.99997     | 90.00000        | 80.99996        | 80.99996             | 4.33013        |
| hsa-miR-214-5p  | UGCCUGUCUACACUUGCUGUGC  | 0.00001      | 89.71653        | 4.27968         | 0.00005              | 0.00239        |
| hsa-miR-208b-3p | AUAAGACGAACAAAAGGUUUGU  | 0.00000      | 89.71643        | 1.15424         | 0.00004              | 0.00170        |
| hsa-miR-210-5p  | AGCCCCUGCCCACCGCACACUG  | 0.00000      | 89.71684        | 2.53726         | 0.00006              | 0.00170        |
| hsa-miR-212-5p  | ACCUUGGCUCUAGACUGCUUACU | 0.00000      | 89.72340        | 1.74311         | 0.00006              | 0.00170        |
| hsa-miR-211-5p  | UUCCCUUUGUCAUCCUUCGCCU  | 0.00000      | 89.71612        | 2.53687         | 0.00276              | 0.00170        |
| hsa-miR-208b-5p | AAGCUUUUUGCUCGAAUUAUGU  | 0.00000      | 89.71161        | 1.15708         | 0.00004              | 0.00169        |
| hsa-miR-212-3p  | UAACAGUCUCCAGUCACGGCC   | 0.00000      | 90.48685        | 1.90022         | 0.00008              | 0.00160        |
| hsa-miR-214-3p  | ACAGCAGGCACAGACAGGCAGU  | 0.00000      | 89.27519        | 2.70569         | 0.00001              | 0.00082        |
| hsa-miR-211-3p  | GCAGGGACAGCAAAGGGGUGC   | 0.00000      | 97.53812        | 4.02094         | 0.00001              | 0.00056        |
+-----------------+-------------------------+--------------+-----------------+-----------------+----------------------+----------------+

Since hsa-miR-210-3p has by far the highest activation of all the RNAs tested (as expected), this switch is highly specific to the desired target! There are other result display option available (notably prettify()) so be sure to check out the full API reference in the sidebar on the left.

Note

Your output might have slightly different values since Boltzmann sampling is pseudo-random.

ToeholdTools 0.3.0

Quick start¶