CascAID is a potentially universal tool for nucleic acid detection. Fast adaptation of our platform to new targets requires in silico verification of the crRNA design. Crucial factors for the development of these crRNA designs are the binding of the crRNA to Cas13a, which is mainly determined by its secondary structure, and the uniqueness of the targeting sequence in the transcriptome (to rule out false positive results). To ensure the integrity of the Cas13a-crRNA complex, we developed a python script that uses the established program packages for secondary structures, NUPACK and Mfold. In order to verify the specificity of the targeting sequence, we used the BLASTN-short program to check for similar structures in a transcriptome databank. Additionally, we created a database of crRNA designs that have already worked and made it as extensive as possible given the limited time, checking for collaboration with other teams working with Cas13a, mainly TU Delft. The second branch of software we developed is needed for hardware control in our project. They allow user's devices such as computers and smartphones to control our hardware, Heatbringer and Lightbringer. The repository to our software can be found here.
There are two main problems regarding the design of crRNA for a diagnostic test. First, the secondary structure of the crRNA needed for Cas13a activity needs to be verified. Secondly, the sequence targeted by the crRNA has to be specific, i.e. there must be no identical sequence in the reference transcriptome of an healthy patient. Otherwise, off-target effects will lead to false positive results since Cas13a is activated even though the pathogen is not present. To address these issues, we developed a software relying on bioinformatic principles such as secondary structure prediction and Basic Local Alignment Search Tool (BLAST).
For secondary structure prediction of the crRNA we utilised the two established program packages in the field, NUPACK and Mfold to compare newly designed crRNA with secondary structures of crRNAs that were already known to be active. These reference crRNA structures were either obtained from actual crystallography data of crRNA in complex with Cas13a, or from structure prediction data of experimentally tested crRNAs. Using secondary structure verification we were able to rule out misfolding crRNA designs prior to experiment. We developed a script for the end user automating this procedure.
From this, we can extract the secondary structure in Vienna notation as well as the free energies of the RNA structure to predict the probability of formation in solution with help of the calculation of the full partition function. Using these results, the user can make qualitative assumptions about the activity of the corresponding Cas13a-crRNA complex.
GOOD NEWS! YOU'VE GOT THE RIGHT SECONDARY STRUCTURE!
This is also a good example to show that it might happen that one program recognizes the crRNA secondary structure while the other does not. In this case, NUPACK has predicted the structure while Mfold is not able to predict the structure. Even though this is an experimental construct that worked, we did not put the secondary structure prediction of this into the database for Mfold, since it was unable to predict the right structure.
All data was retreived from the Transcriptome Release #90 of the ENSEMBL project. The output is generated from the output of a blastn-short run and consists in the example below of all sequences that show sequence identity of 18 bp or higher. For an actual run, the identity would need to be 26 bp or higher in order to actually show off-target effects since Cas13a is selective up to 2 point mutations regarding the binding of crRNA and subsequent RNase activity. The expectation value here describes the number of hits one can expect to find in a random database the same size as the database used for the blastn-short run.
The database program gives you an interface to interact with the MySQL database created for crRNAs that have been shown to work experimentally .
However, these still need to be tested for off-target effects experimentally since in silico screening can only confirm specificity to a certain amount of certainty.
The software for reading out the fluorescence detector is described in the Hardware section. All software developed for hardware can be found in our GitHub repository.
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