We created a software tool that uses a DNA strand input, and defines a list of crRNA sites output. These crRNA will bind to the DNA with a strength previously specified by the user, as a percentage of the fold change of the dCas9. It also calculates the approximate structure of the crRNA and displays first the ones which disturb the dCas9 handle the least. To achieve this, it incorporates open source code from RNAfold [1] and code used in the research of Iman Farasat and Howard M. Salis [2] generously provided by them. The aim is to use this tool in order to design PAM proximal promoter sequences, as there is currently no analytical method available for prediction of binding strength between a dCas9 enzyme and PAM sequence.
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Revision as of 09:54, 18 October 2016
Abstract
1. Calculation of the binding energy
The Cas9Calculator, written by Iman Farasat and Howard M. Salis and published in their latest paper [2], is used for all calculations determining the strength of crRNA:DNA binding. For its creation they analysed datasets from different experiments and took into account the effects of supercoiling on adjacent DNA sites, different PAM sites, crRNA:DNA site mismatches, Cas9 and crRNA expression levels, organisms and growth conditions. This allowed them to predict approximately the dynamics of dCas9-based binding and Cas9-based cleavage at all DNA sites. For our calculations, site mismatches are characterised only by their number and position and not the specific genes.
2. Relation between fold change and binding energy
The fold change is the fold difference in gene expression between the case when the dCas9 is bound to the promoter and when it's not bound. Since binding of dCas9 in a CRISPRi system prevents transcription by blocking access to the promoter, the strength of binding of dCas9 determines the fold-repression.
To estimate the relation between these, data from two papers is used. For a given DNA sequence one paper presents the fold change in binding of dCas9 for different number of mutations [3]. The second paper presents the repression activity of a single mutation at different locations[4].
In both cases the RNA:DNA binding is calculated with the Cas9Calculator and then plotted on a logarithmic scale against the fold change (Fig. 1) or one over the repression activity, respectively (Fig. 2). With the exception of few points, correlations seems to be linear and hence the relation is fitted with the following formula:๐=๐^(โ๐ฝ(๐๐๐ฅ๐บโ๐บ))
Where maxG is the Gibbson free energy when there are no mismatches and G is the energy for the given state. P is the fold change between the two states. The coefficient %beta = 1.003074571 is the average of all individual coefficient satisfying the formula (with the exception of the few points noted above).
3. The Algorithm
Once the user inputs a DNA sequence and the desired fold change, the software calculates its required binding energy to the crRNA. Then starting from the perfectly matching crRNA, a random number of mutations is chosen and then for each mutation two further random numbers indicate its position and the replacement gene. This process is repeated 10 000 times where the binding energy of each sequence is calculated using the Cas9calculator. At the end of this step, only sequences with energy within the allowed range are selected.
Next, the dCas9 handle and terminator are added to the crRNA and the RNAfold is called to calculate the structure of the final sequence. This is to determine whether the crRNA will disturb the expression of dCas9 by binding to it. Since for high percentage of fold change, this depends mainly on the initial sequence, for many cases all crRNA sequences bind to the dCas9. Hence, we selected the sequences with at least half of their bases unpaired and show them in ascending order of pairings.
4. Next Steps
Our aim is to launch a website which will run the code described above in real time making it easier to use, thus reaching a wider range of users. So far an interface has been created and the code was successfully run on a local server (Fig. 3,4,5). The tool could be further improved by increasing its precision or decreasing the time it takes to return results. This could happen by fitting the data from section 2 with a different function or developing a more efficient algorithm for creating crRNA sequences. However, the idea behind this tool is simple but could still prove to be efective in the engineering of CRISPR/Cas9 systems.
References
[1] Lorenz, Ronny and Bernhart, Stephan H. and Hรถner zu Siederdissen, Christian and Tafer, Hakim and Flamm, Christoph and Stadler, Peter F. and Hofacker, Ivo L. ViennaRNA Package 2.0 Algorithms for Molecular Biology, 6:1 26, 2011, doi:10.1186/1748-7188-6-26
[2] Farasat I, Salis HM (2016) A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation. PLoS Comput Biol 12(1): e1004724. doi:10.1371/journal.Pcbi.1004724 (http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004724)
[3] http://nar.oxfordjournals.org/content/41/15/7429.abstract
[4] http://www.sciencedirect.com/science/article/pii/S0092867413002110
