Viraless

Production of B5R protein by optimal IPTG concentration

Overview


Figure 1. The expression system of our proposed mechanism

To synthesize B5R protein, we chose to use BL21(DE3) E. coli strain since it lacks proteases that can degrade our recombinant protein. The BL21(DE3) has a specific T7 RNA Polymerase (T7 RNAP) gene controlled by an IPTG-regulated lacUV5 promoter (Figure 1). This T7 RNAP is derived from λ bacteriophage, and lacUV5 promoter is a leakier promoter than wt lac promoter. The lacUV5 promoter is inhibited by LacI inhibitor when IPTG is absent. It forms dimers in cytoplasm, and LacI dimer can be inhibited by IPTG [1]. Since we use BL21(DE3) strain, we decided to use pET23a plasmid for expression of B5R since it contains a T7 promoter that specifically allows T7 RNAP binding [2]. 


Why do we need a mathematical model?

The high expression of T7 polymerase resulted in high metabolic burden on cells, higher chances of mutations in target gene, and loss of expression plasmid [3]. Therefore, it is important to regulate IPTG concentration and still have optimal recombinant protein expression. Moreover, the modeling of optimal IPTG concentration can be useful in mass production of our protein. You can learn more about our future plans in the Implementation part. 


Expression of T7 polymerase

In our system, T7 polymerase is synthesized from LacUV5 promoter via the following reactions:

And the ODEs that model the change of concentration of and during transcription and translation processes are:

(1)

(2)

Assuming that the translation process is much longer than the transcription, for most of translation, transcription has reached steady state conditions:

(3)

Thus,

(4)

(5)

And the change in concentration of the protein can be described as:

(6)

After the translation process has reached steady-state, the concentration of T7 polymerase can be calculated and the concentration of T7 polymerase depending on the lacUV5 operon can be modeled as:

(7)

Where the constant is:

(8)

The model shows the speed of production and the amount of T7 polymerase produced with the pET23a plasmid. It can be noted that the assumption that the transcription process is at steady state is confirmed (Figure 2 and 3).


Figure 2. Concentration change during T7 expression in 5-hour period


Figure 3. Concentration change during T7 expression in 40-hour period

Parameters used for the modeling:

Parameters

Value

Units

k_mr

0.1019

1/second

k_t7

0.0083

1/second

d_mr

0.0077

1/second

d_mr

0.0033

1/second


Expression of B5R

Similar logic is used to model the expression of the B5R protein using the T7 polymerase. The reactions of transcription and translation are:

And the ODEs representing the rate of transcription and translation are:

(9)

(10)

where is the copy number of the plasmid pET-23 that we used. Similarly, assuming that transcription reaches the steady-state conditions for most of the translation process, the equation for the rate of translation is written as:

(11)

And when translation has reached the steady-state conditions, concentration of B5R can be modeled from  the initial concentration of T7 polymerase:

(12)

(13)


Modeling the influence of IPTG on the T7 polymerase expression

Using the same reasoning, the amount of LacI protein, that suppresses the lacUV5 promoter of T7 polymerase production, can be determined as:

(14)

The reactions are:

And adding IPTG into the system, its production theoretically becomes:

(15)

Therefore, reducing the production of LacI, meaning that it represses the repressor of the T7 polymerase production, thus adding IPTG results in a more yielding of the T7 polymerase. 

Modeling the concentration change of LacI, it is noticed that with increasing the concentration of IPTG, the suppressor LacI is depleted faster. This data is useful to pick the concentrations of IPTG to add to obtain the desired concentration of T7 and B5R proteins.


Figure 4. The concentration of free LacI (µM) vs time (second)


Reference list

  1. Jeong, H., Kim, H. J., & Lee, S. J. (2015). Complete Genome Sequence of Escherichia coli Strain BL21. Genome announcements, 3(2), e00134-15. https://doi.org/10.1128/genomeA.00134-15

  2. Du, F., Liu, Y. Q., Xu, Y. S., Li, Z. J., Wang, Y. Z., Zhang, Z. X., & Sun, X. M. (2021, September 26). Regulating the T7 RNA polymerase expression in E. coli BL21 (DE3) to provide more host options for recombinant protein production. Microbial Cell Factories, 20(1). https://doi.org/10.1186/s12934-021-01680-6

  3. Du, F., Liu, Y. Q., Xu, Y. S., Li, Z. J., Wang, Y. Z., Zhang, Z. X., & Sun, X. M. (2021). Regulating the T7 RNA polymerase expression in E. coli BL21 (DE3) to provide more host options for recombinant protein production. Microbial cell factories, 20(1), 189. https://doi.org/10.1186/s12934-021-01680-6





Aptamer modeling

In the scope of the project, we have designed DNA aptamer sequences to vaccinia virus proteins using a program called MAWS (see Design page). After we had the aptamer sequences, in order to observe how the nucleotide sequences are folding and test how well they bind to their target proteins using in-silico model simulations, we modeled their 3D structures.

DNA aptamers are usually no ordinary DNA molecules, but single-stranded ones, and researching the internet, we did not find tools to predict the 3D structure of single-stranded DNAs (ssDNA). Therefore we followed a process described below, inspired by Oliveira et al. and speeded up using the program dnaTurner we made [1].

To model the 3D structure of a single-stranded DNA sequence, as one shown below, the first step is to make it a RNA sequence by changing Thymine nucleotides to Uracil-s:

ATCGTGAGGAAGCGGCGGGA AUCGUGAGGAAGCGGCGGGA

Then we predict the secondary and tertiary (3D) structures of the resulting RNA sequence. A way to do that is to use freely available online tools; we used Mfold to predict the secondary structure, and RNAComposer to predict the 3D structure [2-3].


Then we used dnaTurner and QRNAS to turn the resulting 3D structure of the RNA molecule into the 3D structure of a single-stranded DNA molecule with the original sequence (for information about dnaTurner and how to use it, see Software page).

3D structure of DNA aptamer of L1 vaccinia virus protein

Following the algorithm for DNA aptamers of other proteins, we obtained their 3D models:

3D structure of DNA aptamer of B5R vaccinia virus protein

3D structure of DNA aptamer of A33 vaccinia virus protein


3D structure of DNA aptamer of A27 vaccinia virus protein

When we have the 3D structures of the aptamers and their target proteins ready, we can take advantage of online docking tools to model their interactions in-silico. Below you can see the docking models of interactions of L1, A27, A33 and B5R, obtained via Hdock [5].

L1 and its aptamer


B5R and its aptamer


A27 and its aptamer


A33 and its aptamer


Summary

To sum up, we modeled 3D structures of single-stranded DNA aptamers and their interaction with the target proteins. For making 3D models of ssDNA-s we used an updated method that can be used by future iGEM teams.


References

  1. Oliveira, R., Pinho, E., Sousa, A. L., Dias, Ó., Azevedo, N. F., & Almeida, C. (2022). Modelling aptamers with nucleic acid mimics (NAM): From sequence to three-dimensional docking. Plos one, 17(3), e0264701, https://doi.org/10.1371/journal.pone.0264701.

  2. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003;31: 3406–3415. pmid:12824337

  3. Antczak, M., Popenda, M., Zok, T., Sarzynska, J., Ratajczak, T., Tomczyk, K., Adamiak, R.W., Szachniuk, M. New functionality of RNAComposer: an application to shape the axis of miR160 precursor structure, Acta Biochimica Polonica, 2016, 63(4):737-744 (doi:10.18388/abp.2016_1329).         

  4. Popenda, M., Szachniuk, M., Antczak, M., Purzycka, K.J., Lukasiak, P., Bartol, N., Blazewicz, J., Adamiak, R.W. Automated 3D structure composition for large RNAs, Nucleic Acids Research, 2012, 40(14):e112 (doi:10.1093/nar/gks339).

  5. Yan Y, Zhang D, Zhou P, Li B, Huang S-Y. HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res. 2017;45(W1):W365-W373.