Part:BBa_K3396000
DocS
The Coch2 module binds DocS (BBa_K3396000) modules constitutively.
Usage and Biology
The DocS[1] module comes from The C. thermocellum scaffoldin and it could recognize and bind tightly to complementary Coh2 modules. The Coh2–DocS pair represents the interaction between two complementary families of protein modules that exhibit divergent specificities and affinities, ranging from one of the highest known affinity constants between two proteins to relatively low-affinity interactions.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12COMPATIBLE WITH RFC[12]
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000COMPATIBLE WITH RFC[1000]
Contribution by CU_Egypt team 2022
DocS (Dockerin) is a cellulolytic enzyme in Clostridium thermocellum. Each cellulosomal enzyme has one or more catalytic modules as well as a single dockerin module. The scaffolding is multifunctional, with the nine Cohesin modules integrating nine dockerin-bearing enzymes into the complex and the carbohydrate-binding enzymes. The complex is bound to the cellulosic substrate by the Cellulose Binding Domain (CBM), and the C-terminal dockerin module is implicated in this process. The interaction between Cohesin and Dockerin happens in two different forms, called the dual binding mode, in a calcium-dependent manner due to the presence of a calcium-binding site in the dockerin protein.
The expression of Dockerin and Cohesin modules individually has been proven to yield low expression and very unstable proteins that are degraded upon expression, which hinders any attempts of producing any type of the modules alone.
Previous experiments proposed the expression of the Doc module in E. Coli fused with different large protein domains, which seemed to produce highly stable Dockerin that is soluble and acquired increased functionality regarding binding to the Cohesin module. Thus, we decided to test the expression GST tag and compare it to that expressed with His, to see if the fusion with GST would fit the theory and increase the yield and stability of DocS.
We started by optimizing the sequence to fit the expression in E-coli. and added different both wet and dry lab characterization for Docs on the registry. The wet lab characterization was done by different methods such as Agarose gel electrophoresis, SDS PAGE, transformation efficiency, affinity chromatography, and Bradford assay.
The Dry Lab results proved the theory as the binding affinity of GST-DocS to Cohesin was higher than that of His-DocS, moreover, wet lab results supported those results as the expression yield of Docs increased upon tagging by GST and successfully bound our expressed Cohesin and initiated the awaited signaling in our systems, So in conclusion, individual expression of the module could be carried out by this method and the Doc/Coh modules can be used by future iGEM teams for signaling purposes in any pathway that aims to target different diseases.
Dry Lab Characterization
Our dry lab work was to validate and predict the wet-lab results. This validation also helps in reducing experimental trials and only focuses on promising ones. According to Benchmarking, we chose four tools for modeling and the best models were chosen according to quality assessment parameters found in JSON files of the Swiss Model quality assessment ranking.
1.1. Modeling
Assembly of Docs was done with both GST and His, then the models were designed by several tools to acquire the best model.
GST-Docs
Figure 1.: Predicted 3D structure of GST-Docs designed by RosettaFold tool displayed by Pymol.
| cbeta_deviations | clashscore | molprobity | ramachandran_favored | ramachandran_outliers | Qmean_4 | Qmean_6 |
|---|---|---|---|---|---|---|
| 0 | 182.87 | 2.72 | 98.25 | 0.7 | 0.75816 | 1.025826 |
Table 1.: QA scores by SWISS model tool of GST-DocS structure.
His-Docs
Figure 2.: Predicted 3D structure of His-Docs designed by TRrosetta tool displayed by Pymol.
| cbeta_deviations | clashscore | molprobity | ramachandran_favored | ramachandran_outliers | Qmean_4 | Qmean_6 |
|---|---|---|---|---|---|---|
| 1 | 2.53 | 1.69 | 85.33 | 1.33 | -1.68577 | -1.79068 |
Table 2.: QA scores by SWISS model tool of His-Docs structure.
1.2. Docking
The docking results showed that the fusion of GST to DocS increased its binding affinity score to Coh2 more than without the GST. Illustrating that GST increases the contact between both proteins. In addition, Galaxy and ClusPro docking results showed that the affinity when GST is fused to DocS is better than when fused to Coh2, this may be due to decreasing the overall non-interacting surface of the protein and consequently increasing the affinity. So, we decided to order the four parts (His-DocS, GST-DocS, His-Coh2, GST-DocS) for further characterization.
All of the docking results were ranked using our code for calculating the binding affinity.
GST-Docs VS His-Coh by Cluspro
Figure 3.: Docked structure of GST-Docs VS His-Coh2 designed by Cluspro displayed by Pymol.
GST-Docs VS His-Coh2 by Galaxy
Figure 4.: Docked structure of GST-Docs VS His-Coh2 designed by Galaxy visualized by Pymol.
His-Docs VS GST-Coh by ClusPro
Figure 5.: Docked structure of His-Docs VS GST-Coh2 designed by ClusPro visualized by Pymol.
His-Docs VS GST-Coh2 by Galaxy
Figure 6.: Docked structure of His-Docs VS GST-Coh2 designed by Galaxy visualized by Pymol.
Binding energies of Docs VS Coh2
| Binnding affinities of DocS Vs Coh2 docking (kcal/mol) | ||
|---|---|---|
| Model | Galaxy | Cluspro |
| GST_Coh2 vs His_DocS | -13.153 | -11.635 |
| GST_DocS vs His_Coh2 | -13.488 | -14.026 |
Table 3.: Binding energies of Docs VS Coh2 tagged with GST and His designed by Galaxy and ClusPro.
1. Functional Parameters
| PI | 5.30 |
|---|---|
| Molecular Weight | 16751.19 Da |
WetLab Results
As we investigated from the literature we found that the Docs part yields little amount due to its suitability to proteolytic attack, hence we tried to configure its expression by adding two different tags and followed this pipeline to prove our concept experimentally: started with cloning in the pJET vector followed by the expression in the pgs21a, then we performed two different kinds of lysis to extract the protein to find which lysis buffer will give better yield, and quantified the protein expression before and after induction using BCA assay, in the end, we tested the his-docs affinity by the pulldown assay against the GST-Coh2 and GST DOC against His COH
Results of His-DocS
Transformation of His Doc in DH-5 alpha using pJET cloning vector
The transformation was done using TSS buffer protocol, after trying three buffers which are Calcium chloride, Magnesium chloride and a combination between Calcium chloride and Magnesium chloride, we optimized our protocol to use the TSS buffer protocol as it showed the best results with a transformation efficiency of His DOC in DH-5 alpha using pJET vector is 8.61×〖10〗^4/μg while in BL-21a using pGS-21a vector is 5×〖10〗^3/μg you can find the complete protocol in our wiki page
Figure 7. Transformed plate of His Doc + pJET using TSS buffer protocol
Transformation of His Doc in BL-21 using pGS-21a expression vector
Figure 8. Transformed plate of His Doc + pGS-21a
Comparison between chemical lysis and sonication for His DOC
Figure 9. This graph shows the difference between chemical lysis and sonication for His DOC,
after we had the results, we optimized our protocol to use chemical lysis for His DOC
SDS PAGE for induced and non induced samples of His DOC
SDS PAGE depends on the molecular weight of the protein, we performed SDS to make sure that our protein is in the exact size and to show the difference between induced and non induced samples of His DOC.
Figure 10. This figure shows the comparison between induced and non-induced samples of His DOC where well no.2 is
the non-induced sample while well no.4 is the induced sample showing that our protein is induced effectively
owing to our right choice of IPTG, time interval, and concentration
Pull down assay of His COH with GST DOC and His DOC with GST COH
Pull down assay was performed to check and compare between the interaction between His DOC with GST COH and that of His COH with GST DOC
Figure 11. This graph illustrates that the binding between His DOC with GST COH is more stable than that
of His COH with GST DOC
Results of GST-DocS
Transformation of GST DOC in DH-5 alpha using pJET cloning vector
The transformation was done using TSS buffer protocol, after trying three buffers which are Calcium chloride, Magnesium chloride and a combination between Calcium chloride and Magnesium chloride, we optimized our protocol to use the TSS buffer protocol as it showed the best results with a transformation efficiency of GST DOC in DH-5 alpha using pJET vector is 155000 transformants/μg while in BL-21a using pGS-21a vector is 170000 No.of transformants/μg you can find the complete protocol in our wiki page
Figure 11. Transformed plate of GST DOC + pJET
Transformation of GST DOC in BL-21 using pGS-21a expression vector
Figure 12. Transformed plate of GST Doc + pGS-21a
Comparison between chemical lysis and sonication for GST DOC
Figure 13. This graph shows a significant difference between chemical lysis and sonication for GST DOC after we had the
results, we optimized our protocol to use chemical lysis for GST DOC
<img src="" style="margin-left:200px;" alt="" width="500" />
we compared the results of the sonication and chemical lysis to identify which was better in extracting the Docs fused the two different tags: His and GST, the graph proved our theory that states that the extraction of GST-Docs gives a higher yield than the His-Docs extraction. Hence, the GST addition succeded in stabilizing the expression of the protein.
Figure 14: A comparison between extracting GST-Docs and His-Docs by sonication, showing a significant increase when the
dockerin was fused with the GST tag
Reference
[1] BARAK Y, HANDELSMAN T, NAKAR D, et al. Matching fusion protein systems for affinity analysis of two interacting families of proteins: the cohesin-dockerin interaction [J]. J Mol Recognit, 2005, 18(6): 491-501.
[2] Kazutaka Sakka, Yuka Sugihara, Sadanari Jindou, Makiko Sakka, Minoru Inagaki, Kazuo Sakka, Tetsuya Kimura, Analysis of cohesin–dockerin interactions using mutant dockerin proteins, FEMS Microbiology Letters, Volume 314, Issue 1, January 2011, Pages 75–80, https://doi.org/10.1111/j.1574-6968.2010.02146.x
[3] Lawrie, J., Song, X., Niu, W., & Guo, J. (2018). A high throughput approach for the generation of orthogonally interacting protein pairs. Scientific Reports, 8. https://doi.org/10.1038/s41598-018-19281-6
[4] Wojciechowski, M., Różycki, B., Huy, P. D. Q., Li, M. S., Bayer, E. A., & Cieplak, M. (2018). Dual binding in cohesin-dockerin complexes: the energy landscape and the role of short, terminal segments of the dockerin module. Scientific reports, 8(1), 1-14.
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