Difference between revisions of "Part:BBa K4814007"
 
(4 intermediate revisions by the same user not shown)
Line 1: Line 1:
 
__NOTOC__
 
__NOTOC__
 
<partinfo>BBa_K4814007 short</partinfo>
 
<partinfo>BBa_K4814007 short</partinfo>
 +
 +
* NOTE: This part is used with <html><a href="https://parts.igem.org/Part:BBa_K4814006">BBa_K4814006 (ATRIP-EGFP)</a></html> as a FRET pair.
  
 
FRET is using fluorescent proteins as probes to detect the interaction of targeted proteins. The distance-dependent process transfers energy from an excited molecular fluorophore (the donor) to another fluorophore (the acceptor) through intermolecular long-range dipole–dipole coupling once the desired proteins bind (Sekar, R. B. and Periasamy, A., 2003). The critical Förster radius (typically 3-6 nm) at angstrom distances (10–100 Å) can be calculated to increase the accuracy and ensure precise energy transfer. (Alan Mulllan, n.d.) By using FRET, we can therefore observe the interaction of two proteins by measuring the lifetime of the fluorescent proteins attached to them.
 
FRET is using fluorescent proteins as probes to detect the interaction of targeted proteins. The distance-dependent process transfers energy from an excited molecular fluorophore (the donor) to another fluorophore (the acceptor) through intermolecular long-range dipole–dipole coupling once the desired proteins bind (Sekar, R. B. and Periasamy, A., 2003). The critical Förster radius (typically 3-6 nm) at angstrom distances (10–100 Å) can be calculated to increase the accuracy and ensure precise energy transfer. (Alan Mulllan, n.d.) By using FRET, we can therefore observe the interaction of two proteins by measuring the lifetime of the fluorescent proteins attached to them.
Line 7: Line 9:
  
 
The mCherry is derived from https://www.snapgene.com/plasmids/fluorescent_protein_genes_and_plasmids/mCherry (same as BBa_K4814011), a mammalian codon optimized mCherry.
 
The mCherry is derived from https://www.snapgene.com/plasmids/fluorescent_protein_genes_and_plasmids/mCherry (same as BBa_K4814011), a mammalian codon optimized mCherry.
 +
 +
<html>
 +
<h2>3D Protein Docking Modeling</h2>
 +
</html>
 +
 +
As the efficiency of FRET is largely dependent on the degree of the molecular separation and overall spatial arrangement of the two fluorophores involved, further ratification is needed to ensure the reliability of the sequences we designed for our FRET system and thereby validate the credibility of the data acquired from FRET imaging. To accomplish this, we have undertaken the development of a protein model.
 +
 +
Our protein model involves a series of scientific methodologies, including sequence predictions, RMSD calculations, protein-protein docking, and distance measurements. We aim to predict and elucidate the interaction between ATRIP-eGFP and RPA1-mCherry, shedding light on the properties exhibited by the two fluorescent proteins as they interact, to investigate the system’s efficiency and further validate our FRET-based approach.
 +
 +
By docking the fluorophores (eGFP, mCherry, eCFP, YFP) with the respective DNA  damage response (DDR) proteins (ATRIP, RPA1), we plan to gain insight into their arrangements, their respective feasibilities, and their binding configurations, and then dock the bound structures together to further our understanding.
 +
 +
===ClusPro===
 +
 +
We used ClusPro 2.0[4][5][6][7] next, utilizing its CPU, to perform molecular docking simulations. The docking calculations were carried out with hydrophobic-favored coefficients to enhance the accuracy of the results.
 +
<html><center><p>E = 0.40E<sub>rep</sub> +− 0.40E<sub>att</sub> + 600E<sub>elec</sub> +2.00E<sub>DARS</sub></p></center></html>
 +
A total of 30 models were generated through the docking simulations. Subsequently, we focused our analysis on the top 10 models with the highest scores. Remarkably, all of these models exhibited a range of FRET effectiveness within the distance range of 10-100Å, indicating that our designed bio-reporter system is feasible in terms of configuration and proximity.
 +
 +
(Blue: ATRIP-EGFP; Green: RPA1-mCherry)
 +
 +
<html><img src=https://static.igem.wiki/teams/4814/wiki/cp6.png style="width: 750px;"></html>
 +
 +
Figure 1. ClusPro hydrophobic-favored model no.6, with an angstrom distance of 56.5Å
 +
 +
<html><img src=https://static.igem.wiki/teams/4814/wiki/cp7.png style="width: 750px;"></html>
 +
 +
Figure 2. ClusPro hydrophobic-favored model no.7, with an angstrom distance of 97.4Å
 +
 +
===HDOCK===
 +
 +
In addition to ClusPro, we opted for HDOCK to dock our predicted structures in hopes that the results from these simulations would be able to co-validate each other. In the case of any disparities in the outcomes of these two algorithms, it would be meaningful to compare the different scores, parameters and configurations that they may provide.
 +
Each HDOCK model comes with two scores, a docking score and a confidence score:  The docking scores are calculated by a knowledge-based iterative scoring function. More negative docking scores indicate more likely binding models. However, since the score has not been calibrated to experimental data, it should not be interpreted as the actual binding affinity of two molecules. The confidence score is determined based on the docking score and is designed to indicate the likelihood of binding between the protein-protein/RNA/DNA complexes. Generally, when the confidence score is above 0.7, the two molecules would be very likely to bind in this pose.[8] The calculation of the confidence score is defined as follows:
 +
 +
(Blue: ATRIP-EGFP; Green: RPA1-mCherry)
 +
 +
<html><p>Confidence_score = 1.0/[1.0+e<sup>0.02*(Docking_Score+150)</sup>]</p></html>
 +
Below are the docking scores and confidence scores for the top ten models, which are ranked by their docking scores:</p>
 +
<html><center><figure>
 +
  <table class="table table-bordered" style="width:800px">
 +
    <tr>
 +
      <th>Rank</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th>
 +
    </tr>
 +
    <tr>
 +
      <th>Docking Score</th>
 +
      <td>-252.39</td>
 +
      <td>-239.71</td>
 +
      <td>-231.76</td>
 +
      <td>-228.53</td>
 +
      <td>-221.35</td>
 +
      <td>-217.78</td>
 +
      <td>-217.14</td>
 +
      <td>-215.96</td>
 +
      <td>-214.32</td>
 +
      <td>-213.43</td>
 +
    </tr>
 +
    <tr>
 +
      <th>Confidence Score</th>
 +
      <td>0.8857</td>
 +
      <td>0.8574</td>
 +
      <td>0.8369</td>
 +
      <td>0.8279</td>
 +
      <td>0.8064</td>
 +
      <td>0.7950</td>
 +
      <td>0.7930</td>
 +
      <td>0.7890</td>
 +
      <td>0.7835</td>
 +
      <td>0.7805</td>
 +
    </tr>
 +
  </table>
 +
</figure></center></html>
 +
 +
<html><img src=https://static.igem.wiki/teams/4814/wiki/h10.png style="width: 750px;"></html>
 +
 +
Figure 3. The pose with the 1st highest score generated by HDOCK, with an angstrom distance od 132.5Å.
 +
 +
<html><img src=https://static.igem.wiki/teams/4814/wiki/h1.png style="width: 750px;"></html>
 +
 +
Figure 4. The pose with the 10th highest score generated by HDOCK, with an angstrom distance of 12.7Å.
 +
 +
===Using PyMOL to measure the distance===
 +
 +
Based on a comprehensive literature review, FRET can be an accurate measurement of molecular proximity within the range of angstrom distances (10–100 Å). Using PyMOL, we analyzed the results of both ClusPro and HDOCK by calculating the angstrom distance between the two fluorophores attached to ATRIP and RPA in the poses generated by these algorithms. Remarkably, all of the top 10 ClusPro models exhibited a range of FRET effectiveness within the distance range of 56.5-97.4Å. The results in HDOCK displayed a wider spectrum, ranging from 12.7Å to 132.5Å. It is important to emphasize that the scores given by ClusPro and HDOCK are not directly correlated with the distances determined by PyMOL; poses with higher scores do not necessarily indicate a larger or smaller degree of separation. However, these highly-ranked poses are more likely to form, so analysing their distance is relatively meaningful as it encompasses a substantial portion of the poses generated.
  
 
<h2>FRET results</h2>
 
<h2>FRET results</h2>
Line 13: Line 96:
 
<html><img src=https://static.igem.wiki/teams/4814/wiki/lab/human/g-m-uv-treatement-20231004.png style="width: 600px;"></html>
 
<html><img src=https://static.igem.wiki/teams/4814/wiki/lab/human/g-m-uv-treatement-20231004.png style="width: 600px;"></html>
  
−
 
+
Figure 5. The image of ATRIP-EGFP (excited at 488 nm) + RPA1-mCherry (RPA1-mCherry transfected twice) (excited at 561 nm).
−
Figure 3. The image of ATRIP-EGFP (excited at 488 nm) + RPA1-mCherry (RPA1-mCherry transfected twice) (excited at 561 nm).
+
  
 
To enhance the accuracy and reliability of our data analysis, we utilized ImageJ software to precisely outline the cell nuclei present in the Green Channel (excited at 488 nm). This step ensured that we specifically selected cells that were transfected with EGFP, as depicted in Figure . We focused on GFP-emitting cells because we observed that the image captured in the 488 nm excited green channel did not completely overlap with the image in the 488 nm excited red channel.  
 
To enhance the accuracy and reliability of our data analysis, we utilized ImageJ software to precisely outline the cell nuclei present in the Green Channel (excited at 488 nm). This step ensured that we specifically selected cells that were transfected with EGFP, as depicted in Figure . We focused on GFP-emitting cells because we observed that the image captured in the 488 nm excited green channel did not completely overlap with the image in the 488 nm excited red channel.  
  
−
However, it is important to note that the 488 nm red channel fluorescence should correspond to GFP emission at approximately 560 nm. Therefore, the shape of the cells in the red channel should be identical to that in the green channel (as shown in Figure .). By choosing GFP-emitting cells, we aimed to reduce background noise and focus our analysis specifically on the G+M cells (cells expressing both ATRIP-EGFP and RPA1-mCherry), excluding cells expressing only RPA1-mCherry.
+
However, it is important to note that the 488 nm red channel fluorescence should correspond to GFP emission at approximately 560 nm. Therefore, the shape of the cells in the red channel should be identical to that in the green channel. By choosing GFP-emitting cells, we aimed to reduce background noise and focus our analysis specifically on the G+M cells (cells expressing both ATRIP-EGFP and RPA1-mCherry), excluding cells expressing only RPA1-mCherry.
  
 
<html>
 
<html>
Line 27: Line 109:
 
To handle the non-linear nature of the data, we took the logarithm of the values with a base of 2, which brings the values onto a comparable scale.
 
To handle the non-linear nature of the data, we took the logarithm of the values with a base of 2, which brings the values onto a comparable scale.
  
−
In Figure 4, the data points in the UV- graph are divided into two groups, with a separation occurring at 0.1. We set this value as the cutoff point, indicating the presence of FRET when the data point exceeds 0.1.
+
In Figure 6, the data points in the UV- graph are divided into two groups, with a separation occurring at 0.1. We set this value as the cutoff point, indicating the presence of FRET when the data point exceeds 0.1.
  
 
In the UV+ graph, there is a noticeable distinction in the proportion of data points indicating FRET.
 
In the UV+ graph, there is a noticeable distinction in the proportion of data points indicating FRET.
Line 33: Line 115:
 
<html>
 
<html>
 
       <img src="https://static.igem.wiki/teams/4814/wiki/lab/human/log2-red-green-ratio-distribution.png" style="width: 600px;">
 
       <img src="https://static.igem.wiki/teams/4814/wiki/lab/human/log2-red-green-ratio-distribution.png" style="width: 600px;">
−
       <p>Figure 4. Distribution of the Log base 2 Red/Green data before and after UV.</p>
+
       <p>Figure 6. Distribution of the Log base 2 Red/Green data before and after UV.</p>
 
</html>
 
</html>
  
Line 46: Line 128:
 
</html>
 
</html>
  
−
Figure 5. The Mean Log base 2 of Red over Green ratio with standard error before and after UV. Technical sample number = 4 with about 30 data points in each sample.
+
Figure 7. The Mean Log base 2 of Red over Green ratio with standard error before and after UV. Technical sample number = 4 with about 30 data points in each sample.
 +
 
 +
<h3>References</h3>
  
 
Sekar, R. B., & Periasamy, A. (2003). Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations. The Journal of cell biology, 160(5), 629–633. https://doi.org/10.1083/jcb.200210140
 
Sekar, R. B., & Periasamy, A. (2003). Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations. The Journal of cell biology, 160(5), 629–633. https://doi.org/10.1083/jcb.200210140
Line 53: Line 137:
  
 
*caagtttgtacaaaaaagcaggctgccacc contains Kozak (gccacc)
 
*caagtttgtacaaaaaagcaggctgccacc contains Kozak (gccacc)
 +
 +
<html>
 +
<h3>Modeling References:</h3>
 +
<hr>
 +
<p>[1] Chen, R., Li, L., & Weng, Z. (2003). ZDOCK: an initial-stage protein-docking algorithm. Proteins, 52(1), 80–87. <a href="https://doi.org/10.1002/prot.10389">https://doi.org/10.1002/prot.10389</a></p>
 +
<p>[2] Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: Making protein folding accessible to all. Nature Methods, 19(6), 679-682. <a href="https://doi.org/10.1038/s41592-022-01488-1">https://doi.org/10.1038/s41592-022-01488-1</a></p>
 +
<p>[3] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A., Ballard, A. J., Cowie, A., Nikolov, S., Jain, R., Adler, J., Back, T., . . . Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589. <a href="https://doi.org/10.1038/s41586-021-03819-2">https://doi.org/10.1038/s41586-021-03819-2</a></p>
 +
<p>[4] Desta IT, Porter KA, Xia B, Kozakov D, Vajda S. Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure. 2020 Sep; 28 (9):1071-1081. <a href="https://doi.org/10.1016/j.str.2020.06.006">https://doi.org/10.1016/j.str.2020.06.006</a></p>
 +
<p>[5] Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, Kozakov D. New additions to the ClusPro server motivated by CAPRI. Proteins: Structure, Function, and Bioinformatics. 2017 Mar; 85(3):435-444. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5313348/pdf/nihms834822.pdf">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5313348/pdf/nihms834822.pdf</a></p>
 +
<p>[6] Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S. The ClusPro web server for protein-protein docking. Nature Protocols. 2017 Feb;12(2):255-278. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540229/pdf/nihms883869.pdf">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540229/pdf/nihms883869.pdf</a> </p>
 +
<p>[7] Kozakov D, Beglov D, Bohnuud T, Mottarella S, Xia B, Hall DR, Vajda, S. How good is automated protein docking? Proteins: Structure, Function, and Bioinformatics. 2013 Dec; 81(12):2159-66. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934018/pdf/nihms556382.pdf">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934018/pdf/nihms556382.pdf</a> </p>
 +
<p>[8]Yan, Y., Tao, H., He, J., & Huang, S. (2020). The HDOCK server for integrated protein–protein docking. Nature Protocols, 15(5), 1829-1852. <a href="https://doi.org/10.1038/s41596-020-0312-x">https://doi.org/10.1038/s41596-020-0312-x</a></p>
 +
<p>[9] Sekar, R. B., & Periasamy, A. (2003). Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations. The Journal of cell biology, 160(5), 629–633. <a href="https://doi.org/10.1083/jcb.200210140">https://doi.org/10.1083/jcb.200210140</a></p>
 +
</html>
  
 
<!-- -->
 
<!-- -->

Latest revision as of 13:45, 12 October 2023

RPA1-mCherry

FRET is using fluorescent proteins as probes to detect the interaction of targeted proteins. The distance-dependent process transfers energy from an excited molecular fluorophore (the donor) to another fluorophore (the acceptor) through intermolecular long-range dipole–dipole coupling once the desired proteins bind (Sekar, R. B. and Periasamy, A., 2003). The critical Förster radius (typically 3-6 nm) at angstrom distances (10–100 Å) can be calculated to increase the accuracy and ensure precise energy transfer. (Alan Mulllan, n.d.) By using FRET, we can therefore observe the interaction of two proteins by measuring the lifetime of the fluorescent proteins attached to them.

As the aim of this design is to detect DNA damages in mammalian cells, we have used CMV promoter and the Lentivirus vector. Please refer to BBa_K4814004 and BBa_K4814005 (ATRIP and RPA1) for detailed explanation of the two proteins involved in the DNA damage checkpoint process.

The mCherry is derived from https://www.snapgene.com/plasmids/fluorescent_protein_genes_and_plasmids/mCherry (same as BBa_K4814011), a mammalian codon optimized mCherry.

3D Protein Docking Modeling

As the efficiency of FRET is largely dependent on the degree of the molecular separation and overall spatial arrangement of the two fluorophores involved, further ratification is needed to ensure the reliability of the sequences we designed for our FRET system and thereby validate the credibility of the data acquired from FRET imaging. To accomplish this, we have undertaken the development of a protein model.

Our protein model involves a series of scientific methodologies, including sequence predictions, RMSD calculations, protein-protein docking, and distance measurements. We aim to predict and elucidate the interaction between ATRIP-eGFP and RPA1-mCherry, shedding light on the properties exhibited by the two fluorescent proteins as they interact, to investigate the system’s efficiency and further validate our FRET-based approach.

By docking the fluorophores (eGFP, mCherry, eCFP, YFP) with the respective DNA damage response (DDR) proteins (ATRIP, RPA1), we plan to gain insight into their arrangements, their respective feasibilities, and their binding configurations, and then dock the bound structures together to further our understanding.

ClusPro

We used ClusPro 2.0[4][5][6][7] next, utilizing its CPU, to perform molecular docking simulations. The docking calculations were carried out with hydrophobic-favored coefficients to enhance the accuracy of the results.

E = 0.40Erep +− 0.40Eatt + 600Eelec +2.00EDARS

A total of 30 models were generated through the docking simulations. Subsequently, we focused our analysis on the top 10 models with the highest scores. Remarkably, all of these models exhibited a range of FRET effectiveness within the distance range of 10-100Å, indicating that our designed bio-reporter system is feasible in terms of configuration and proximity.

(Blue: ATRIP-EGFP; Green: RPA1-mCherry)

Figure 1. ClusPro hydrophobic-favored model no.6, with an angstrom distance of 56.5Å

Figure 2. ClusPro hydrophobic-favored model no.7, with an angstrom distance of 97.4Å

HDOCK

In addition to ClusPro, we opted for HDOCK to dock our predicted structures in hopes that the results from these simulations would be able to co-validate each other. In the case of any disparities in the outcomes of these two algorithms, it would be meaningful to compare the different scores, parameters and configurations that they may provide. Each HDOCK model comes with two scores, a docking score and a confidence score: The docking scores are calculated by a knowledge-based iterative scoring function. More negative docking scores indicate more likely binding models. However, since the score has not been calibrated to experimental data, it should not be interpreted as the actual binding affinity of two molecules. The confidence score is determined based on the docking score and is designed to indicate the likelihood of binding between the protein-protein/RNA/DNA complexes. Generally, when the confidence score is above 0.7, the two molecules would be very likely to bind in this pose.[8] The calculation of the confidence score is defined as follows:

(Blue: ATRIP-EGFP; Green: RPA1-mCherry)

Confidence_score = 1.0/[1.0+e0.02*(Docking_Score+150)]

Below are the docking scores and confidence scores for the top ten models, which are ranked by their docking scores:</p>
Rank12345678910
Docking Score -252.39 -239.71 -231.76 -228.53 -221.35 -217.78 -217.14 -215.96 -214.32 -213.43
Confidence Score 0.8857 0.8574 0.8369 0.8279 0.8064 0.7950 0.7930 0.7890 0.7835 0.7805

Figure 3. The pose with the 1st highest score generated by HDOCK, with an angstrom distance od 132.5Å.

Figure 4. The pose with the 10th highest score generated by HDOCK, with an angstrom distance of 12.7Å.

Using PyMOL to measure the distance

Based on a comprehensive literature review, FRET can be an accurate measurement of molecular proximity within the range of angstrom distances (10–100 Å). Using PyMOL, we analyzed the results of both ClusPro and HDOCK by calculating the angstrom distance between the two fluorophores attached to ATRIP and RPA in the poses generated by these algorithms. Remarkably, all of the top 10 ClusPro models exhibited a range of FRET effectiveness within the distance range of 56.5-97.4Å. The results in HDOCK displayed a wider spectrum, ranging from 12.7Å to 132.5Å. It is important to emphasize that the scores given by ClusPro and HDOCK are not directly correlated with the distances determined by PyMOL; poses with higher scores do not necessarily indicate a larger or smaller degree of separation. However, these highly-ranked poses are more likely to form, so analysing their distance is relatively meaningful as it encompasses a substantial portion of the poses generated.

FRET results

After subjecting the cells to UVB treatment at a dosage of 100 J/m^2, we observed a change in the density of both EGFP and mCherry signals. When excited at 488 nm, we noticed that the EGFP signal became weaker following exposure to UVB. However, in contrast, the red fluorescence emitted by mCherry (with an emission range of 570-620 nm) intensified.

Figure 5. The image of ATRIP-EGFP (excited at 488 nm) + RPA1-mCherry (RPA1-mCherry transfected twice) (excited at 561 nm).

To enhance the accuracy and reliability of our data analysis, we utilized ImageJ software to precisely outline the cell nuclei present in the Green Channel (excited at 488 nm). This step ensured that we specifically selected cells that were transfected with EGFP, as depicted in Figure . We focused on GFP-emitting cells because we observed that the image captured in the 488 nm excited green channel did not completely overlap with the image in the 488 nm excited red channel.

However, it is important to note that the 488 nm red channel fluorescence should correspond to GFP emission at approximately 560 nm. Therefore, the shape of the cells in the red channel should be identical to that in the green channel. By choosing GFP-emitting cells, we aimed to reduce background noise and focus our analysis specifically on the G+M cells (cells expressing both ATRIP-EGFP and RPA1-mCherry), excluding cells expressing only RPA1-mCherry.

Statistical Analysis

We calculated the ratio of FRET using the Red over Green (Channel 3 over Channel 2) ratio. When the ratio is bigger than 1, there is more red fluorescence in the cell. We can compare the ratio before and after UV treatment to determine whether FRET occurs.

To handle the non-linear nature of the data, we took the logarithm of the values with a base of 2, which brings the values onto a comparable scale.

In Figure 6, the data points in the UV- graph are divided into two groups, with a separation occurring at 0.1. We set this value as the cutoff point, indicating the presence of FRET when the data point exceeds 0.1.

In the UV+ graph, there is a noticeable distinction in the proportion of data points indicating FRET.

Figure 6. Distribution of the Log base 2 Red/Green data before and after UV.

The Red over Green ratio (Log_2) showed an increase of more than threefold after UVB light treatment. This substantial increase indicates that there is energy transfer from GFP to mCherry, resulting in the emission of red fluorescence when exposed to UV light. This confirms the occurrence of FRET energy transfer.

To assess the significance of the relationship between the two categorical variables, we employed Fisher's exact test. This statistical test is suitable when dealing with small cell counts. When the two-sided p-value is less than 0.01, it suggests a significant association between the two groups. (MedCalc Software Ltd. Fisher, 2023)

The result of Fisher's exact test revealed a strong significance between the two groups (p-value = 0.00122178, p-value < 0.01), indicating a stastical significance.

Figure 7. The Mean Log base 2 of Red over Green ratio with standard error before and after UV. Technical sample number = 4 with about 30 data points in each sample.

References

Sekar, R. B., & Periasamy, A. (2003). Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations. The Journal of cell biology, 160(5), 629–633. https://doi.org/10.1083/jcb.200210140

Alan Mulllan. (n.d.). Advanced microscopy applications – an overview of FRET. OXFORD instruments. https://andor.oxinst.com/learning/view/article/fret

  • caagtttgtacaaaaaagcaggctgccacc contains Kozak (gccacc)

Modeling References:

[1] Chen, R., Li, L., & Weng, Z. (2003). ZDOCK: an initial-stage protein-docking algorithm. Proteins, 52(1), 80–87. https://doi.org/10.1002/prot.10389

[2] Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: Making protein folding accessible to all. Nature Methods, 19(6), 679-682. https://doi.org/10.1038/s41592-022-01488-1

[3] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A., Ballard, A. J., Cowie, A., Nikolov, S., Jain, R., Adler, J., Back, T., . . . Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589. https://doi.org/10.1038/s41586-021-03819-2

[4] Desta IT, Porter KA, Xia B, Kozakov D, Vajda S. Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure. 2020 Sep; 28 (9):1071-1081. https://doi.org/10.1016/j.str.2020.06.006

[5] Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, Kozakov D. New additions to the ClusPro server motivated by CAPRI. Proteins: Structure, Function, and Bioinformatics. 2017 Mar; 85(3):435-444. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5313348/pdf/nihms834822.pdf

[6] Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S. The ClusPro web server for protein-protein docking. Nature Protocols. 2017 Feb;12(2):255-278. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540229/pdf/nihms883869.pdf

[7] Kozakov D, Beglov D, Bohnuud T, Mottarella S, Xia B, Hall DR, Vajda, S. How good is automated protein docking? Proteins: Structure, Function, and Bioinformatics. 2013 Dec; 81(12):2159-66. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934018/pdf/nihms556382.pdf

[8]Yan, Y., Tao, H., He, J., & Huang, S. (2020). The HDOCK server for integrated protein–protein docking. Nature Protocols, 15(5), 1829-1852. https://doi.org/10.1038/s41596-020-0312-x

[9] Sekar, R. B., & Periasamy, A. (2003). Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations. The Journal of cell biology, 160(5), 629–633. https://doi.org/10.1083/jcb.200210140

Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    INCOMPATIBLE WITH RFC[21]
    Illegal XhoI site found at 1606
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    INCOMPATIBLE WITH RFC[1000]
    Illegal BsaI.rc site found at 2419