Difference between revisions of "Homology-modelling HEXA"

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(RMSD and TM-Score)
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== Homology structure groups ==
 
== Homology structure groups ==
   
We choosed one protein from each sequence identity group which is shown in the following table. This proteins were used for almost every homology based applications which were discribed below.
+
We chose one protein from each sequence identity group which is shown in the following table. These proteins were used for almost every homology based applications which were described below.
   
 
(The complete HHsearch output can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/hh_search_output here ]])
 
(The complete HHsearch output can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/hh_search_output here ]])
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|-
 
|-
 
|}
 
|}
  +
<br><br>
 
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
== Swissmodel ==
 
== Swissmodel ==
   
 
=== Calculation ===
 
=== Calculation ===
To calculate the models with Swiss-Model we used the [[http://swissmodel.expasy.org/ Webserver]]. For the template with high sequence identity, we used the automated and the alignment method, for the other two templates we only used the alignment method.
+
To calculate the models with Swissmodel we used the [[http://Swissmodel.expasy.org/ Webserver]]. For the template with high sequence identity, we used the automated and the alignment method, for the other two templates we only used the alignment method.
   
 
The used alignments can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php?title=Homology-modelling_HEXA/swissmodel_ali here]].
 
The used alignments can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php?title=Homology-modelling_HEXA/swissmodel_ali here]].
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
   
 
=== Results ===
 
=== Results ===
Line 53: Line 56:
 
The detailed prediction can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/swissmodel_3CUI here]]
 
The detailed prediction can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/swissmodel_3CUI here]]
   
Swiss-Model returns some scores to give the user the possibility to estimate the quality of the predicted model. This scores are shown in the next table. The most important score is the QMEAN4 score, because this score consists of the other scores above and gives the user the possibility to compare the different results.
+
Swissmodel returns some scores to give the user the possibility to estimate the quality of the predicted model. These scores are shown in the next table. The most important score is the QMEAN4 score, because this score consists of the other scores above and gives the user the possibility to compare the different results.
   
   
Line 86: Line 89:
   
   
Furthermore, Swiss-Modeler returns two different structure predictions, one of the HEXA-HUMAN with 3CUI as template structure and one of teh wrong predicted residue. This two predictions are displayed in the following figures:
+
Furthermore, Swissmodeler returns two different structure predictions, one of the HEXA-HUMAN with 3CUI as template structure and one of the wrong predicted residue. These two predictions are displayed in Figure 1 and Figure 2:
   
 
{|
 
{|
| [[Image:swissmodel_3cui.png|thumb|Prediction the structure of HEXA_HUMAN with 3cui as template structure]]
+
| [[Image:Swissmodel_3cui.png|thumb|Figure 1: Prediction the structure of HEXA_HUMAN with 3cui as template structure]]
| [[Image:wrong_res_3cui.png|thumb|Prediction of the wrong predicted residues]]
+
| [[Image:wrong_res_3cui.png|thumb|Figure 2: Prediction of the wrong predicted residues (wrong predicted residues are colored in red)]]
 
|}
 
|}
   
  +
On Figure 2, we can see that most of the residues are colored in red and therefore, we know that the result of our prediction is really bad.
   
Besides, Swissmodel creates some pictures, which show the qualitity of the model, as well. This ones were shown in the following figures:
+
Besides, Swissmodel creates some pictures, which show the quality of the model, as well. This ones were shown in the figure 3, figure 4, figure 5 and figure 6:
   
 
{|
 
{|
| [[Image:zscore_qmean_3cui.png|thumb|Visualisation of the QMEAN Z-Score for this model]]
+
| [[Image:zscore_qmean_3cui.png|thumb|Figure 3: Visualization of the QMEAN Z-Score for this model]]
| [[Image:gaus_qmean_3cui.png|thumb|Visualisation of the QMEAN score in comparison with a gaussian distribution]]
+
| [[Image:gaus_qmean_3cui.png|thumb|Figure 4: Visualization of the QMEAN score in comparison with a gaussian distribution]]
| [[Image:score_comp_3cui.png|thumb|center|Quality of the model in comparison to a X-ray structure]]
+
| [[Image:score_comp_3cui.png|thumb|center|Figure 5: Quality of the model in comparison to a X-ray structure]]
| [[Image:plot_wrong_res_3cui.png‎ |thumb|Plot, which shows the wrong predicted residues of this model]]
+
| [[Image:plot_wrong_res_3cui.png‎ |thumb|Figure 6: Plot, which shows the wrong predicted residues of this model]]
 
|}
 
|}
   
  +
As we can see on the figure 3, our model is beyond the curve of the different Z-scores, so our model is not very good. If we have a look at the figure 4, we can see, that the Q-means score of the model is left of the gaussian curve, which also shows that this model is not very good. Next it is possible to compare the quality of our model with X-ray structure models. Normally, X-ray structure models have a Z-score about 0. In our case, shown at Figure 5 all scores are significant less than 0, so again this picture shows us, that the model is bad. The best part of our model is the C-beta interactions, which have a score of -4.65, which is far away from 0. The other scores are lower than the C-beta interactions score, so therefore, they are worse. We can therefore suggest, that the model is quite bad. The last figure, figure 6, is a plot, which shows the wrong predicted residues. Most of the residues have a prediction error more than 10, which is extremely high.<br>
 
  +
So in general, we can see that our model does not have a very good quality. That should be kept in mind by analysing the prediction result, because it is nearly impossible to get a good prediction with a bad template.
  +
   
 
'''3LUT:'''
 
'''3LUT:'''
   
We decided to model the 3D structure with the template structure which has a very low sequence identity. Therefore, we decided to model the structure with 3LUT. Sadly, Swissmodel could not model the structure with this template, because Swissmodel was not able to create an alignment which can be used as basic for the model. Because of this, we decided to calculate a model with 3HN3 as template, which was the next template with a little bit more sequence identity. Therefore, we present here the results of the modelling with 3HN3, whereas by the other methods, we used 3LUT.
+
We decided to model the 3D structure with the template structure which has a very low sequence identity. Therefore, we decided to model the structure with 3LUT. Sadly, Swissmodel could not model the structure with this template, because it was not able to create an alignment which can be used as basic for the model. Because of this, we decided to calculate a model with 3HN3 as template, which was the next template with a little bit more sequence identity. Therefore, we present here the results of the modeling with 3HN3, whereas by the other methods, we used 3LUT.
 
<br>
 
<br>
   
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The detailed prediction can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/swissmodel_3LUT here]]
 
The detailed prediction can be found [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/swissmodel_3LUT here]]
   
Swiss-Model returns some scores to give the user the possibility to estimate the quality of the predicted model. This scores are shown in the next table. The most important score is the QMEAN4 score, because this score consists of the other scores above and gives the user the possibility to compare the different results.
+
Swissmodel returns some scores to give the user the possibility to estimate the quality of the predicted model. This scores are shown in the next table. The most important score is the QMEAN4 score, because this score consists of the other scores above and gives the user the possibility to compare the different results.
   
 
{| border="1" style="text-align:center; border-spacing:0;"
 
{| border="1" style="text-align:center; border-spacing:0;"
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|}
 
|}
   
Furthermore, Swiss-Modeler returns two different structure predictions, one of the HEXA-HUMAN with 3hn3 as template structure and one of teh wrong predicted residue. This two predictions are displayed in the following figures
+
Furthermore, Swissmodeler returns two different structure predictions, one of the HEXA-HUMAN with 3hn3 as template structure and one of the wrong predicted residue. This two predictions are displayed in Figure 7 and Figure 8.
   
 
{|
 
{|
| [[Image:swissmodel_3lut.png|thumb|Prediction the structure of HEXA_HUMAN with 3hn3 as template structure]]
+
| [[Image:Swissmodel_3lut.png|thumb|Figure 7: Prediction the structure of HEXA_HUMAN with 3hn3 as template structure]]
| [[Image:wrong_res_3lut.png|thumb|Prediction of the wrong predicted residues]]
+
| [[Image:wrong_res_3lut.png|thumb|Figure 8: Prediction of the wrong predicted residues (wrong predicted residues are colored in red]]
 
|}
 
|}
   
  +
On Figure 8, we can see that there is a hugh amount of wrong predicted residues so therefore, our prediction result is really bad.
   
Besides, Swissmodel creates some pictures, which show the qualitity of the model, as well. This ones were shown in the following figures:
+
Besides, Swissmodel creates some pictures, which show the quality of the model, as well. This ones were shown in Figure 9, Figure 10, Figure 11 and Figure 12.
   
 
{|
 
{|
| [[Image:zscore_qmean_3lut.png|thumb|Visualisation of the QMEAN Z-Score for this model]]
+
| [[Image:zscore_qmean_3lut.png|thumb|Figure 9: Visualization of the QMEAN Z-Score for this model]]
| [[Image:gaus_qmean_3lut.png|thumb|Visualisation of the QMEAN score in comparison with a gaussian distribution]]
+
| [[Image:gaus_qmean_3lut.png|thumb|Figure 10: Visualization of the QMEAN score in comparison with a gaussian distribution]]
| [[Image:score_comp_3lut.png|thumb|center|Quality of the model in comparison to a X-ray structure]]
+
| [[Image:score_comp_3lut.png|thumb|center|Figure 11: Quality of the model in comparison to a X-ray structure]]
| [[Image:plot_wrong_res_3lut.png‎ |thumb|Plot, which shows the wrong predicted residues of this model]]
+
| [[Image:plot_wrong_res_3lut.png‎ |thumb|Figure 12: Plot, which shows the wrong predicted residues of this model]]
 
|}
 
|}
  +
  +
If we have a look at the Figure 9, Figure 10 and Figure 11, we can see that the model is worse than the model of 3cui. This was expected, because the sequence identity between our template and target is very low.
  +
The Figure 9 shows us, that the model is really bad. The point has nearly the same y-value as the 3cui model, but the protein is shorter and therefore, normally shorter models have a higher QMEAN score, which is not the case here. The QMEAN score is a little bit lower so therefore this model is worse than the model of 3cui. The same is possible to see in Figure 10, where we can see that the Z-score of our model is left of the gaussian distribution. Figure 11 shows us how the quality of our model is compared to X-ray structures and again it is possible to see, that the quality is quite bad, worse than the model of 3cui. Interestingly, the prediction error of the different residues, shown in Figure 12, is not that high as we seen before by looking at the plot of 3cui. So it seems, that more residues could be predicted in the right way, although the model is very bad.
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
   
 
=== RMSD and TM-Score ===
 
=== RMSD and TM-Score ===
   
The next step after the use of Swissmodel is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins don't receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal. <br>
+
The next step after the use of Swissmodel is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there is only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal. <br>
We used the RMSD calculation by Pymol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to caluclate the RMSD. <br>
+
We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD values were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD. <br>
So first of all, it is important to clarify how these two methods calculate the RMSD. Pymol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results. <br>
+
So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results. <br>
 
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different. <br>
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different. <br>
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
   
  +
The following table displays the TM-score and the RMSD for the Swissmodel results. Here we can compare only the results of 2CUI and 3HN3, because we do not get a result for 3BC9. Looking at the RMSD calculated by PyMol we can see that both are really high. This agrees with the structure alignment calculated by PyMol. The most of the structure elements do not match with the one from hexosaminidase chain A.
  +
In contrast, the RMSD of TM-align is much lower, but not the best as well. The TM-score is low which means it is not good as well. This is also shown in the structure alignment of TM-align. Here in the most cases the structures do not fit good (compare Figure 13 – Figure 16).
   
 
{| border="1" style="text-align:center; border-spacing:0;"
 
{| border="1" style="text-align:center; border-spacing:0;"
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|3HN3
 
|3HN3
 
|-
 
|-
|RMSD (Pymol)
+
|RMSD (PyMol)
 
|no result
 
|no result
 
|24.447
 
|24.447
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|0.40661
 
|0.40661
 
|-
 
|-
|Structural alignment (Pymol)
+
|Structural alignment (PyMol)
 
|no result
 
|no result
|[[Image:superpose_swissmodel_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by Swissmodel]]
+
|[[Image:superpose_swissmodel_3cui.png|thumb|150px|Figure 13: Superposition by PyMol with the structure predicted by Swissmodel]]
|[[Image:superpose_swissmodel_3hn3.png|thumb|150px|Superposition by Pymol with the structure predicted by Swissmodel]]
+
|[[Image:superpose_swissmodel_3hn3.png|thumb|150px|Figure 14: Superposition by PyMol with the structure predicted by Swissmodel]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
 
|no result
 
|no result
|[[Image:tm_superpose_swissmodel_3cui.png|thumb|150px|Superposition by TM-align with the structure predicted by Swissmodel]]
+
|[[Image:tm_superpose_swissmodel_3cui.png|thumb|150px|Figure 15: Superposition by TM-align with the structure predicted by Swissmodel]]
|[[Image:tm_superpose_swissmodel_3hn3.png|thumb|150px|Superposition by TM-align with the structure predicted by Swissmodel]]
+
|[[Image:tm_superpose_swissmodel_3hn3.png|thumb|150px|Figure 16: Superposition by TM-align with the structure predicted by Swissmodel]]
 
|-
 
|-
 
|}
 
|}
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
   
 
=== Discussion ===
 
=== Discussion ===
   
  +
In this section we discuss the results of 3CUI and 3HN3, because 3BC9 and 3LUT did not work with Swissmodel.
  +
First of all, we look at the results returned by Swissmodel itself. Here, we can see that for both structure the predicted model is really bad. The different scores and the plots created by Swissmodel display the quality of the predicted structure. All these plots confirm that both predicted models were not good. Furthermore, the different RMSD and the TM-score indicate the same. Like expected the result for 3HN3 is even worser than for 3CUI. This can be explained by the fact that 3HN3 has a smaller sequence identity to hexosaminidase A than 3CUI. It is really sad, that there is no result for 3BC9 because it would be really interesting if this one would receive the best prediction. This would be expected because it has the highest sequence identity to hexosaminidase chain A.
  +
All in all, these two predictions are not very good and have a bad quality which is confirmed by the received scores and plots. This and the fact that Swissmodel was not possible to calculate a prediction for 3BC9 display that Swissmodel is not the best homology modeling tool.
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
== Modeller ==
 
== Modeller ==
   
 
=== Calculation ===
 
=== Calculation ===
   
We used Modeller from the command line. Therefore we followed the instructions, described [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Task_4:_Homology_based_structure_predictions#Homology_modelling_with_Modeller here]].
+
We used Modeller from the command line. Therefore we followed the instructions, described [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Resource_software#Modeller here]].
   
 
First of all, we created an alignment for each of our three selected sequences. In the next step we used Modeller to model the 3D structure of the protein.
 
First of all, we created an alignment for each of our three selected sequences. In the next step we used Modeller to model the 3D structure of the protein.
   
 
For Modeller we used the Pir Alignment format, which can be found here: [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/3bc9.pir 3BC9]], [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/3cui.pir 3CUI]], [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/3lut.pir, 3LUT]]
 
For Modeller we used the Pir Alignment format, which can be found here: [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/3bc9.pir 3BC9]], [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/3cui.pir 3CUI]], [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Homology-modelling_HEXA/3lut.pir, 3LUT]]
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
   
 
=== Results ===
 
=== Results ===
   
Modeller calculated for each structure (3BC9, 3CUI and 3LUT) one model which can be seen in the next pictures:
+
Modeller calculated one model for each structure (3BC9, 3CUI and 3LUT) which can be seen in the Figure 17, Figure 18 and Figure 19:
   
 
{| border="1" style="text-align:center; border-spacing:0;"
 
{| border="1" style="text-align:center; border-spacing:0;"
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|3LUT
 
|3LUT
 
|-
 
|-
|[[Image:modeller_3bc9.png|center|thumb|3D structure of HEXA_HUMAN with 3BC9 as template predicted by Modeller.]]
+
|[[Image:modeller_3bc9.png|center|thumb|Figure 17: 3D structure of HEXA_HUMAN with 3BC9 as template predicted by Modeller.]]
|[[Image:modeller_3cui.png|center|thumb|3D structure of HEXA_HUMAN with 3CUI as template predicted by Modeller.]]
+
|[[Image:modeller_3cui.png|center|thumb|Figure 18: 3D structure of HEXA_HUMAN with 3CUI as template predicted by Modeller.]]
|[[Image:modeller_3lut.png|center|thumb|3D structure of HEXA_HUMAN with 3LUT as template predicted by Modeller.]]
+
|[[Image:modeller_3lut.png|center|thumb|Figure 19: 3D structure of HEXA_HUMAN with 3LUT as template predicted by Modeller.]]
 
|-
 
|-
 
|}
 
|}
  +
<br><br>
 
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
=== RMSD and TM-Score ===
 
=== RMSD and TM-Score ===
   
The next step after the use of Swissmodel is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins don't receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal. <br>
+
The next step after the use of Modeller is to check the quality of the predicted structure. Therefore, we calculated the RMSD score and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal. <br>
We used the RMSD calculation by Pymol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to caluclate the RMSD. <br>
+
We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD. <br>
So first of all, it is important to clarify how these two methods calculate the RMSD. Pymol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results. <br>
+
So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results. <br>
 
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different. <br>
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different. <br>
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
  +
  +
The following table displays the two different RMSD scores and the TM-score as well as the structure alignment for 3BC9 (Figure 20, Figure 23), 3CUI (Figure 21, Figure 24) and 3LUT (Figure 22, Figure 25). Looking at the RMSD of PyMol we can see that all RMSDs are very high. For 3BC9 we receive the highest one and for 3CUI the lowest.
  +
This agrees with the structure alignment with PyMol where all different structures match badly with the hexosaminidase chain A.
  +
The RMSDs of TM-align are all lower than the one with PyMol. Here, the scores are more equal, but the order is the same. 3BC9 delivers the worst RMSD and 3CUI the best one. The TM-score delivers the same result which means it is relatively bad and the order is the same. This can be seen in the structure alignment of TM-align as well. In all three cases the structures match very badly.
   
 
{| border="1" style="text-align:center; border-spacing:0;"
 
{| border="1" style="text-align:center; border-spacing:0;"
Line 264: Line 290:
 
|-
 
|-
 
|Structural alignment (Pymol)
 
|Structural alignment (Pymol)
|[[Image:superpose_modeller_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by Modeller]]
+
|[[Image:superpose_modeller_3bc9.png|thumb|150px|Figure 20: Superposition by PyMol with the structure predicted by Modeller]]
|[[Image:superpose_modeller_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by Modeller]]
+
|[[Image:superpose_modeller_3cui.png|thumb|150px|Figure 21: Superposition by PyMol with the structure predicted by Modeller]]
|[[Image:superpose_modeller_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by Modeller]]
+
|[[Image:superpose_modeller_3lut.png|thumb|150px|Figure 22: Superposition by PyMol with the structure predicted by Modeller]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
|[[Image:tm_superpose_modeller_3bc9.png|thumb|150px|Superposition by TM-align with the structure predicted by Modeller]]
+
|[[Image:tm_superpose_modeller_3bc9.png|thumb|150px|Figure 23: Superposition by TM-align with the structure predicted by Modeller]]
|[[Image:tm_superpose_modeller_3cui.png|thumb|150px|Superposition by TM-align with the structure predicted by Modeller]]
+
|[[Image:tm_superpose_modeller_3cui.png|thumb|150px|Figure 24: Superposition by TM-align with the structure predicted by Modeller]]
|[[Image:tm_superpose_modeller_3lut.png|thumb|150px|Superposition by TM-align with the structure predicted by Modeller]]
+
|[[Image:tm_superpose_modeller_3lut.png|thumb|150px|Figure 25: Superposition by TM-align with the structure predicted by Modeller]]
 
|-
 
|-
 
|}
 
|}
  +
<br><br>
 
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
=== Discussion ===
 
=== Discussion ===
   
  +
Modeller delivers only the predicted 3D-structures. There are no additional scores or plots which assign the quality of the prediction. So the only possible way to distinguish the quality is in our case the different RMSD and the TM-score. Here, we can see that the RMSDs for all different models are very high. This indicates that the structure alignment is very bad and that there is high divergence between the original structure of hexosaminidase A and the predicted one. Very strange is the order of RMSDs and the TM-score. The resulting best prediction is the one with 3CUI, which has not the highest sequence identity. We would expect that the best result should be returned for 3BC9, because of the high sequence identity. 3LUT delivers the worst predictions which agrees with the fact that it hast the lowest identity with hexosaminidase chain A. <br>
  +
All in all, all three predicted structures are not very good and match badly with the original one.
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
== iTasser ==
 
== iTasser ==
   
Line 282: Line 313:
 
To calculate our models with iTasser we used the [[http://zhanglab.ccmb.med.umich.edu/I-TASSER/ Webserver]].
 
To calculate our models with iTasser we used the [[http://zhanglab.ccmb.med.umich.edu/I-TASSER/ Webserver]].
   
For the calculation we had to define the target and template sequence. Therefore, the target sequence had to be pasted into a frame whereas the tamplate was specified by the PDB-id. Furthermode, we exclude our own sequence and very similar as a template from the iTasser library. Therefore, we first define a cutoff of 80%. Afterwards we also created a further input-file wich contains our own sequence and the similar ones. This should prevent that this sequences were not used. To get the similar sequence we used 3D-Blast. In our case we did not found any other sequence with a similar structure at a high score. This means that our input file only contained our own structure.
+
For the calculation we had to define the target and template sequence. Therefore, the target sequence had to be pasted into a frame whereas the template was specified by the PDB-id. Furthermore, we exclude our own sequence and very similar as a template from the iTasser library. Therefore, we first define a cutoff of 80%. Afterwards we also created a further input-file which contains our own sequence and the similar ones. This should prevent that this sequences were used. To get the similar sequence we used 3D-Blast. In our case we did not found any other sequence with a similar structure at a high score. This means that our input file only contained our own structure.
  +
<br><br>
 
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
=== Results ===
 
=== Results ===
   
iTasser delivers a wide range of result with many predicted informations. The first ones are the predicted secondary structure and the predicted solvent accessibility. Furhtermore it provides the first top 5 predicted models, the predicted function, predicted GO terms and the predicted binding site. The predicted secondary structure elements are shown as H for alpha helix (red),S for beta sheet (blue) & C for coil (yellow). The predicted solvent accessibility has values range from 0 (buried residue) to 9 (highly exposed residue) which describes the solvent accessibility. The predicted function are the predicted EC numbers which are the TM-score, the RMSD score etc. The predicted GO terms are the molecular function, biological process or the cellular location. There are many different predicted GO terms for each protein.
+
iTasser delivers a wide range of result with many predicted informations. The first ones are the predicted secondary structure and the predicted solvent accessibility. Furthermore it provides the first top 5 predicted models, the predicted function, predicted GO terms and the predicted binding site. The predicted secondary structure elements are shown as H for alpha helix (red), S for beta sheet (blue) and C for coil (yellow). The predicted solvent accessibility has values range from 0 (buried residue) to 9 (highly exposed residue) which describe the solvent accessibility. The predicted function are the predicted EC numbers which are the TM-score, the RMSD score and so on. The predicted GO terms are the molecular function, biological process or the cellular location. There are many different predicted GO terms for each protein.
   
   
 
'''3BC9''':
 
'''3BC9''':
   
The following three pictures show the predicted secondary structure, the predicted solvent accessibility and the predicted binding site of 3BC9.<br>
+
The following three Figures show the predicted secondary structure (Figure 26), the predicted solvent accessibility (Figure 27) and the predicted binding site (Figure 28) of 3BC9.<br>
The predicted secondary structur contains 17 alpha-helices and 18 beta-sheets. At first sight it agrees in the number with the predicted secondary structure from the last task. There, the number for alpha-helices was 14-16 and the number of sheets was 15. This is a good sign and displays that the predicted structure is probably good.<br>
+
The predicted secondary structure contains 17 alpha-helices and 18 beta-sheets.<br>
The predicted solvent accessibility is displayed by solvent accesibility value that ranges from 0 (buried residue) to 9 (highly exposed residue). <br>
+
The predicted solvent accessibility is displayed by solvent accessibility value that ranges from 0 (buried residue) to 9 (highly exposed residue). <br>
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atom, red for O-atome and blue for N-atome. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3BC9 were 10 amino acids predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 2 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid, 1 Lysin and 1 Tyrosine.
+
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atom, red for O-atoms and blue for N-atoms. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3BC9 10 amino acids were predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 2 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid, 1 Lysin and 1 Tyrosine.
 
 
 
{| class="centered"
 
{| class="centered"
|[[Image:3bc9_sec_pred.png|thumb|center| Predicted secondary structure of 3BC9(chain A)]]
+
|[[Image:3bc9_sec_pred.png|thumb|center| Figure 26: Predicted secondary structure of 3BC9(chain A)]]
|[[Image:3bc9_acc_pred.png|thumb|center| Predicted solvent accessibility of 3BC9(chain A)]]
+
|[[Image:3bc9_acc_pred.png|thumb|center| Figure 27: Predicted solvent accessibility of 3BC9(chain A)]]
|[[Image:3bc9_bind_pred.png|thumb|center| Predicted binding site of 3BC9(chain A)]]
+
|[[Image:3bc9_bind_pred.png|thumb|center| Figure 28: Predicted binding site of 3BC9(chain A)]]
 
|}
 
|}
   
The following pictures show the top 5 models predicted by iTasser. This five model display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3BC9 the C-score goes from about -2.1 to 0.2. The C-score indicates that the most confident model is the first one and the worst confident is model 4. Outstanding is that model 2 and 3 have the same C-score.
+
The following pictures (Figure 29 – Figure 33) show the top 5 models predicted by iTasser. This five models display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3BC9 the C-score goes from about -2.1 to 0.2. The C-score indicates that the most confident model is the first one (Figure 29) and the worst confident model is model 4 (Figure 32). Outstanding is that model 2 (Figure 30) and 3 (Figure 31) have the same C-score.
   
 
{| class="centered"
 
{| class="centered"
| [[Image:3bc9_model1.png|thumb|center| First predicted model for 3BC9 (chain A) with an C-score of 0.139]]
+
| [[Image:3bc9_model1.png|thumb|center| Figure 29: First predicted model for 3BC9 (chain A) with an C-score of 0.139]]
| [[Image:3bc9_model2.png |thumb| Second predicted model for 3BC9 (chain A) with an C-score of -1.069]]
+
| [[Image:3bc9_model2.png |thumb| Figure 30: Second predicted model for 3BC9 (chain A) with an C-score of -1.069]]
| [[Image:3bc9_model3.png |thumb| Third predicted model for 3BC9 (chain A) with an C-score of -1.069]]
+
| [[Image:3bc9_model3.png |thumb| Figure 31: Third predicted model for 3BC9 (chain A) with an C-score of -1.069]]
| [[Image:3bc9_model4.png |thumb| Fourth predicted model for 3BC9 (chain A) with an C-score of -2.059]]
+
| [[Image:3bc9_model4.png |thumb| Figure 32: Fourth predicted model for 3BC9 (chain A) with an C-score of -2.059]]
| [[Image:3bc9_model5.png |thumb| Fifth predicted model for 3BC9 (chain A) with an C-score of -1.645]]
+
| [[Image:3bc9_model5.png |thumb| Figure 33: Fifth predicted model for 3BC9 (chain A) with an C-score of -1.645]]
 
|}
 
|}
   
Line 317: Line 349:
 
'''3CUI''':
 
'''3CUI''':
   
The following three pictures show the predicted secondary structure, the predicted solvent accessibility and the predicted binding site of 3CUI.<br>
+
The following three pictures show the predicted secondary structure (Figure 34), the predicted solvent accessibility (Figure 35) and the predicted binding (Figure 36) site of 3CUI.<br>
  +
The predicted secondary structure contains 17 alpha-helices and 18 beta-sheets. <br>
The predicted secondary structur contains 17 alpha-helices and 18 beta-sheets. At first sight it agrees in the number with the predicted secondary structure from the last task as well. There, the number for alpha-helices was 14-16 and the number of sheets was 15. Further more it corresponds to the number of helices predicted for 3BC9. This is a good sign and displays that the predicted structure is probably good.<br>
 
The predicted solvent accessibility is displayed by solvent accesibility value that ranges from 0 (buried residue) to 9 (highly exposed residue). <br>
+
The predicted solvent accessibility is displayed by solvent accessibility value that ranges from 0 (buried residue) to 9 (highly exposed residue). <br>
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atom, red for O-atome and blue for N-atome. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3CUI were 9 amino acids predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 2 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid and 1 Alanine.
+
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atoms, red for O-atoms and blue for N-atoms. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3CUI were 9 amino acids predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 2 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid and 1 Alanine.
   
 
{| class="centered"
 
{| class="centered"
|[[Image:3cui_sec_pred.png|thumb|center| Predicted secondary structure of 3CUI (chain A)]]
+
|[[Image:3cui_sec_pred.png|thumb|center| Figure 34: Predicted secondary structure of 3CUI (chain A)]]
|[[Image:3cui_acc_pred.png|thumb|center| Predicted solvent accessibility of 3CUI(chain A)]]
+
|[[Image:3cui_acc_pred.png|thumb|center| Figure 35: Predicted solvent accessibility of 3CUI(chain A)]]
|[[Image:3cui_bdg_pred.png|thumb|center| Predicted binding site of 3CUI(chain A)]]
+
|[[Image:3cui_bdg_pred.png|thumb|center| Figure 36: Predicted binding site of 3CUI(chain A)]]
 
|}
 
|}
   
The following pictures show the top 5 models predicted by iTasser. This five model display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3CUI the C-score goes from about -3.4 to 0.4. The C-score indicates that the most confident model is the first one and the worst confident is model 5.
+
The following pictures (Figure 37 – Figure 41) show the top 5 models predicted by iTasser. This five model display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3CUI the C-score goes from about -3.4 to 0.4. The C-score indicates that the most confident model is the first one and the worst confident is model 5.
   
 
{| class="centered"
 
{| class="centered"
| [[Image:3cui_model1.gif|thumb|center| First predicted model for 3CUI (chain A) with an C-score of 0.349]]
+
| [[Image:3cui_model1.gif|thumb|center| Figure 37: First predicted model for 3CUI (chain A) with an C-score of 0.349]]
| [[Image:3cui_model2.gif |thumb| Second predicted model for 3CUI (chain A) with an C-score of 0.076]]
+
| [[Image:3cui_model2.gif |thumb| Figure 38: Second predicted model for 3CUI (chain A) with an C-score of 0.076]]
| [[Image:3cui_model3.gif |thumb| Third predicted model for 3CUI (chain A) with an C-score of -2.544]]
+
| [[Image:3cui_model3.gif |thumb| Figure 39: Third predicted model for 3CUI (chain A) with an C-score of -2.544]]
| [[Image:3cui_model4.gif |thumb| Fourth predicted model for 3CUI (chain A) with an C-score of -0.906]]
+
| [[Image:3cui_model4.gif |thumb| Figure 40: Fourth predicted model for 3CUI (chain A) with an C-score of -0.906]]
| [[Image:3cui_model5.gif |thumb| Fifth predicted model for 3CUI (chain A) with an C-score of -3.362]]
+
| [[Image:3cui_model5.gif |thumb| Figure 41: Fifth predicted model for 3CUI (chain A) with an C-score of -3.362]]
 
|}
 
|}
   
Line 343: Line 375:
 
'''3LUT''':
 
'''3LUT''':
   
The following three pictures show the predicted secondary structure, the predicted solvent accessibility and the predicted binding site of 3LUT.<br>
+
The following three pictures show the predicted secondary structure (Figure 42) , the predicted solvent accessibility (Figure 43) and the predicted binding (Figure 44) site of 3LUT.<br>
  +
The predicted secondary structure contains 17 alpha-helices and 18 beta-sheets. <br>
The predicted secondary structur contains 17 alpha-helices and 18 beta-sheets. At first sight it agrees in the number with the predicted secondary structure from the last task as well. There, the number for alpha-helices was 14-16 and the number of sheets was 15. Further more it corresponds to the number of helices predicted for 3BC9 and 3CUI. This is a good sign and displays that the predicted structure is probably good.<br>
 
The predicted solvent accessibility is displayed by solvent accesibility value that ranges from 0 (buried residue) to 9 (highly exposed residue). <br>
+
The predicted solvent accessibility is displayed by solvent accessibility value that ranges from 0 (buried residue) to 9 (highly exposed residue). <br>
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atom, red for O-atome and blue for N-atome. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3CUI were 9 amino acids predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 1 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid, 1 Tyrosine and 1 Asparagine.
+
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atoms, red for O-atoms and blue for N-atoms. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3CUI were 9 amino acids predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 1 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid, 1 Tyrosine and 1 Asparagine.
   
   
 
{| class="centered"
 
{| class="centered"
|[[Image:3lut_sec.png|thumb|center| Predicted secondary structure of 3LUT (chain A)]]
+
|[[Image:3lut_sec.png|thumb|center| Figure 42: Predicted secondary structure of 3LUT (chain A)]]
|[[Image:3lut_acc.png|thumb|center| Predicted solvent accessibility of 3LUT(chain A)]]
+
|[[Image:3lut_acc.png|thumb|center| Figure 43: Predicted solvent accessibility of 3LUT(chain A)]]
|[[Image:3lut_BdgSitePred.png|thumb|center| Predicted binding site of 3LUT(chain A)]]
+
|[[Image:3lut_BdgSitePred.png|thumb|center| Figure 44: Predicted binding site of 3LUT(chain A)]]
 
|}
 
|}
   
The following pictures show the top 5 models predicted by iTasser. This five model display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3LUT the C-score goes from about -3.6 to 0.3. The C-score indicates that the most confident model is the first one and the worst confident is model 5.
+
The following pictures (Figure 45 – Figure 49) show the top 5 models predicted by iTasser. This five model display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3LUT the C-score goes from about -3.6 to 0.3. The C-score indicates that the most confident model is the first one and the worst confident is model 5.
   
 
{| class="centered"
 
{| class="centered"
| [[Image:model1_3lut.png|thumb|center| First predicted model for 3LUT (chain A) with an C-score of 0.268]]
+
| [[Image:model1_3lut.png|thumb|center| Figure 45: First predicted model for 3LUT (chain A) with an C-score of 0.268]]
| [[Image:model2_3lut.png |thumb| Second predicted model for 3LUT (chain A) with an C-score of -0.093]]
+
| [[Image:model2_3lut.png |thumb| Figure 46: Second predicted model for 3LUT (chain A) with an C-score of -0.093]]
| [[Image:model3_3lut.png |thumb| Third predicted model for 3LUT (chain A) with an C-score of -0.163]]
+
| [[Image:model3_3lut.png |thumb| Figure 47: Third predicted model for 3LUT (chain A) with an C-score of -0.163]]
| [[Image:model4_3lut.png |thumb| Fourth predicted model for 3LUT (chain A) with an C-score of -3.189]]
+
| [[Image:model4_3lut.png |thumb| Figure 48: Fourth predicted model for 3LUT (chain A) with an C-score of -3.189]]
| [[Image:model5_3lut.png |thumb| Fifth predicted model for 3LUT (chain A) with an C-score of -3.521]]
+
| [[Image:model5_3lut.png |thumb| Figure 49: Fifth predicted model for 3LUT (chain A) with an C-score of -3.521]]
 
|}
 
|}
   
 
The detailed prediction can be found [[http://zhanglab.ccmb.med.umich.edu/I-TASSER/output/S78786/ here]]
 
The detailed prediction can be found [[http://zhanglab.ccmb.med.umich.edu/I-TASSER/output/S78786/ here]]
  +
<br><br>
 
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
=== RMSD and TM-Score ===
 
=== RMSD and TM-Score ===
   
The next step after the use of Swissmodel is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins don't receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal. <br>
+
The next step after the use of iTasser is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal. <br>
We used the RMSD calculation by Pymol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to caluclate the RMSD. <br>
+
We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD. <br>
So first of all, it is important to clarify how these two methods calculate the RMSD. Pymol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results. <br>
+
So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results. <br>
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different. <br>
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different. <br>
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
Line 377: Line 410:
 
'''3BC9'''
 
'''3BC9'''
   
The following table displays the RMSD and the TM-score from Pymol and TM-aling. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green).<br>
+
The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore, it contains the structure alignments of the predicted models of both methods (red) and the original hexosaminidase chain A (green) (Figure 50 - 59).<br>
Looking at the RMSD from Pymol, the best models are the first and the fifth one. The other three model have a very high RMSD. This agrees with the structure alignment of pymol where these three models align very bad and many structure elements do not align at all.<br>
+
Looking at the RMSD from PyMol, the best models are the first (Figure 50) and the fifth one (Figure 54). The other three model have a very high RMSD. This agrees with the structure alignment of PyMol where these three models align very bad and many structure elements do not align at all.<br>
In contrast, the RMSD of TM-align is in the most cases much better. Furthermore the fourth model does not belong to the best ones and is almost similar to the other model 2-5. Only model 1 has an very good RMSD.
+
In contrast, the RMSD of TM-align is in the most cases much better. Furthermore, the fourth model (Figure 58) does not belong to the best ones and is almost similar to the other model 2-5 (Figure 56 – Figure 59). Only model 1 (Figure 55) has an very good RMSD.
 
The TM-score agrees more with the RMSD of PyMol which means the best score is received for model 1 which followed by model 4. The other three models have a worser TM-score.
 
The TM-score agrees more with the RMSD of PyMol which means the best score is received for model 1 which followed by model 4. The other three models have a worser TM-score.
 
The structure alignment of TM-align agrees with the TM-score and nearly with the RMSD. For all cases the alignments seems to be only a few better than the one with PyMol and not remarkable more structure elements do agree with the original structure of hexosaminidase chain A. Structure three is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. A closer look at the other two structures which have a bad RMSD as well show that they have only a few matches as well. <br>
 
The structure alignment of TM-align agrees with the TM-score and nearly with the RMSD. For all cases the alignments seems to be only a few better than the one with PyMol and not remarkable more structure elements do agree with the original structure of hexosaminidase chain A. Structure three is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. A closer look at the other two structures which have a bad RMSD as well show that they have only a few matches as well. <br>
Line 414: Line 447:
 
|-
 
|-
 
|Structural alignment (Pymol)
 
|Structural alignment (Pymol)
|[[Image:pymol_itasser_model1_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 1)]]
+
|[[Image:pymol_itasser_model1_3bc9.png|thumb|150px|Figure 50: Superposition by PyMol with the structure predicted by iTasser (Model 1)]]
|[[Image:pymol_itasser_model2_v2_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 2)]]
+
|[[Image:pymol_itasser_model2_v2_3bc9.png|thumb|150px|Figure 51: Superposition by PyMol with the structure predicted by iTasser (Model 2)]]
|[[Image:pymol_itasser_model3_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 3)]]
+
|[[Image:pymol_itasser_model3_3bc9.png|thumb|150px|Figure 52: Superposition by PyMol with the structure predicted by iTasser (Model 3)]]
|[[Image:pymol_itasser_model4_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 4)]]
+
|[[Image:pymol_itasser_model4_3bc9.png|thumb|150px|Figure 53: Superposition by PyMol with the structure predicted by iTasser (Model 4)]]
|[[Image:pymol_itasser_model5_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 5)]]
+
|[[Image:pymol_itasser_model5_3bc9.png|thumb|150px|Figure 54: Superposition by PyMol with the structure predicted by iTasser (Model 5)]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
|[[Image:tm_3bc9_model1_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 1)]]
+
|[[Image:tm_3bc9_model1_2.png|thumb|150px|Figure 55: Superposition by TM-align with the structure predicted by iTasser (Model 1)]]
|[[Image:tm_3bc9_model2_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 2)]]
+
|[[Image:tm_3bc9_model2_2.png|thumb|150px|Figure 56: Superposition by TM-align with the structure predicted by iTasser (Model 2)]]
|[[Image:tm_3bc9_model3_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 3)]]
+
|[[Image:tm_3bc9_model3_2.png|thumb|150px|Figure 57: Superposition by TM-align with the structure predicted by iTasser (Model 3)]]
|[[Image:tm_3bc9_model4_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 4)]]
+
|[[Image:tm_3bc9_model4_2.png|thumb|150px|Figure 58: Superposition by TM-align with the structure predicted by iTasser (Model 4)]]
|[[Image:tm_3bc9_model5_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 5)]]
+
|[[Image:tm_3bc9_model5_2.png|thumb|150px|Figure 59: Superposition by TM-align with the structure predicted by iTasser (Model 5)]]
 
|-
 
|-
 
|}
 
|}
Line 432: Line 465:
 
'''3CUI'''
 
'''3CUI'''
   
The following table displays the RMSD and the TM-score from Pymol and TM-aling. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green).<br>
+
The following table displays the RMSD and the TM-score from PyMol and TM-aling. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 60 – Figure 69).<br>
Looking at the RMSD from Pymol, the best models are the first two ones. The other three model have a higher RMSD whereas model 5 has the worst one. This agrees with the structure alignment of pymol where the first two model display a good alignment and the other alingment become more and more worser. The last one has the fewest matchest which corresponds to the high RMSD.<br>
+
Looking at the RMSD from PyMol, the best models are the first two ones (Figure 60, Figure 61). The other three model have a higher RMSD whereas model 5 (Figure 64) has the worst one. This agrees with the structure alignment of PyMol where the first two model display a good alignment and the other alignment become more and more worse. The last one has the fewest matches which corresponds to the high RMSD.<br>
In contrast, the RMSD of TM-align has a different order. Here, the second model has the lowest RMSD followed by the first model.
+
In contrast, the RMSD of TM-align has a different order. Here, the second model (Figure 66) has the lowest RMSD followed by the first model (Figure 65).
Furthermore the fifth model is the worst one, but it is not so striking higher than the other two. This means that the three last models are almost similar.
+
Furthermore the fifth model (Figure 69) is the worst one, but it is not so striking higher than the other two. This means that the three last models are almost similar (Figure 67 – Figure 69).
The TM-score agrees more with the RMSD of TM-align which means the best score is received for model 2 followed by model 1. The other three models have a worser TM-score.
+
The TM-score agrees more with the RMSD of TM-align which means the best score is received for model 2 followed by model 1. The other three models have a worse TM-score.
The structure alignment of TM-align agrees with the TM-score and with the RMSD. For all cases the alignments seems to be only a few better than the one with PyMol and not remarkable more structure elements do agree with the original structure of hexosaminidase chain A.
+
The structure alignment of TM-align agrees with the TM-score and with the RMSD. For each cases the alignments seems to be only a few better than the one with PyMol and not remarkable more structure elements do agree with the original structure of hexosaminidase chain A.
 
Structure five is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. Furthermore it really aligns bad, which corresponds to the RMSD and the TM-score. A closer look at the other two structures of model 3 and 4 which have a bad RMSD as well show that they have only a few matches as well. <br>
 
Structure five is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. Furthermore it really aligns bad, which corresponds to the RMSD and the TM-score. A closer look at the other two structures of model 3 and 4 which have a bad RMSD as well show that they have only a few matches as well. <br>
   
Line 471: Line 504:
 
|-
 
|-
 
|Structural alignment (Pymol)
 
|Structural alignment (Pymol)
|[[Image:pymol_itasser_model1_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 1)]]
+
|[[Image:pymol_itasser_model1_3cui.png|thumb|150px|Figure 60: Superposition by PyMol with the structure predicted by iTasser (Model 1)]]
|[[Image:pymol_itasser_model2_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 2)]]
+
|[[Image:pymol_itasser_model2_3cui.png|thumb|150px|Figure 61: Superposition by PyMol with the structure predicted by iTasser (Model 2)]]
|[[Image:pymol_itasser_model3_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 3)]]
+
|[[Image:pymol_itasser_model3_3cui.png|thumb|150px|Figure 62: Superposition by PyMol with the structure predicted by iTasser (Model 3)]]
|[[Image:pymol_itasser_model4_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 4)]]
+
|[[Image:pymol_itasser_model4_3cui.png|thumb|150px|Figure 63: Superposition by PyMol with the structure predicted by iTasser (Model 4)]]
|[[Image:pymol_itasser_model5_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 5)]]
+
|[[Image:pymol_itasser_model5_3cui.png|thumb|150px|Figure 64: Superposition by PyMol with the structure predicted by iTasser (Model 5)]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
|[[Image:tm_3cui_model1_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 1)]]
+
|[[Image:tm_3cui_model1_2.png|thumb|150px|Figure 65: Superposition by TM-align with the structure predicted by iTasser (Model 1)]]
|[[Image:tm_3cui_model2_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 2)]]
+
|[[Image:tm_3cui_model2_2.png|thumb|150px|Figure 66: Superposition by TM-align with the structure predicted by iTasser (Model 2)]]
|[[Image:tm_3cui_model3_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 3)]]
+
|[[Image:tm_3cui_model3_2.png|thumb|150px|Figure 67: Superposition by TM-align with the structure predicted by iTasser (Model 3)]]
|[[Image:tm_3cui_model4_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 4)]]
+
|[[Image:tm_3cui_model4_2.png|thumb|150px|Figure 68: Superposition by TM-align with the structure predicted by iTasser (Model 4)]]
|[[Image:tm_3cui_model5_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 5)]]
+
|[[Image:tm_3cui_model5_2.png|thumb|150px|Figure 69: Superposition by TM-align with the structure predicted by iTasser (Model 5)]]
 
|-
 
|-
 
|}
 
|}
Line 489: Line 522:
 
'''3LUT'''
 
'''3LUT'''
   
The following table displays the RMSD and the TM-score from Pymol and TM-aling. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green).<br>
+
The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 70 – Figure 79).<br>
Looking at the RMSD from Pymol, the best models are the third, the second one and the first one with a really low RMSD. The other two model have a very high RMSD. This agrees with the structure alignment of pymol where the first three models align very good and many elements match with the one in the structure of hexosaminidase chain A. The other two structure alignmen are really bad which agrees with the RMSDs.<br>
+
Looking at the RMSD from PyMol, the best models are the third (Figure 72), the second one (Figure 71) and the first one (Figure 70) with a really low RMSD. The other two model (Figure 73, Figure 74) have a very high RMSD. This agrees with the structure alignment of PyMol where the first three models align very good and many elements match with the one in the structure of hexosaminidase chain A. The other two structure alignment are really bad which agrees with the RMSDs.<br>
In contrast, the RMSD of TM-align has the same order of the models. The best models are the first three and the one with the highest RMSD are the last two. Striking is that in contrast to the PyMol-RMSD the RMSD of the last two models are not so extremly high which means that the difference of highest RMSD to the lowest is not so large.
+
In contrast, the RMSD of TM-align has the same order of the models. The best models are the first three (Figure 75 – Figure 77) and the one with the highest RMSD are the last two (Figure 78, Figure 79). Striking is that in contrast to the PyMol RMSD the RMSD of the last two models are not so extremely high which means that the difference of highest RMSD to the lowest is not so large.
The TM-score agrees more with both RMSDs which means the best score is received for the first three models. The other three models have a worser TM-score whereas model 5 has a much worser TM-score than model 4.
+
The TM-score agrees more with both RMSDs which means the best score is received for the first three models. The other three models have a worse TM-score whereas model 5 has a worst TM-score than model 4.
The structure alignment of TM-align agrees with the TM-score and the RMSD. The first three models deliver really good structure alignments whereas model 4 and 5 align badly.
+
The structure alignment of TM-align agrees with the TM-score and the RMSD. The first three models deliver really good structure alignments whereas model 4 and 5 align badly. The structure of model 5 is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. A closer look at the last two structures show that this alignments are really worse and that there are almost no matches with the hexosaminidase chain A. <br>
Structure three is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. A closer look at the other two structures which have a bad RMSD as well show that they have only a few matches as well. <br>
 
   
   
Line 505: Line 537:
 
|iTasser Model 5
 
|iTasser Model 5
 
|-
 
|-
|RMSD (Pymol)
+
|RMSD (PyMol)
 
|1.956
 
|1.956
 
|1.128
 
|1.128
Line 527: Line 559:
 
|-
 
|-
 
|Structural alignment (Pymol)
 
|Structural alignment (Pymol)
|[[Image:pymol_itasser_model1_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 1)]]
+
|[[Image:pymol_itasser_model1_3lut.png|thumb|150px|Figure 70: Superposition by Pyol with the structure predicted by iTasser (Model 1)]]
|[[Image:pymol_itasser_model2_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 2)]]
+
|[[Image:pymol_itasser_model2_3lut.png|thumb|150px|Figure 71: Superposition by PyMol with the structure predicted by iTasser (Model 2)]]
|[[Image:pymol_superpose_itasser_model3_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 3)]]
+
|[[Image:pymol_superpose_itasser_model3_3lut.png|thumb|150px|Figure 72: Superposition by PyMol with the structure predicted by iTasser (Model 3)]]
|[[Image:pymol_superpose_itasser_model4_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 4)]]
+
|[[Image:pymol_superpose_itasser_model4_3lut.png|thumb|150px|Figure 73: Superposition by PyMol with the structure predicted by iTasser (Model 4)]]
|[[Image:pymol_superpose_itasser_model5_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by iTasser (Model 5)]]
+
|[[Image:pymol_superpose_itasser_model5_3lut.png|thumb|150px|Figure 74: Superposition by PyMol with the structure predicted by iTasser (Model 5)]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
|[[Image:tm_3lut_model1_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 1)]]
+
|[[Image:tm_3lut_model1_2.png|thumb|150px|Figure 75: Superposition by TM-align with the structure predicted by iTasser (Model 1)]]
|[[Image:tm_3lut_model2_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 2)]]
+
|[[Image:tm_3lut_model2_2.png|thumb|150px|Figure 76: Superposition by TM-align with the structure predicted by iTasser (Model 2)]]
|[[Image:tm_3lut_model3_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 3)]]
+
|[[Image:tm_3lut_model3_2.png|thumb|150px|Figure 77: Superposition by TM-align with the structure predicted by iTasser (Model 3)]]
|[[Image:tm_3lut_model4_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 4)]]
+
|[[Image:tm_3lut_model4_2.png|thumb|150px|Figure 78: Superposition by TM-align with the structure predicted by iTasser (Model 4)]]
|[[Image:tm_3lut_model5_2.png|thumb|150px|Superposition by TM-align with the structure predicted by iTasser (Model 5)]]
+
|[[Image:tm_3lut_model5_2.png|thumb|150px|Figure 79: Superposition by TM-align with the structure predicted by iTasser (Model 5)]]
 
|-
 
|-
 
|}
 
|}
 
<br><br>
 
<br><br>
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
  +
=== Discussion ===
   
  +
First of all, we compared the secondary structure prediction. All three predicted ones have the same number of helices and sheets. At first sight it agrees in the number with the predicted secondary structure from the last task as well. There, the number for alpha-helices was 14-16 and the number of sheets was 15. This show that the secondary structure prediction is very consistent and that it is probably good, because it agrees with the other predictions<br>
  +
The solvent accessibility gives not so much information so we decided not to got too much in detail. The predicted binding site differs in all three models in some residues. First of all, 3BC9 has 10 residues which are involved in the binding site whereas the other two have only 9 residues. The residues itself agree mostly with some exceptions. This indicates that the binding site is mostly consistent for 3BC9, 3CUI and 3LUT with some small differences.<br>
  +
The 3D structure of the 5 best predicted models varies more. This can be best seen in the calculated RMSD and the TM-Score. The best resulting structure is the first model of iTasser which receives a very small RMSD with PyMol and TM-align and a very high TM-score. Further good predictions are model 1 and 2 of 3CUI and model 3 and 4 of 3LUT. The other achieved models display worse RMSDs and TM-Scores. It is very unexpected, that 3BC9 has only one good model, because it has a high sequence identity to the original structure of hexosaminidase chain A. <br>
  +
All in all, iTasser delivers some really good predictions. One big disadvantage is that iTasser takes a lot of time for its calculation and that it only one job can be started from one IP adresse.
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
== 3D-Jigsaw ==
 
== 3D-Jigsaw ==
   
Line 549: Line 590:
 
For the 3D-Jigsaw calculation we used the [[http://bmm.cancerresearchuk.org/~populus/populus_submit.html Webserver]].
 
For the 3D-Jigsaw calculation we used the [[http://bmm.cancerresearchuk.org/~populus/populus_submit.html Webserver]].
   
For this calculation we had to create a PDB-file which contains the models which should be considered. We took for each structure the best five models. To get those five best models we decided to look mainly at the tm-score, because this score has fewer disadvanteges. For 3lut we decided to make an exception and to take not the swissmodel result which had a good tm-score. The reason is that we took 3HN3 for the swissmodel calculation and not 3LUT itself.<br>
+
For this calculation we had to create a PDB-file which contains the models which should be considered. We took for each structure the best five models. To get those five best models we decided to look mainly at the TM-score, because this score has fewer disadvantages. For 3LUT we decided to make an exception and to take not the Swissmodel results which had a good TM-score. The reason is that we took 3HN3 for the Swissmodel calculation and not 3LUT itself.<br>
This means for 3BC9 we took the iTasser model 1, 2, 4, 5 and modeller. For 3CUI we took iTasser model 1-4 and swissmodel. Contrary, for 3LUT we took all iTasser models.
+
This means for 3BC9 we took the iTasser model 1, 2, 4, 5 and the Modeller model. For 3CUI we took iTasser model 1-4 and the Swissmodel model. Contrary, for 3LUT we took all iTasser models.
  +
<br><br>
 
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
=== Results ===
 
=== Results ===
  +
  +
3D-Jigsaw delivers a wide range of results. First, it returns a secondary structure prediction. Besides, it delivers an energy plot. At last it returns 5 predicted 3D-models. Therefore, a lot of further informations were shown, like the energy and a Ramachandran plot. We decided to mainly display the predicted secondary structure and 3D-structure of the five models.
   
 
'''3BC9'''
 
'''3BC9'''
  +
  +
Figure 80 shows the predicted secondary structure. It contains 17 alpha-helices and 17 beta-sheets.
  +
  +
[[Image:jigsaw_3bc9_sec_pred.png|thumb|center| Figure 80: Predicted secondary structure for 3BC9(chain A)]]
  +
  +
The following pictures (Figure 81 – Figure 85) show the 5 models predicted by 3D-Jigsaw. These five models display the predicted 3D-structure. Here, we can see that model 3 (Figure 83) and 4 (Figure 84) are very similar. Model 2 (Figure 82) agrees mostly with this two models as well. It has only one out-standing difference: the helix at the bottom of the right side which is not existent in the other two. Model 1 (Figure 81) and 5 (Figure 85) are similar to each other, because both have this part at the bottom which the other models do not contain. One difference between this two is that model 1 seems to be more compact than model 5.
   
 
{| class="centered"
 
{| class="centered"
| [[Image:jigsaw_3bc9_model1.png|thumb|center| First predicted model for 3BC9 (chain A)]]
+
| [[Image:jigsaw_3bc9_model1.png|thumb|center| Figure 81: First predicted model for 3BC9 (chain A)]]
| [[Image:jigsaw_3bc9_model2.png |thumb| Second predicted model for 3BC9 (chain A)]]
+
| [[Image:jigsaw_3bc9_model2.png |thumb| Figure 82: Second predicted model for 3BC9 (chain A)]]
| [[Image:jigsaw_3bc9_model3.png |thumb| Third predicted model for 3BC9 (chain A)]]
+
| [[Image:jigsaw_3bc9_model3.png |thumb| Figure 83: Third predicted model for 3BC9 (chain A)]]
| [[Image:jigsaw_3bc9_model4.png |thumb| Fourth predicted model for 3BC9 (chain A)]]
+
| [[Image:jigsaw_3bc9_model4.png |thumb| Figure 84: Fourth predicted model for 3BC9 (chain A)]]
| [[Image:jigsaw_3bc9_model5.png |thumb| Fifth predicted model for 3BC9 (chain A)]]
+
| [[Image:jigsaw_3bc9_model5.png |thumb| Figure85: Fifth predicted model for 3BC9 (chain A)]]
 
|}
 
|}
   
 
'''3CUI'''
 
'''3CUI'''
  +
  +
Figure 86 shows the predicted secondary structure. It contains 17 alpha-helices and 17 beta-sheets.
  +
  +
[[Image:jigsaw_3cui_sec_pred.png|thumb|center| Figure 86: Predicted secondary structure for 3CUI(chain A)]]
  +
  +
The following pictures show the 5 models (Figure 87 – Figure 91) predicted by 3D-Jigsaw. This five model display the predicted 3D-structure. Here, we can see that model 1 (Figure 87) and 2 (Figure 88) are very similar. Furthermore, the other three models (Figure 89 – Figure 91) agree with each other. Model 1 and 2 have more beta-sheets and one end is on the right side. Contrary, model 3, 4 and 5 have only a few beta-sheets and their one end is to the top. Besides, the helices seems to be very consistent in all models.
   
 
{| class="centered"
 
{| class="centered"
| [[Image:jigsaw_3cui_model1.png|thumb|center| First predicted model for 3CUI (chain A)]]
+
| [[Image:jigsaw_3cui_model1.png|thumb|center| Figure 87: First predicted model for 3CUI (chain A)]]
| [[Image:jigsaw_3cui_model2.png |thumb| Second predicted model for 3CUI (chain A)]]
+
| [[Image:jigsaw_3cui_model2.png |thumb| Figure 88: Second predicted model for 3CUI (chain A)]]
| [[Image:jigsaw_3cui_model3.png |thumb| Third predicted model for 3CUI (chain A)]]
+
| [[Image:jigsaw_3cui_model3.png |thumb| Figure 89: Third predicted model for 3CUI (chain A)]]
| [[Image:jigsaw_3cui_model4.png |thumb| Fourth predicted model for 3CUI (chain A)]]
+
| [[Image:jigsaw_3cui_model4.png |thumb| Figure 90: Fourth predicted model for 3CUI (chain A)]]
| [[Image:jigsaw_3cui_model5.png |thumb| Fifth predicted model for 3CUI (chain A)]]
+
| [[Image:jigsaw_3cui_model5.png |thumb| Figure 91: Fifth predicted model for 3CUI (chain A)]]
 
|}
 
|}
   
 
'''3LUT'''
 
'''3LUT'''
  +
  +
Figure 92 shows the predicted secondary structure. It contains 17 alpha-helices and 17 beta-sheets.
  +
  +
[[Image:jigsaw_3lut_sec_pred.png|thumb|center| Figure 92: Predicted secondary structure for 3LUT(chain A)]]
  +
  +
The following pictures show the 5 models (Figure 93 – Figure 97) predicted by 3D-Jigsaw. This five model display the predicted 3D-structure. Here, we can see that all models are very similar. The only small difference that can be seen is that model 5 (Figure 97) seems to be wider than the others.
   
 
{| class="centered"
 
{| class="centered"
| [[Image:jigsaw_3lut_model1.png|thumb|center| First predicted model for 3LUT (chain A)]]
+
| [[Image:jigsaw_3lut_model1.png|thumb|center| Figure 93: First predicted model for 3LUT (chain A)]]
| [[Image:jigsaw_3lut_model2.png |thumb| Second predicted model for 3LUT (chain A)]]
+
| [[Image:jigsaw_3lut_model2.png |thumb| Figure 94: Second predicted model for 3LUT (chain A)]]
| [[Image:jigsaw_3lut_model3.png |thumb| Third predicted model for 3LUT (chain A)]]
+
| [[Image:jigsaw_3lut_model3.png |thumb| Figure 95 Third predicted model for 3LUT (chain A)]]
| [[Image:jigsaw_3lut_model4.png |thumb| Fourth predicted model for 3LUT (chain A)]]
+
| [[Image:jigsaw_3lut_model4.png |thumb| Figure 96: Fourth predicted model for 3LUT (chain A)]]
| [[Image:jigsaw_3lut_model5.png |thumb| Fifth predicted model for 3LUT (chain A)]]
+
| [[Image:jigsaw_3lut_model5.png |thumb| Figure 97: Fifth predicted model for 3LUT (chain A)]]
 
|}
 
|}
  +
<br><br>
 
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
=== RMSD and TM-Score ===
 
=== RMSD and TM-Score ===
   
The next step after the use of Swissmodel is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins don't receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal.
+
The next step after the use of Jigsaw is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal.
We used the RMSD calculation by Pymol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to caluclate the RMSD.
+
We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD.
So first of all, it is important to clarify how these two methods calculate the RMSD. Pymol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results.
+
So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results.
   
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different.
 
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different.
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
 
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.
  +
   
 
'''3BC9'''
 
'''3BC9'''
  +
  +
The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore, it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 98 - Figure 107).<br>
  +
Looking at the RMSD from PyMol, the best model is the first one. The other models have a very similar RMSD which is not extremely higher. This agrees with the structure alignment of PyMol where the first model (Figure 98) seems to align best. Structure alignments of model 3 (Figure 100) and 4 (Figure 101) are similar to the second one (Figure 99). Only model 1 and 5 have different alignments, because both structures have some helices at the bottom.<br>
  +
In contrast, the RMSD of TM-align has a different order. Here, the first (Figure 102) and the fifth (Figure 107) models have the lowest RMSD. The other models (Figure 103 – Figure 106) have a very similar RMSD which is not extremely higher.
  +
The TM-score is different to the RMSD. The TM-scores of all models are almost the same and is not that good.
  +
The structure alignment of TM-align agrees with the TM-score and nearly with the RMSD. The structure alignments of all models show no high difference in the matching parts. Main differences exist like in the PyMol alignment between model 1 and 5 and other models. Models 1 and 5 have part of the structure at the bottom whereas models 2, 3 and 4 have the part at the top.
  +
<br>
   
 
{| border="1" style="text-align:center; border-spacing:0;"
 
{| border="1" style="text-align:center; border-spacing:0;"
Line 603: Line 674:
 
|3D-Jigsaw Model 5
 
|3D-Jigsaw Model 5
 
|-
 
|-
|RMSD (Pymol)
+
|RMSD (PyMol)
 
|2.773
 
|2.773
 
|1.790
 
|1.790
Line 625: Line 696:
 
|-
 
|-
 
|Structural alignment (Pymol)
 
|Structural alignment (Pymol)
|[[Image:pymol_jigsaw_model1_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 1)]]
+
|[[Image:pymol_jigsaw_model1_3bc9.png|thumb|150px|Figure 98: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 1)]]
|[[Image:pymol_jigsaw_model2_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 2)]]
+
|[[Image:pymol_jigsaw_model2_3bc9.png|thumb|150px|Figure 99: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 2)]]
|[[Image:pymol_jigsaw_model3_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 3)]]
+
|[[Image:pymol_jigsaw_model3_3bc9.png|thumb|150px|Figure 100: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 3)]]
|[[Image:pymol_jigsaw_model4_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 4)]]
+
|[[Image:pymol_jigsaw_model4_3bc9.png|thumb|150px|Figure 101: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 4)]]
|[[Image:pymol_jigsaw_model5_3bc9.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 5)]]
+
|[[Image:pymol_jigsaw_model5_3bc9.png|thumb|150px|Figure 102: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 5)]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
|[[Image:tm_3bc9_jigsaw_model1.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)]]
+
|[[Image:tm_3bc9_jigsaw_model1.png|thumb|150px|Figure 103: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)]]
|[[Image:tm_3bc9_jigsaw_model2.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)]]
+
|[[Image:tm_3bc9_jigsaw_model2.png|thumb|150px|Figure 104: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)]]
|[[Image:tm_3bc9_jigsaw_model3.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)]]
+
|[[Image:tm_3bc9_jigsaw_model3.png|thumb|150px|Figure 105: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)]]
|[[Image:tm_3bc9_jigsaw_model4.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)]]
+
|[[Image:tm_3bc9_jigsaw_model4.png|thumb|150px|Figure 106: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)]]
|[[Image:tm_3bc9_jigsaw_model5.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)]]
+
|[[Image:tm_3bc9_jigsaw_model5.png|thumb|150px|Figure 107: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)]]
 
|-
 
|-
 
|}
 
|}
Line 642: Line 713:
   
 
'''3CUI'''
 
'''3CUI'''
  +
  +
The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 108 – Figure 117).<br>
  +
Looking at the RMSD from PyMol, the best models are model 3-5 (Figure 110 – Figure 112) which have all a similar RMSD. The other models (Figure 108, Figure 109) have a bit higher RMSD. This agrees with the structure alignment of PyMol where all models are almost equal. The last three models have their one end at the top and the other have this end at the right side. Furthermore, a closer look at the last three models shows that these ones align better in some parts of the structure which explains the different RMSD.<br>
  +
The RMSD of TM-align has a same result. Here, the last three models (Figure 115 – Figure 117) have the lowest RMSD as well. The other models (Figure 113, Figure 114) have a very similar RMSD which is not extremely higher.
  +
Furthermore, TM-score is delivers a similar result. The TM-scores of the models 3-5 are highest whereas the TM-score of model 1 and 2 are little lower.
  +
The structure alignment of TM-align agrees with the TM-score and the RMSD as well as with the structure alignment of PyMol. It has therefore the same explanations.
  +
<br>
   
 
{| border="1" style="text-align:center; border-spacing:0;"
 
{| border="1" style="text-align:center; border-spacing:0;"
Line 651: Line 729:
 
|3D-Jigsaw Model 5
 
|3D-Jigsaw Model 5
 
|-
 
|-
|RMSD (Pymol)
+
|RMSD (PyMol)
 
|3.575
 
|3.575
 
|3.628
 
|3.628
Line 673: Line 751:
 
|-
 
|-
 
|Structural alignment (Pymol)
 
|Structural alignment (Pymol)
|[[Image:pymol_jigsaw_model1_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 1)]]
+
|[[Image:pymol_jigsaw_model1_3cui.png|thumb|150px|Figure 108: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 1)]]
|[[Image:pymol_jigsaw_model2_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 2)]]
+
|[[Image:pymol_jigsaw_model2_3cui.png|thumb|150px|Figure 109: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 2)]]
|[[Image:pymol_jigsaw_model3_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 3)]]
+
|[[Image:pymol_jigsaw_model3_3cui.png|thumb|150px|Figure 110: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 3)]]
|[[Image:pymol_jigsaw_model4_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 4)]]
+
|[[Image:pymol_jigsaw_model4_3cui.png|thumb|150px|Figure 111: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 4)]]
|[[Image:pymol_jigsaw_model5_3cui.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 5)]]
+
|[[Image:pymol_jigsaw_model5_3cui.png|thumb|150px|Figure 112: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 5)]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
|[[Image:tm_3cui_jigsaw_model1.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)]]
+
|[[Image:tm_3cui_jigsaw_model1.png|thumb|150px|Figure 113: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)]]
|[[Image:tm_3cui_jigsaw_model2_2.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)]]
+
|[[Image:tm_3cui_jigsaw_model2_2.png|thumb|150px|Figure 114: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)]]
|[[Image:tm_3cui_jigsaw_model3.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)]]
+
|[[Image:tm_3cui_jigsaw_model3.png|thumb|150px|Figure 115: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)]]
|[[Image:tm_3cui_jigsaw_model4_2.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)]]
+
|[[Image:tm_3cui_jigsaw_model4_2.png|thumb|150px|Figure 116: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)]]
|[[Image:tm_3cui_jigsaw_model5_2.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)]]
+
|[[Image:tm_3cui_jigsaw_model5_2.png|thumb|150px|Figure 117: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)]]
 
|-
 
|-
 
|}
 
|}
Line 690: Line 768:
   
 
'''3LUT'''
 
'''3LUT'''
  +
  +
The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 118 – Figure 127).<br>
  +
Looking at the RMSD from PyMol, the best model is the fourth one (Figure 121). The other models (Figure 118 – Figure 120, Figure 122) have a very similar RMSD which is really low as well. This agrees with the structure alignment of PyMol where all models seems to align very good and look very similar. The only small difference can be seen in the alpha-helix at the right top which has in each model another position which do not differ a lot. The rest of the alignments seems to be very consistent.
  +
<br>
  +
In contrast, the RMSD of TM-align has a different order. Here, the first two models (Figure 123, Figure 124) have the lowest RMSD. The other models (Figure 125 – Figure 127) have a very similar RMSD which is not extremely higher.
  +
The TM-score is different to the RMSD. The TM-scores of all models are almost the same and is relatively high.
  +
The structure alignment of TM-align agrees with the TM-score and the RMSD as well as with the structure alignment of PyMol. It has therefore the same explanations.
  +
<br>
   
 
{| border="1" style="text-align:center; border-spacing:0;"
 
{| border="1" style="text-align:center; border-spacing:0;"
Line 699: Line 785:
 
|3D-Jigsaw Model 5
 
|3D-Jigsaw Model 5
 
|-
 
|-
|RMSD (Pymol)
+
|RMSD (PyMol)
 
|1.813
 
|1.813
 
|1.813
 
|1.813
Line 721: Line 807:
 
|-
 
|-
 
|Structural alignment (Pymol)
 
|Structural alignment (Pymol)
|[[Image:pymol_jigsaw_model1_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 1)]]
+
|[[Image:pymol_jigsaw_model1_3lut.png|thumb|150px|Figure 118: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 1)]]
|[[Image:pymol_jigsaw_model2_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 2)]]
+
|[[Image:pymol_jigsaw_model2_3lut.png|thumb|150px|Figure 119: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 2)]]
|[[Image:pymol_jigsaw_model3_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 3)]]
+
|[[Image:pymol_jigsaw_model3_3lut.png|thumb|150px|Figure 120: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 3)]]
|[[Image:pymol_jigsaw_model4_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 4)]]
+
|[[Image:pymol_jigsaw_model4_3lut.png|thumb|150px|Figure 121: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 4)]]
|[[Image:pymol_jigsaw_model5_3lut.png|thumb|150px|Superposition by Pymol with the structure predicted by 3D-Jigsaw (Model 5)]]
+
|[[Image:pymol_jigsaw_model5_3lut.png|thumb|150px|Figure 122: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 5)]]
 
|-
 
|-
 
|Structural alignment (TM-align)
 
|Structural alignment (TM-align)
|[[Image:tm_3lut_jigsaw_model1.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)]]
+
|[[Image:tm_3lut_jigsaw_model1.png|thumb|150px|Figure 123: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)]]
|[[Image:tm_3lut_jigsaw_model2.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)]]
+
|[[Image:tm_3lut_jigsaw_model2.png|thumb|150px|Figure 124: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)]]
|[[Image:tm_3lut_jigsaw_model3.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)]]
+
|[[Image:tm_3lut_jigsaw_model3.png|thumb|150px|Figure 125: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)]]
|[[Image:tm_3lut_jigsaw_model4.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)]]
+
|[[Image:tm_3lut_jigsaw_model4.png|thumb|150px|Figure 126: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)]]
|[[Image:tm_3lut_jigsaw_model5.png|thumb|150px|Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)]]
+
|[[Image:tm_3lut_jigsaw_model5.png|thumb|150px|Figure 127: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)]]
 
|-
 
|-
 
|}
 
|}
 
 
<br><br>
 
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
   
  +
=== Discussion ===
  +
  +
First of all we look at the secondary structure prediction. Here, we can see that we get every time 17 helices and 17 sheets. Regarding the last task, this agrees with the other predictions, where the number of alpha-helices was 14-16 and the number of sheets was 15. This shows that the secondary structure prediction is very consistent and probably good.<br>
  +
Afterwards we regarded the five predicted models. This were widely similar to each other. Only some small variations exist between them. The most consistent models are returned for 3LUT and the most different models for 3CUI where the number of beta-sheets differs a lot. For 3BC9 the shape differs more for one certain part of structure as well. Comparing the 5 models of 3BC9, 3CUI and 3LUT we can see that they are also very similar. <br>
  +
The RMSD and the TM-score agrees with the above described characteristics. Both RMSDs and the TM-Score is mostly similar. Outstanding is that the TM-scores for the 3BC9 models are the worst ones. The TM-scores of 3CUI and 3LUT is very consistent at a high level. In contrast, the best RMSD-score with TM-align is achieved with the first model for 3BC9. The worst ones are received in model 1 and 2 for 3CUI, but it is not extremely higher than the others. The best RMSD calculated by PyMol have the models 3 and 5 for 3LUT. The worst ones have all models except model 2 for 3BC9 and the first two models for 3CUI. <br>
  +
The structure alignments of PyMol an TM-align are mostly very good looking. This means that many structure elements of the predicted models agree with the original structure of hexosaminidase chain A. This corresponds to the TM-score and the two RMSD which are very consistent in all models as well. <br>
  +
All in all, the predicted structures are very good and agree very well with the original structure. <br>
  +
<br><br>
  +
Back to [[http://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php/Tay-Sachs_Disease Tay-Sachs Disease]]<br><br>
 
== Summary and Discussion ==
 
== Summary and Discussion ==
   
The first we can see on the tables above is, that the RMSD score calculated by Pymol is always much higher than the RMSD score calulcated by TM-align. Therefore, it is more effective to rotate the structure to each other, than to use sequence and structure alignment. This can be seen by looking at the RMSD score, but also by looking at the pictures, which show the superposed structures.
+
The first we can see on the tables above is, that the RMSD score calculated by PyMol is always much higher than the RMSD score calculated by TM-align. Therefore, it is more effective to rotate the structure to each other, than to use sequence and structure alignment. This can be seen by looking at the RMSD score, but also by looking at the pictures, which show the superposed structures.
Furthermore, Modeller and Swissmodel both predict the structure bad. Both methods always have a very high RMSD and a very low TM-Score. To learn more about the prediction results, we analysed the scores for each template.
+
Furthermore, Modeller and Swissmodel both predict the structure mostly worse than iTasser. Both methods always have a very high RMSD and a very low TM-Score. To learn more about the prediction results, we analysed the scores for each template.
 
<br><br>
 
<br><br>
   
 
* 3BC9:
 
* 3BC9:
3BC9 is the template with the highest sequence identity. Therefore, the predicted results should be very similar to our structure. Unfortunately Swissmodel could not return a result, because the method was not able to align target and template sequence. This is very surprisingly, because an alignment between two very identical sequences should be easy to do. Even if we used the alignment mode in swissmodel, it was not able to return a prediction. The prediction of Modeller is really bad and also iTasser predicted wrong structures. Only model 1 of iTasser is very similar to the real structure, which can also be seen in the RMSD (near to 0) and the TM-Score (near to 1).
+
3BC9 is the template with the highest sequence identity. Therefore, the predicted results should be very similar to our structure. Unfortunately Swissmodel could not return a result, because the method was not able to align target and template sequence. This is very surprisingly, because an alignment between two very identical sequences should be easy to do. Even if we used the alignment mode in Swissmodel, it was not able to return a prediction. The prediction of Modeller is really bad and also iTasser predicted wrong structures. Only model 1 of iTasser is very similar to the real structure, which can also be seen in the RMSD (near to 0) and the TM-Score (near to 1).
 
<br>
 
<br>
The best result with 3BC9 as target was the iTasser model1 prediction.
+
The best result with 3BC9 as target was the iTasser model 1 prediction (Figure 29).
 
<br><br>
 
<br><br>
 
* 3CUI:
 
* 3CUI:
3CUI has a sequence identity of 49.5%, which is not that much, but it should be possible to predict a structure which is almost similar to the real structure. As before, Swissmodeller and Modeller predict structures which fit not very well to our real structure. But iTasser predicted two models, which are very similar to our structure. Model1 and Model4 have very low RMSD values, high TM-Scores and with a look to the pictures it is clear, that target and template structure are really similar.
+
3CUI has a sequence identity of 49.5%, which is not that much, but it should be possible to predict a structure which is almost similar to the real structure. As before, Swissmodell and Modeller predict structures which fit not very well to our real structure. But iTasser predicted two models, which are very similar to our structure. Model 1 and model 2 have very low RMSD values, high TM-Scores and with a look to the pictures it is clear, that target and template structure are really similar.
 
<br>
 
<br>
So again, in this case we got the best result from iTasser.
+
So again, in this case we got the best result from iTasser. (Figure 35, Figure 36)
 
<br><br>
 
<br><br>
 
* 3LUT /3HN3:
 
* 3LUT /3HN3:
 
Swissmodel was not able to predict the structure of our target with 3LUT as template. Therefore, we used 3HN3, which has with 25% a bit more sequence identity than 3LUT (20%).
 
Swissmodel was not able to predict the structure of our target with 3LUT as template. Therefore, we used 3HN3, which has with 25% a bit more sequence identity than 3LUT (20%).
We suggest, that this prediction result is the worst result, because of this low sequence identity. Interesstingly, the prediction results of Modeller and Swissmodel are not much worse than their result with 3CUI as template. Furthermore, iTasser predicted two models, which fit very well to our real structure and also has very low RMSD scores and high TM-Scores. <br>
+
We suggest, that this prediction result is the worst result, because of this low sequence identity. Interestingly, the prediction results of Modeller and Swissmodel are not much worse than their result with 3CUI as template. Furthermore, iTasser predicted three models, which fit very well to our real structure and also has very low RMSD scores and high TM-Scores. <br>
 
We want to highlight, that this result is not the norm. We aligned the structure of 2GJX:A and 3LUT:A and the TM-Score between these two structures is 0.50014, the RMSD 5.04, which is a very good result regarding that the sequence identity is that low. So in this case we were lucky to get such a good result, but in general, the results by predicting two that much distinct sequences is much worse.
 
We want to highlight, that this result is not the norm. We aligned the structure of 2GJX:A and 3LUT:A and the TM-Score between these two structures is 0.50014, the RMSD 5.04, which is a very good result regarding that the sequence identity is that low. So in this case we were lucky to get such a good result, but in general, the results by predicting two that much distinct sequences is much worse.
 
<br>
 
<br>
In agreement with the two results from above, iTasser again gave the best results.
+
In agreement with the two results from above, iTasser again received the best results (Figure 41 – Figure 44).
 
<br><br>
 
<br><br>
In sum, iTasser is the best prediction method from the three used methods. But iTasser also needs a lot of time to predict the sequences and also allows only one sequence per user to predict in the same time. Therefore, if there is enough time, iTasser is the best choice. If there is not that much time, Modeller and Swissmodel can be used. Both methods have approximalty the same prediction results. Modeller can only run on the command line, which means Modeller have to be installed on the system. If the user just want to install Modeller, it will take a while, because Modeller sends a licence per E-Mail which can take up to one day. Swissmodel is available on the Internet and can be used without any delay. So if the user only want to get an approximat estimation of the structure of the protein and do not have that much time, Swissmodel will be the right choice.
+
In sum, iTasser is the best prediction method from the three used methods. But iTasser also needs a lot of time to predict the sequences and also allows only one sequence per user to predict in the same time. Therefore, if there is enough time, iTasser is the best choice. If there is not that much time, Modeller and Swissmodel can be used. Both methods have approximately the same prediction results. Modeller can only run on the command line, which means Modeller have to be installed on the system. If the user just want to install Modeller, it will take a while, because Modeller sends a license per E-Mail which can take up to one day. Swissmodel is available on the Internet and can be used without any delay. So if the user only want to get an approximate estimation of the structure of the protein and do not have that much time, Swissmodel will be the right choice.
   
  +
Furthermore, we used 3D-Jigsaw to get a final model, which is build off the five best models for 3BC9, 3CUI and 3LUT. For all these, the predicted structures are very good, with a small RMSD and a high TM-Score. Besides the certain results of 3D-Jigsaw agree widely and show no big differences. Therefore, 3D-Jigsaw is a good tool to get recombined optimized models. One disadvantage is that it took about 1 1/2 days to calculate these models.
Furthermore, we used Jigsaw to get a final model, which is build with five different models. In case of 3BC9, the Jigsaw result is very good, with a RMSD score less than 1 and a TM-Score near by 1. We used Jigsaw with the first four iTasser predictions and the prediction of Modeller. We could not use the Swissmodel prediction, because Swissmodel did not work. So, we have one very good model, three moderate models and only one bad model. This could explain, why the result of Jigsaw is that good. In the case of 3CUI is the result very bad, and this result is worse than the results of iTasser. In this case we used the models of Swissmodel, Modeller and the first three iTasser models. The prediction results of Swissmodel and Modeller are very bad and the iTasser results are also not that good. Therefore, it seems that Jigsaw can not compensate four very bad models, although if one very good model is available. In our last case with 3LUT the result is very good with again an RMSD less than 1 and a TM-Score near by 1. Here again the models of Swissmodel and Modeller are very bad, but the three iTasser results are quit good and therefore it seems, that Jigsaw can compensate two very bad models, if three models are very good and similar.
 
  +
<br><br>
In sum, Jigsaw can improve the prediction results if the models which are the basic for the Jigsaw prediction are quite good. Otherwise, the prediction of Jigsaw is worse than the predictions of the single methods.
 
  +
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Latest revision as of 21:33, 31 August 2011

Homology structure groups

We chose one protein from each sequence identity group which is shown in the following table. These proteins were used for almost every homology based applications which were described below.

(The complete HHsearch output can be found [here ])

> 60% sequence identity
PDB id name similarity
3bc9_A AMYB, alpha amylase 80.8%
> 40%
3cui_A EXO-beta-1,4-glucanase; 49.5%
< 40%
3hn3_A Beta-G1, beta-glucuroni 25.1%
3lut_A Voltage-gated potassium 20.1%



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Swissmodel

Calculation

To calculate the models with Swissmodel we used the [Webserver]. For the template with high sequence identity, we used the automated and the alignment method, for the other two templates we only used the alignment method.

The used alignments can be found [here].

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Results

3BC9:

Sadly, Swissmodel was not able to align our template 3BC9 with our target sequence. Even when we tried to get a prediction with other templates with high sequence identity (1L8N, 1ZJA), Swissmodel was not able to align these two sequences. Therefore, we do not have a prediction result for high identical template and target.

3CUI:

The detailed prediction can be found [here]

Swissmodel returns some scores to give the user the possibility to estimate the quality of the predicted model. These scores are shown in the next table. The most important score is the QMEAN4 score, because this score consists of the other scores above and gives the user the possibility to compare the different results.


Global Score
Scoring function term Raw score Z score
C_beta interaction energy 202.24 -4.65
All-atom pairwise energy 9942.28 -6.16
Solvation energy 67.79 -8.08
Torsion angle energy 76.36 -7.72
QMEAN4 score 0.057 -11.76


Furthermore, Swissmodeler returns two different structure predictions, one of the HEXA-HUMAN with 3CUI as template structure and one of the wrong predicted residue. These two predictions are displayed in Figure 1 and Figure 2:

Figure 1: Prediction the structure of HEXA_HUMAN with 3cui as template structure
Figure 2: Prediction of the wrong predicted residues (wrong predicted residues are colored in red)

On Figure 2, we can see that most of the residues are colored in red and therefore, we know that the result of our prediction is really bad.

Besides, Swissmodel creates some pictures, which show the quality of the model, as well. This ones were shown in the figure 3, figure 4, figure 5 and figure 6:

Figure 3: Visualization of the QMEAN Z-Score for this model
Figure 4: Visualization of the QMEAN score in comparison with a gaussian distribution
Figure 5: Quality of the model in comparison to a X-ray structure
Figure 6: Plot, which shows the wrong predicted residues of this model

As we can see on the figure 3, our model is beyond the curve of the different Z-scores, so our model is not very good. If we have a look at the figure 4, we can see, that the Q-means score of the model is left of the gaussian curve, which also shows that this model is not very good. Next it is possible to compare the quality of our model with X-ray structure models. Normally, X-ray structure models have a Z-score about 0. In our case, shown at Figure 5 all scores are significant less than 0, so again this picture shows us, that the model is bad. The best part of our model is the C-beta interactions, which have a score of -4.65, which is far away from 0. The other scores are lower than the C-beta interactions score, so therefore, they are worse. We can therefore suggest, that the model is quite bad. The last figure, figure 6, is a plot, which shows the wrong predicted residues. Most of the residues have a prediction error more than 10, which is extremely high.
So in general, we can see that our model does not have a very good quality. That should be kept in mind by analysing the prediction result, because it is nearly impossible to get a good prediction with a bad template.


3LUT:

We decided to model the 3D structure with the template structure which has a very low sequence identity. Therefore, we decided to model the structure with 3LUT. Sadly, Swissmodel could not model the structure with this template, because it was not able to create an alignment which can be used as basic for the model. Because of this, we decided to calculate a model with 3HN3 as template, which was the next template with a little bit more sequence identity. Therefore, we present here the results of the modeling with 3HN3, whereas by the other methods, we used 3LUT.


3HN3:

The detailed prediction can be found [here]

Swissmodel returns some scores to give the user the possibility to estimate the quality of the predicted model. This scores are shown in the next table. The most important score is the QMEAN4 score, because this score consists of the other scores above and gives the user the possibility to compare the different results.

Global Score
Scoring function term Raw score Z score
C_beta interaction energy 120.70 -5.31
All-atom pairwise energy 2585.98 -5.22
Solvation energy 71.87 -9.92
Torsion angle energy 80.43 -8.44
QMEAN4 score 0.010 -12.80

Furthermore, Swissmodeler returns two different structure predictions, one of the HEXA-HUMAN with 3hn3 as template structure and one of the wrong predicted residue. This two predictions are displayed in Figure 7 and Figure 8.

Figure 7: Prediction the structure of HEXA_HUMAN with 3hn3 as template structure
Figure 8: Prediction of the wrong predicted residues (wrong predicted residues are colored in red

On Figure 8, we can see that there is a hugh amount of wrong predicted residues so therefore, our prediction result is really bad.

Besides, Swissmodel creates some pictures, which show the quality of the model, as well. This ones were shown in Figure 9, Figure 10, Figure 11 and Figure 12.

Figure 9: Visualization of the QMEAN Z-Score for this model
Figure 10: Visualization of the QMEAN score in comparison with a gaussian distribution
Figure 11: Quality of the model in comparison to a X-ray structure
Figure 12: Plot, which shows the wrong predicted residues of this model

If we have a look at the Figure 9, Figure 10 and Figure 11, we can see that the model is worse than the model of 3cui. This was expected, because the sequence identity between our template and target is very low. The Figure 9 shows us, that the model is really bad. The point has nearly the same y-value as the 3cui model, but the protein is shorter and therefore, normally shorter models have a higher QMEAN score, which is not the case here. The QMEAN score is a little bit lower so therefore this model is worse than the model of 3cui. The same is possible to see in Figure 10, where we can see that the Z-score of our model is left of the gaussian distribution. Figure 11 shows us how the quality of our model is compared to X-ray structures and again it is possible to see, that the quality is quite bad, worse than the model of 3cui. Interestingly, the prediction error of the different residues, shown in Figure 12, is not that high as we seen before by looking at the plot of 3cui. So it seems, that more residues could be predicted in the right way, although the model is very bad.

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RMSD and TM-Score

The next step after the use of Swissmodel is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there is only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal.
We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD values were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD.
So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results.
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different.
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.

The following table displays the TM-score and the RMSD for the Swissmodel results. Here we can compare only the results of 2CUI and 3HN3, because we do not get a result for 3BC9. Looking at the RMSD calculated by PyMol we can see that both are really high. This agrees with the structure alignment calculated by PyMol. The most of the structure elements do not match with the one from hexosaminidase chain A. In contrast, the RMSD of TM-align is much lower, but not the best as well. The TM-score is low which means it is not good as well. This is also shown in the structure alignment of TM-align. Here in the most cases the structures do not fit good (compare Figure 13 – Figure 16).

3BC9 3CUI 3HN3
RMSD (PyMol) no result 24.447 27.968
RMSD (TM-align) no result 5.49 4.30
TM Score (TM-align) no result 0.45333 0.40661
Structural alignment (PyMol) no result
Figure 13: Superposition by PyMol with the structure predicted by Swissmodel
Figure 14: Superposition by PyMol with the structure predicted by Swissmodel
Structural alignment (TM-align) no result
Figure 15: Superposition by TM-align with the structure predicted by Swissmodel
Figure 16: Superposition by TM-align with the structure predicted by Swissmodel



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Discussion

In this section we discuss the results of 3CUI and 3HN3, because 3BC9 and 3LUT did not work with Swissmodel. First of all, we look at the results returned by Swissmodel itself. Here, we can see that for both structure the predicted model is really bad. The different scores and the plots created by Swissmodel display the quality of the predicted structure. All these plots confirm that both predicted models were not good. Furthermore, the different RMSD and the TM-score indicate the same. Like expected the result for 3HN3 is even worser than for 3CUI. This can be explained by the fact that 3HN3 has a smaller sequence identity to hexosaminidase A than 3CUI. It is really sad, that there is no result for 3BC9 because it would be really interesting if this one would receive the best prediction. This would be expected because it has the highest sequence identity to hexosaminidase chain A. All in all, these two predictions are not very good and have a bad quality which is confirmed by the received scores and plots. This and the fact that Swissmodel was not possible to calculate a prediction for 3BC9 display that Swissmodel is not the best homology modeling tool.

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Modeller

Calculation

We used Modeller from the command line. Therefore we followed the instructions, described [here].

First of all, we created an alignment for each of our three selected sequences. In the next step we used Modeller to model the 3D structure of the protein.

For Modeller we used the Pir Alignment format, which can be found here: [3BC9], [3CUI], [3LUT]

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Results

Modeller calculated one model for each structure (3BC9, 3CUI and 3LUT) which can be seen in the Figure 17, Figure 18 and Figure 19:

3BC9 3CUI 3LUT
Figure 17: 3D structure of HEXA_HUMAN with 3BC9 as template predicted by Modeller.
Figure 18: 3D structure of HEXA_HUMAN with 3CUI as template predicted by Modeller.
Figure 19: 3D structure of HEXA_HUMAN with 3LUT as template predicted by Modeller.



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RMSD and TM-Score

The next step after the use of Modeller is to check the quality of the predicted structure. Therefore, we calculated the RMSD score and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal.
We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD.
So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results.
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different.
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.

The following table displays the two different RMSD scores and the TM-score as well as the structure alignment for 3BC9 (Figure 20, Figure 23), 3CUI (Figure 21, Figure 24) and 3LUT (Figure 22, Figure 25). Looking at the RMSD of PyMol we can see that all RMSDs are very high. For 3BC9 we receive the highest one and for 3CUI the lowest. This agrees with the structure alignment with PyMol where all different structures match badly with the hexosaminidase chain A. The RMSDs of TM-align are all lower than the one with PyMol. Here, the scores are more equal, but the order is the same. 3BC9 delivers the worst RMSD and 3CUI the best one. The TM-score delivers the same result which means it is relatively bad and the order is the same. This can be seen in the structure alignment of TM-align as well. In all three cases the structures match very badly.

3BC9 3CUI 3LUT
RMSD (Pymol) 26.271 23.856 24.153
RMSD (TM-align) 5.94 5.46 5.29
TM Score (TM-align) 0.43072 0.44048 0.38126
Structural alignment (Pymol)
Figure 20: Superposition by PyMol with the structure predicted by Modeller
Figure 21: Superposition by PyMol with the structure predicted by Modeller
Figure 22: Superposition by PyMol with the structure predicted by Modeller
Structural alignment (TM-align)
Figure 23: Superposition by TM-align with the structure predicted by Modeller
Figure 24: Superposition by TM-align with the structure predicted by Modeller
Figure 25: Superposition by TM-align with the structure predicted by Modeller



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Discussion

Modeller delivers only the predicted 3D-structures. There are no additional scores or plots which assign the quality of the prediction. So the only possible way to distinguish the quality is in our case the different RMSD and the TM-score. Here, we can see that the RMSDs for all different models are very high. This indicates that the structure alignment is very bad and that there is high divergence between the original structure of hexosaminidase A and the predicted one. Very strange is the order of RMSDs and the TM-score. The resulting best prediction is the one with 3CUI, which has not the highest sequence identity. We would expect that the best result should be returned for 3BC9, because of the high sequence identity. 3LUT delivers the worst predictions which agrees with the fact that it hast the lowest identity with hexosaminidase chain A.
All in all, all three predicted structures are not very good and match badly with the original one.

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iTasser

Calculation of the models

To calculate our models with iTasser we used the [Webserver].

For the calculation we had to define the target and template sequence. Therefore, the target sequence had to be pasted into a frame whereas the template was specified by the PDB-id. Furthermore, we exclude our own sequence and very similar as a template from the iTasser library. Therefore, we first define a cutoff of 80%. Afterwards we also created a further input-file which contains our own sequence and the similar ones. This should prevent that this sequences were used. To get the similar sequence we used 3D-Blast. In our case we did not found any other sequence with a similar structure at a high score. This means that our input file only contained our own structure.

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Results

iTasser delivers a wide range of result with many predicted informations. The first ones are the predicted secondary structure and the predicted solvent accessibility. Furthermore it provides the first top 5 predicted models, the predicted function, predicted GO terms and the predicted binding site. The predicted secondary structure elements are shown as H for alpha helix (red), S for beta sheet (blue) and C for coil (yellow). The predicted solvent accessibility has values range from 0 (buried residue) to 9 (highly exposed residue) which describe the solvent accessibility. The predicted function are the predicted EC numbers which are the TM-score, the RMSD score and so on. The predicted GO terms are the molecular function, biological process or the cellular location. There are many different predicted GO terms for each protein.


3BC9:

The following three Figures show the predicted secondary structure (Figure 26), the predicted solvent accessibility (Figure 27) and the predicted binding site (Figure 28) of 3BC9.
The predicted secondary structure contains 17 alpha-helices and 18 beta-sheets.
The predicted solvent accessibility is displayed by solvent accessibility value that ranges from 0 (buried residue) to 9 (highly exposed residue).
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atom, red for O-atoms and blue for N-atoms. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3BC9 10 amino acids were predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 2 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid, 1 Lysin and 1 Tyrosine.

Figure 26: Predicted secondary structure of 3BC9(chain A)
Figure 27: Predicted solvent accessibility of 3BC9(chain A)
Figure 28: Predicted binding site of 3BC9(chain A)

The following pictures (Figure 29 – Figure 33) show the top 5 models predicted by iTasser. This five models display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3BC9 the C-score goes from about -2.1 to 0.2. The C-score indicates that the most confident model is the first one (Figure 29) and the worst confident model is model 4 (Figure 32). Outstanding is that model 2 (Figure 30) and 3 (Figure 31) have the same C-score.

Figure 29: First predicted model for 3BC9 (chain A) with an C-score of 0.139
Figure 30: Second predicted model for 3BC9 (chain A) with an C-score of -1.069
Figure 31: Third predicted model for 3BC9 (chain A) with an C-score of -1.069
Figure 32: Fourth predicted model for 3BC9 (chain A) with an C-score of -2.059
Figure 33: Fifth predicted model for 3BC9 (chain A) with an C-score of -1.645

The detailed prediction can be found [here]


3CUI:

The following three pictures show the predicted secondary structure (Figure 34), the predicted solvent accessibility (Figure 35) and the predicted binding (Figure 36) site of 3CUI.
The predicted secondary structure contains 17 alpha-helices and 18 beta-sheets.
The predicted solvent accessibility is displayed by solvent accessibility value that ranges from 0 (buried residue) to 9 (highly exposed residue).
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atoms, red for O-atoms and blue for N-atoms. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3CUI were 9 amino acids predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 2 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid and 1 Alanine.

Figure 34: Predicted secondary structure of 3CUI (chain A)
Figure 35: Predicted solvent accessibility of 3CUI(chain A)
Figure 36: Predicted binding site of 3CUI(chain A)

The following pictures (Figure 37 – Figure 41) show the top 5 models predicted by iTasser. This five model display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3CUI the C-score goes from about -3.4 to 0.4. The C-score indicates that the most confident model is the first one and the worst confident is model 5.

Figure 37: First predicted model for 3CUI (chain A) with an C-score of 0.349
Figure 38: Second predicted model for 3CUI (chain A) with an C-score of 0.076
Figure 39: Third predicted model for 3CUI (chain A) with an C-score of -2.544
Figure 40: Fourth predicted model for 3CUI (chain A) with an C-score of -0.906
Figure 41: Fifth predicted model for 3CUI (chain A) with an C-score of -3.362

The detailed prediction can be found [here]


3LUT:

The following three pictures show the predicted secondary structure (Figure 42) , the predicted solvent accessibility (Figure 43) and the predicted binding (Figure 44) site of 3LUT.
The predicted secondary structure contains 17 alpha-helices and 18 beta-sheets.
The predicted solvent accessibility is displayed by solvent accessibility value that ranges from 0 (buried residue) to 9 (highly exposed residue).
The predicted binding site displays the ligand which binds and the residues that interact with the ligand. The color of the ligand correspond to the CPK colors of Jmol. This means grey stands for a C-atoms, red for O-atoms and blue for N-atoms. The residues were displayed in violet and the first character is the corresponding amino acid whereas the following number delivers the position in the sequence. For 3CUI were 9 amino acids predicted to be involved in the binding site. This ones are 1 Histidine, 1 Arginine, 1 Glutamatic acid, 3 Tryptophane, 1 Aspartic acid, 1 Tyrosine and 1 Asparagine.


Figure 42: Predicted secondary structure of 3LUT (chain A)
Figure 43: Predicted solvent accessibility of 3LUT(chain A)
Figure 44: Predicted binding site of 3LUT(chain A)

The following pictures (Figure 45 – Figure 49) show the top 5 models predicted by iTasser. This five model display the best predicted overall 3D-structure. Furthermore, each model has a different C-score. The C-score estimates the quality of the predicted models. A high value of the C-score signifies a model with high confidence and vice-versa. The range of the C-score goes normally from -5 to 2. For 3LUT the C-score goes from about -3.6 to 0.3. The C-score indicates that the most confident model is the first one and the worst confident is model 5.

Figure 45: First predicted model for 3LUT (chain A) with an C-score of 0.268
Figure 46: Second predicted model for 3LUT (chain A) with an C-score of -0.093
Figure 47: Third predicted model for 3LUT (chain A) with an C-score of -0.163
Figure 48: Fourth predicted model for 3LUT (chain A) with an C-score of -3.189
Figure 49: Fifth predicted model for 3LUT (chain A) with an C-score of -3.521

The detailed prediction can be found [here]

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RMSD and TM-Score

The next step after the use of iTasser is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal.
We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD.
So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results.
The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different.
The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.

3BC9

The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore, it contains the structure alignments of the predicted models of both methods (red) and the original hexosaminidase chain A (green) (Figure 50 - 59).
Looking at the RMSD from PyMol, the best models are the first (Figure 50) and the fifth one (Figure 54). The other three model have a very high RMSD. This agrees with the structure alignment of PyMol where these three models align very bad and many structure elements do not align at all.
In contrast, the RMSD of TM-align is in the most cases much better. Furthermore, the fourth model (Figure 58) does not belong to the best ones and is almost similar to the other model 2-5 (Figure 56 – Figure 59). Only model 1 (Figure 55) has an very good RMSD. The TM-score agrees more with the RMSD of PyMol which means the best score is received for model 1 which followed by model 4. The other three models have a worser TM-score. The structure alignment of TM-align agrees with the TM-score and nearly with the RMSD. For all cases the alignments seems to be only a few better than the one with PyMol and not remarkable more structure elements do agree with the original structure of hexosaminidase chain A. Structure three is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. A closer look at the other two structures which have a bad RMSD as well show that they have only a few matches as well.


iTasser Model 1 iTasser Model 2 iTasser Model 3 iTasser Model 4 iTasser Model 5
RMSD (Pymol) 1.118 15.047 19.744 6.309 15.694
RMSD (TM-align) 1.45 5.88 5.77 5.25 5.80
TM Score 0.90210 0.41932 0.39410 0.62190 0.41661
Structural alignment (Pymol)
Figure 50: Superposition by PyMol with the structure predicted by iTasser (Model 1)
Figure 51: Superposition by PyMol with the structure predicted by iTasser (Model 2)
Figure 52: Superposition by PyMol with the structure predicted by iTasser (Model 3)
Figure 53: Superposition by PyMol with the structure predicted by iTasser (Model 4)
Figure 54: Superposition by PyMol with the structure predicted by iTasser (Model 5)
Structural alignment (TM-align)
Figure 55: Superposition by TM-align with the structure predicted by iTasser (Model 1)
Figure 56: Superposition by TM-align with the structure predicted by iTasser (Model 2)
Figure 57: Superposition by TM-align with the structure predicted by iTasser (Model 3)
Figure 58: Superposition by TM-align with the structure predicted by iTasser (Model 4)
Figure 59: Superposition by TM-align with the structure predicted by iTasser (Model 5)



3CUI

The following table displays the RMSD and the TM-score from PyMol and TM-aling. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 60 – Figure 69).
Looking at the RMSD from PyMol, the best models are the first two ones (Figure 60, Figure 61). The other three model have a higher RMSD whereas model 5 (Figure 64) has the worst one. This agrees with the structure alignment of PyMol where the first two model display a good alignment and the other alignment become more and more worse. The last one has the fewest matches which corresponds to the high RMSD.
In contrast, the RMSD of TM-align has a different order. Here, the second model (Figure 66) has the lowest RMSD followed by the first model (Figure 65). Furthermore the fifth model (Figure 69) is the worst one, but it is not so striking higher than the other two. This means that the three last models are almost similar (Figure 67 – Figure 69). The TM-score agrees more with the RMSD of TM-align which means the best score is received for model 2 followed by model 1. The other three models have a worse TM-score. The structure alignment of TM-align agrees with the TM-score and with the RMSD. For each cases the alignments seems to be only a few better than the one with PyMol and not remarkable more structure elements do agree with the original structure of hexosaminidase chain A. Structure five is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. Furthermore it really aligns bad, which corresponds to the RMSD and the TM-score. A closer look at the other two structures of model 3 and 4 which have a bad RMSD as well show that they have only a few matches as well.


iTasser Model 1 iTasser Model 2 iTasser Model 3 iTasser Model 4 iTasser Model 5
RMSD (Pymol) 1.883 1.175 8.615 4.361 16.829
RMSD (TM-align) 2.44 1.64 4.89 4.49 5.66
TM Score 0.85287 0.89462 0.59559 0.69673 0.40966
Structural alignment (Pymol)
Figure 60: Superposition by PyMol with the structure predicted by iTasser (Model 1)
Figure 61: Superposition by PyMol with the structure predicted by iTasser (Model 2)
Figure 62: Superposition by PyMol with the structure predicted by iTasser (Model 3)
Figure 63: Superposition by PyMol with the structure predicted by iTasser (Model 4)
Figure 64: Superposition by PyMol with the structure predicted by iTasser (Model 5)
Structural alignment (TM-align)
Figure 65: Superposition by TM-align with the structure predicted by iTasser (Model 1)
Figure 66: Superposition by TM-align with the structure predicted by iTasser (Model 2)
Figure 67: Superposition by TM-align with the structure predicted by iTasser (Model 3)
Figure 68: Superposition by TM-align with the structure predicted by iTasser (Model 4)
Figure 69: Superposition by TM-align with the structure predicted by iTasser (Model 5)



3LUT

The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 70 – Figure 79).
Looking at the RMSD from PyMol, the best models are the third (Figure 72), the second one (Figure 71) and the first one (Figure 70) with a really low RMSD. The other two model (Figure 73, Figure 74) have a very high RMSD. This agrees with the structure alignment of PyMol where the first three models align very good and many elements match with the one in the structure of hexosaminidase chain A. The other two structure alignment are really bad which agrees with the RMSDs.
In contrast, the RMSD of TM-align has the same order of the models. The best models are the first three (Figure 75 – Figure 77) and the one with the highest RMSD are the last two (Figure 78, Figure 79). Striking is that in contrast to the PyMol RMSD the RMSD of the last two models are not so extremely high which means that the difference of highest RMSD to the lowest is not so large. The TM-score agrees more with both RMSDs which means the best score is received for the first three models. The other three models have a worse TM-score whereas model 5 has a worst TM-score than model 4. The structure alignment of TM-align agrees with the TM-score and the RMSD. The first three models deliver really good structure alignments whereas model 4 and 5 align badly. The structure of model 5 is outstanding, because there is a part which does not match with the hexosaminidase chain A structure at all. A closer look at the last two structures show that this alignments are really worse and that there are almost no matches with the hexosaminidase chain A.


iTasser Model 1 iTasser Model 2 iTasser Model 3 iTasser Model 4 iTasser Model 5
RMSD (PyMol) 1.956 1.128 1.006 10.479 19.258
RMSD (TM-align) 2.35 1.78 1.57 6.03 5.52
TM Score 0.85521 0.89678 0.89900 0.51449 0.38080
Structural alignment (Pymol)
Figure 70: Superposition by Pyol with the structure predicted by iTasser (Model 1)
Figure 71: Superposition by PyMol with the structure predicted by iTasser (Model 2)
Figure 72: Superposition by PyMol with the structure predicted by iTasser (Model 3)
Figure 73: Superposition by PyMol with the structure predicted by iTasser (Model 4)
Figure 74: Superposition by PyMol with the structure predicted by iTasser (Model 5)
Structural alignment (TM-align)
Figure 75: Superposition by TM-align with the structure predicted by iTasser (Model 1)
Figure 76: Superposition by TM-align with the structure predicted by iTasser (Model 2)
Figure 77: Superposition by TM-align with the structure predicted by iTasser (Model 3)
Figure 78: Superposition by TM-align with the structure predicted by iTasser (Model 4)
Figure 79: Superposition by TM-align with the structure predicted by iTasser (Model 5)





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Discussion

First of all, we compared the secondary structure prediction. All three predicted ones have the same number of helices and sheets. At first sight it agrees in the number with the predicted secondary structure from the last task as well. There, the number for alpha-helices was 14-16 and the number of sheets was 15. This show that the secondary structure prediction is very consistent and that it is probably good, because it agrees with the other predictions
The solvent accessibility gives not so much information so we decided not to got too much in detail. The predicted binding site differs in all three models in some residues. First of all, 3BC9 has 10 residues which are involved in the binding site whereas the other two have only 9 residues. The residues itself agree mostly with some exceptions. This indicates that the binding site is mostly consistent for 3BC9, 3CUI and 3LUT with some small differences.
The 3D structure of the 5 best predicted models varies more. This can be best seen in the calculated RMSD and the TM-Score. The best resulting structure is the first model of iTasser which receives a very small RMSD with PyMol and TM-align and a very high TM-score. Further good predictions are model 1 and 2 of 3CUI and model 3 and 4 of 3LUT. The other achieved models display worse RMSDs and TM-Scores. It is very unexpected, that 3BC9 has only one good model, because it has a high sequence identity to the original structure of hexosaminidase chain A.
All in all, iTasser delivers some really good predictions. One big disadvantage is that iTasser takes a lot of time for its calculation and that it only one job can be started from one IP adresse.

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3D-Jigsaw

Calculation of the models

For the 3D-Jigsaw calculation we used the [Webserver].

For this calculation we had to create a PDB-file which contains the models which should be considered. We took for each structure the best five models. To get those five best models we decided to look mainly at the TM-score, because this score has fewer disadvantages. For 3LUT we decided to make an exception and to take not the Swissmodel results which had a good TM-score. The reason is that we took 3HN3 for the Swissmodel calculation and not 3LUT itself.
This means for 3BC9 we took the iTasser model 1, 2, 4, 5 and the Modeller model. For 3CUI we took iTasser model 1-4 and the Swissmodel model. Contrary, for 3LUT we took all iTasser models.

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Results

3D-Jigsaw delivers a wide range of results. First, it returns a secondary structure prediction. Besides, it delivers an energy plot. At last it returns 5 predicted 3D-models. Therefore, a lot of further informations were shown, like the energy and a Ramachandran plot. We decided to mainly display the predicted secondary structure and 3D-structure of the five models.

3BC9

Figure 80 shows the predicted secondary structure. It contains 17 alpha-helices and 17 beta-sheets.

Figure 80: Predicted secondary structure for 3BC9(chain A)

The following pictures (Figure 81 – Figure 85) show the 5 models predicted by 3D-Jigsaw. These five models display the predicted 3D-structure. Here, we can see that model 3 (Figure 83) and 4 (Figure 84) are very similar. Model 2 (Figure 82) agrees mostly with this two models as well. It has only one out-standing difference: the helix at the bottom of the right side which is not existent in the other two. Model 1 (Figure 81) and 5 (Figure 85) are similar to each other, because both have this part at the bottom which the other models do not contain. One difference between this two is that model 1 seems to be more compact than model 5.

Figure 81: First predicted model for 3BC9 (chain A)
Figure 82: Second predicted model for 3BC9 (chain A)
Figure 83: Third predicted model for 3BC9 (chain A)
Figure 84: Fourth predicted model for 3BC9 (chain A)
Figure85: Fifth predicted model for 3BC9 (chain A)

3CUI

Figure 86 shows the predicted secondary structure. It contains 17 alpha-helices and 17 beta-sheets.

Figure 86: Predicted secondary structure for 3CUI(chain A)

The following pictures show the 5 models (Figure 87 – Figure 91) predicted by 3D-Jigsaw. This five model display the predicted 3D-structure. Here, we can see that model 1 (Figure 87) and 2 (Figure 88) are very similar. Furthermore, the other three models (Figure 89 – Figure 91) agree with each other. Model 1 and 2 have more beta-sheets and one end is on the right side. Contrary, model 3, 4 and 5 have only a few beta-sheets and their one end is to the top. Besides, the helices seems to be very consistent in all models.

Figure 87: First predicted model for 3CUI (chain A)
Figure 88: Second predicted model for 3CUI (chain A)
Figure 89: Third predicted model for 3CUI (chain A)
Figure 90: Fourth predicted model for 3CUI (chain A)
Figure 91: Fifth predicted model for 3CUI (chain A)

3LUT

Figure 92 shows the predicted secondary structure. It contains 17 alpha-helices and 17 beta-sheets.

Figure 92: Predicted secondary structure for 3LUT(chain A)

The following pictures show the 5 models (Figure 93 – Figure 97) predicted by 3D-Jigsaw. This five model display the predicted 3D-structure. Here, we can see that all models are very similar. The only small difference that can be seen is that model 5 (Figure 97) seems to be wider than the others.

Figure 93: First predicted model for 3LUT (chain A)
Figure 94: Second predicted model for 3LUT (chain A)
Figure 95 Third predicted model for 3LUT (chain A)
Figure 96: Fourth predicted model for 3LUT (chain A)
Figure 97: Fifth predicted model for 3LUT (chain A)



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RMSD and TM-Score

The next step after the use of Jigsaw is to check the quality of the predicted structure. Therefore, we calculated the RMSD and the TM score. The RMSD (root-mean square deviation) calculates the distance between two aligned residues. A RMSD near to 0 is a very good result, because than there are only less deviation between template and target. But the RMSD score weights the distance between all residue pairs equally. This means, that some very distant residues can arise the RMSD value dramatically, although the overall topology of the two proteins is quite similar. Another problem with the RMSD is, that the length of the two proteins does not receive attention by the calculation. Therefore, long proteins have almost a worse RMSD value in contrast to short ones, even if the topology of both protein pairs is equal. We used the RMSD calculation by PyMol and also by TM-align. The aligned structures where always the original hexosaminidase chain A and the predicted structure. The different RMSD were displayed in the first two rows of the following table. As you can see, there is a big difference between these two different RMSD values. This can be explained by different calculation methods to calculate the RMSD. So first of all, it is important to clarify how these two methods calculate the RMSD. PyMol first does a sequence alignment and then try to align the structures to minimize the RMSD between all aligned residues. TM-align indeed first rotates one structure to the other in an optimal way and in the next step the RSMD between the corresponding residues is calculated. These two approaches are totally different and also lead to different results.

The TM-score additional pays attention to the length of the protein structures. A TM-Score of 1 means that template and target have the same structure, a TM-Score > 0.5 means, both structures have the same fold, whereas a TM-Score < 0.2 means that both structures are totally different. The TM-Score has some problems, as well. The most important one is, that if it is impossible to align any residues between the two structures, the Score will be 1. So keep in mind, if there is a score of 1, look at the picture to see, if the structures are really identical or if the TM-score failed.


3BC9

The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore, it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 98 - Figure 107).
Looking at the RMSD from PyMol, the best model is the first one. The other models have a very similar RMSD which is not extremely higher. This agrees with the structure alignment of PyMol where the first model (Figure 98) seems to align best. Structure alignments of model 3 (Figure 100) and 4 (Figure 101) are similar to the second one (Figure 99). Only model 1 and 5 have different alignments, because both structures have some helices at the bottom.
In contrast, the RMSD of TM-align has a different order. Here, the first (Figure 102) and the fifth (Figure 107) models have the lowest RMSD. The other models (Figure 103 – Figure 106) have a very similar RMSD which is not extremely higher. The TM-score is different to the RMSD. The TM-scores of all models are almost the same and is not that good. The structure alignment of TM-align agrees with the TM-score and nearly with the RMSD. The structure alignments of all models show no high difference in the matching parts. Main differences exist like in the PyMol alignment between model 1 and 5 and other models. Models 1 and 5 have part of the structure at the bottom whereas models 2, 3 and 4 have the part at the top.

3D-Jigsaw Model 1 3D-Jigsaw Model 2 3D-Jigsaw Model 3 3D-Jigsaw Model 4 3D-Jigsaw Model 5
RMSD (PyMol) 2.773 1.790 2.847 2.847 3.252
RMSD (TM-align) 1.60 2.35 2.93 2.93 1.98
TM Score 0.64803 0.64999 0.63853 0.63851 0.64907
Structural alignment (Pymol)
Figure 98: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 1)
Figure 99: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 2)
Figure 100: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 3)
Figure 101: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 4)
Figure 102: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 5)
Structural alignment (TM-align)
Figure 103: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)
Figure 104: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)
Figure 105: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)
Figure 106: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)
Figure 107: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)



3CUI

The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 108 – Figure 117).
Looking at the RMSD from PyMol, the best models are model 3-5 (Figure 110 – Figure 112) which have all a similar RMSD. The other models (Figure 108, Figure 109) have a bit higher RMSD. This agrees with the structure alignment of PyMol where all models are almost equal. The last three models have their one end at the top and the other have this end at the right side. Furthermore, a closer look at the last three models shows that these ones align better in some parts of the structure which explains the different RMSD.
The RMSD of TM-align has a same result. Here, the last three models (Figure 115 – Figure 117) have the lowest RMSD as well. The other models (Figure 113, Figure 114) have a very similar RMSD which is not extremely higher. Furthermore, TM-score is delivers a similar result. The TM-scores of the models 3-5 are highest whereas the TM-score of model 1 and 2 are little lower. The structure alignment of TM-align agrees with the TM-score and the RMSD as well as with the structure alignment of PyMol. It has therefore the same explanations.

3D-Jigsaw Model 1 3D-Jigsaw Model 2 3D-Jigsaw Model 3 3D-Jigsaw Model 4 3D-Jigsaw Model 5
RMSD (PyMol) 3.575 3.628 1.216 1.209 1.218
RMSD (TM-align) 3.48 3.48 2.73 2.74 2.67
TM Score 0.79749 0.79749 0.84098 0.84064 0.84109
Structural alignment (Pymol)
Figure 108: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 1)
Figure 109: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 2)
Figure 110: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 3)
Figure 111: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 4)
Figure 112: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 5)
Structural alignment (TM-align)
Figure 113: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)
Figure 114: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)
Figure 115: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)
Figure 116: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)
Figure 117: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)



3LUT

The following table displays the RMSD and the TM-score from PyMol and TM-align. Furthermore it contains the structure alignments of the predicted model of both methods (red) and the original hexosaminidase chain A (green) (Figure 118 – Figure 127).
Looking at the RMSD from PyMol, the best model is the fourth one (Figure 121). The other models (Figure 118 – Figure 120, Figure 122) have a very similar RMSD which is really low as well. This agrees with the structure alignment of PyMol where all models seems to align very good and look very similar. The only small difference can be seen in the alpha-helix at the right top which has in each model another position which do not differ a lot. The rest of the alignments seems to be very consistent.
In contrast, the RMSD of TM-align has a different order. Here, the first two models (Figure 123, Figure 124) have the lowest RMSD. The other models (Figure 125 – Figure 127) have a very similar RMSD which is not extremely higher. The TM-score is different to the RMSD. The TM-scores of all models are almost the same and is relatively high. The structure alignment of TM-align agrees with the TM-score and the RMSD as well as with the structure alignment of PyMol. It has therefore the same explanations.

3D-Jigsaw Model 1 3D-Jigsaw Model 2 3D-Jigsaw Model 3 3D-Jigsaw Model 4 3D-Jigsaw Model 5
RMSD (PyMol) 1.813 1.813 1.173 1.224 1.170
RMSD (TM-align) 2.45 2.45 2.95 2.90 2.86
TM Score 0.83585 0.83585 0.83302 0.83034 0.83574
Structural alignment (Pymol)
Figure 118: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 1)
Figure 119: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 2)
Figure 120: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 3)
Figure 121: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 4)
Figure 122: Superposition by PyMol with the structure predicted by 3D-Jigsaw (Model 5)
Structural alignment (TM-align)
Figure 123: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 1)
Figure 124: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 2)
Figure 125: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 3)
Figure 126: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 4)
Figure 127: Superposition by TM-align with the structure predicted by 3D-Jigsaw (Model 5)



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Discussion

First of all we look at the secondary structure prediction. Here, we can see that we get every time 17 helices and 17 sheets. Regarding the last task, this agrees with the other predictions, where the number of alpha-helices was 14-16 and the number of sheets was 15. This shows that the secondary structure prediction is very consistent and probably good.
Afterwards we regarded the five predicted models. This were widely similar to each other. Only some small variations exist between them. The most consistent models are returned for 3LUT and the most different models for 3CUI where the number of beta-sheets differs a lot. For 3BC9 the shape differs more for one certain part of structure as well. Comparing the 5 models of 3BC9, 3CUI and 3LUT we can see that they are also very similar.
The RMSD and the TM-score agrees with the above described characteristics. Both RMSDs and the TM-Score is mostly similar. Outstanding is that the TM-scores for the 3BC9 models are the worst ones. The TM-scores of 3CUI and 3LUT is very consistent at a high level. In contrast, the best RMSD-score with TM-align is achieved with the first model for 3BC9. The worst ones are received in model 1 and 2 for 3CUI, but it is not extremely higher than the others. The best RMSD calculated by PyMol have the models 3 and 5 for 3LUT. The worst ones have all models except model 2 for 3BC9 and the first two models for 3CUI.
The structure alignments of PyMol an TM-align are mostly very good looking. This means that many structure elements of the predicted models agree with the original structure of hexosaminidase chain A. This corresponds to the TM-score and the two RMSD which are very consistent in all models as well.
All in all, the predicted structures are very good and agree very well with the original structure.


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Summary and Discussion

The first we can see on the tables above is, that the RMSD score calculated by PyMol is always much higher than the RMSD score calculated by TM-align. Therefore, it is more effective to rotate the structure to each other, than to use sequence and structure alignment. This can be seen by looking at the RMSD score, but also by looking at the pictures, which show the superposed structures. Furthermore, Modeller and Swissmodel both predict the structure mostly worse than iTasser. Both methods always have a very high RMSD and a very low TM-Score. To learn more about the prediction results, we analysed the scores for each template.

  • 3BC9:

3BC9 is the template with the highest sequence identity. Therefore, the predicted results should be very similar to our structure. Unfortunately Swissmodel could not return a result, because the method was not able to align target and template sequence. This is very surprisingly, because an alignment between two very identical sequences should be easy to do. Even if we used the alignment mode in Swissmodel, it was not able to return a prediction. The prediction of Modeller is really bad and also iTasser predicted wrong structures. Only model 1 of iTasser is very similar to the real structure, which can also be seen in the RMSD (near to 0) and the TM-Score (near to 1).
The best result with 3BC9 as target was the iTasser model 1 prediction (Figure 29).

  • 3CUI:

3CUI has a sequence identity of 49.5%, which is not that much, but it should be possible to predict a structure which is almost similar to the real structure. As before, Swissmodell and Modeller predict structures which fit not very well to our real structure. But iTasser predicted two models, which are very similar to our structure. Model 1 and model 2 have very low RMSD values, high TM-Scores and with a look to the pictures it is clear, that target and template structure are really similar.
So again, in this case we got the best result from iTasser. (Figure 35, Figure 36)

  • 3LUT /3HN3:

Swissmodel was not able to predict the structure of our target with 3LUT as template. Therefore, we used 3HN3, which has with 25% a bit more sequence identity than 3LUT (20%). We suggest, that this prediction result is the worst result, because of this low sequence identity. Interestingly, the prediction results of Modeller and Swissmodel are not much worse than their result with 3CUI as template. Furthermore, iTasser predicted three models, which fit very well to our real structure and also has very low RMSD scores and high TM-Scores.
We want to highlight, that this result is not the norm. We aligned the structure of 2GJX:A and 3LUT:A and the TM-Score between these two structures is 0.50014, the RMSD 5.04, which is a very good result regarding that the sequence identity is that low. So in this case we were lucky to get such a good result, but in general, the results by predicting two that much distinct sequences is much worse.
In agreement with the two results from above, iTasser again received the best results (Figure 41 – Figure 44).

In sum, iTasser is the best prediction method from the three used methods. But iTasser also needs a lot of time to predict the sequences and also allows only one sequence per user to predict in the same time. Therefore, if there is enough time, iTasser is the best choice. If there is not that much time, Modeller and Swissmodel can be used. Both methods have approximately the same prediction results. Modeller can only run on the command line, which means Modeller have to be installed on the system. If the user just want to install Modeller, it will take a while, because Modeller sends a license per E-Mail which can take up to one day. Swissmodel is available on the Internet and can be used without any delay. So if the user only want to get an approximate estimation of the structure of the protein and do not have that much time, Swissmodel will be the right choice.

Furthermore, we used 3D-Jigsaw to get a final model, which is build off the five best models for 3BC9, 3CUI and 3LUT. For all these, the predicted structures are very good, with a small RMSD and a high TM-Score. Besides the certain results of 3D-Jigsaw agree widely and show no big differences. Therefore, 3D-Jigsaw is a good tool to get recombined optimized models. One disadvantage is that it took about 1 1/2 days to calculate these models.

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