Difference between revisions of "Homology-modelling HEXA"
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Furthermore, Swiss-Modeler returns two differenz 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, Swiss-Modeler returns two differenz 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: |
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| − | Besides, it creates some pictures, which show the qualitity of the model. |
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Revision as of 12:29, 30 August 2011
Homology structure groups
We choosed one protein from each sequence identity group which is shown in the following table
(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% | |
Swissmodel
General Information
To calculate the models with Swiss-Model 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].
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]
Swiss-Model also give same scores to give the user the possibility to estimate the quality of the predicted model, which are showed in the next paragraphes.
The most important score in the following table is the QMEAN4 score, because this score consists of the scores above and give the user the possibility to compare 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, Swiss-Modeler returns two differenz 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:
Besides, it creates some pictures, which show the qualitity of the model. This is shown in the following figures:
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.
3HN3:
The detailed prediction can be found [here]
Swiss-Model also give same scores to give the user the possibility to estimate the quality of the predicted model, which are showed in the next paragraphes.
The most important score in the following table is the QMEAN4 score, because this score consists of the scores above and give the user the possibility to compare 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 |
Swiss-Modeler also returns some pictures, which show the qualitity of the model.
Predicted Structure:
Model qualitity:
RMSD and TM-Score
| 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 | ||
| Structural alignment (TM-align) | no result |
Modeller
Calculation of the models
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]
Results
Modeller calculated for each structure (3BC9, 3CUI and 3LUT) one model which can be seen in the next pictures:
| 3BC9 | 3CUI | 3LUT |
RMSD and TM-Score
| 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) | |||
| Structural alignment (TM-align) |
iTasser
Calculation of the models
To calculate our models with iTasser we used the [Webserver]. We defined the target and template sequence, but this time without an alignment. We used the same template sequences as before.
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.
3BC9:
The following picture shows the predicted secondary structure of 3BC9.
The following picture shows the predicted solvent accessibility of 3BC9.
The following pictures show the top 5 models predicted by iTasser. They have different c-values.
The following picture shows the predicted binding site of 3BC9.
The detailed prediction can be found [here]
3CUI:
The following picture shows the predicted secondary structure of 3CUI.
The following picture shows the predicted solvent accessibility of 3CUI.
The following pictures show the top 5 models predicted by iTasser. They have different c-values.
The following picture shows the predicted binding site of 3CUI.
The detailed prediction can be found [here]
3LUT:
The following picture shows the predicted secondary structure of 3LUT.
The following picture shows the predicted solvent accessibility of 3LUT.
The following pictures show the top 5 models predicted by iTasser. They have different c-values.
The following picture shows the predicted binding site of 3LUT.
The detailed prediction can be found [here]
RMSD and TM-Score
3BC9
| 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) | |||||
| Structural alignment (TM-align) |
3CUI
| 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) | |||||
| Structural alignment (TM-align) |
3LUT
| 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) | |||||
| Structural alignment (TM-align) |
3D-Jigsaw
Calculation of the models
Results
3BC9
3CUI
3LUT
RMSD and TM-Score
3BC9
| 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) | |||||
| Structural alignment (TM-align) |
3CUI
| 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) | |||||
| Structural alignment (TM-align) |
3LUT
| 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) | |||||
| Structural alignment (TM-align) |
Conclusion
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 temaplte 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.
We used the RMSD calculation by Pymol and also by TM-align.
As you can see in the tables above, there is a big difference between these two RMSD values. This can be explained by different calculation methods to caluclate the RMSD. So first of all, it is important to clariy 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 rotate 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.
As describe before, the RMSD value has some problems. Therefore, we also calculated the TM-score, which receive 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 also some problems. The most important problem is, that if it is not possible 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 identically or if the TM-score failed.
Comparison of the different methods
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.
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.
- 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 model1 prediction.
- 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.
So again, in this case we got the best result from iTasser.
- 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. 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.
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 gave the best results.
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.
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. 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.
