Difference between revisions of "Homology based structure predictions BCKDHA"

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(SWISS-MODEL)
(Template selection)
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Before we can start working with these hits we have to check wether one of them is a PDB structure for BCKDHA. This is the case for 2bfd_A.<br>
+
Before we can start working with these hits we have to check whether one of them is a PDB structure for BCKDHA. This is the case for 2bfd_A.<br>
 
By looking at our results and the fact that this hit can not be used we only have structures with an identity lower than 40%.
 
By looking at our results and the fact that this hit can not be used we only have structures with an identity lower than 40%.
Since there are just structures available out of this region we decided to take two structures out of it. One with a 39% identity and one with 18% identity so that there is still a variation in the identities.<br>
+
Since there are just structures available from this region we decided to take two structures out of it. One with a 39% identity and one with 18% identity so that there is still a variation in the identities.<br>
 
In the following we worked with '''1qs0_A''' (39%) and with ''' 2o1x_A''' (18%).
 
In the following we worked with '''1qs0_A''' (39%) and with ''' 2o1x_A''' (18%).
   

Revision as of 09:33, 9 August 2011

!!! This task has to be re-done. The template used for the "good" category is a structure of BCKDHA itself, and when running iTasser the self hit was NOT excluded!!!

1.Calculation of models

Template selection

Homology modelling is a technique to determine the secondary structure of a target protein. It is based on an alignment of the target sequence and one or more template sequences with known secondary structures. The target sequence is assigned a secondary structure based on the template structure. The better the alignment, the better the predicted secondary structure for our template. Therefore the template selection is a crucial step in homology modelling.

To find similar structures to BCKDHA we ran HHsearch using the following command:
hhsearch -i query -d database -o output

It found the following 10 hits in the pdb70 database.

No Hit Prob E-value P-value Score SS Cols Query HMM Template HMM Identity
1 2bfd_A 2-oxoisovalerate dehydr 1.0 1 1 791.3 0.0 400 1-400 1-400 (400) 99%
2 1qs0_A 2-oxoisovalerate dehydr 1.0 1 1 571.5 0.0 349 32-382 52-407 (407) 39%
3 1w85_A Pyruvate dehydrogenase 1.0 1 1 530.8 0.0 356 8-382 6-362 (368) 34%
4 1umd_A E1-alpha, 2-OXO acid de 1.0 1 1 521.8 0.0 351 34-386 16-367 (367) 37%
5 2ozl_A PDHE1-A type I, pyruvat 1.0 1 1 482.7 0.0 331 46-380 25-356 (365) 27%
6 3l84_A Transketolase; TKT, str 1.0 1 1 85.4 0.0 133 161-297 113-252 (632) 21%
7 2r8o_A Transketolase 1, TK 1; 1.0 1 1 74.5 0.0 121 161-285 113-245 (669) 33%
8 2o1x_A 1-deoxy-D-xylulose-5-ph 1.0 1 1 74.2 0.0 127 161-287 122-254 (629) 18%
9 1gpu_A Transketolase; transfer 1.0 1 1 74.2 0.0 140 161-302 115-265 (680) 22%
10 3m49_A Transketolase; alpha-be 1.0 1 1 68.8 0.0 121 161-285 139-271 (690) 31%


Before we can start working with these hits we have to check whether one of them is a PDB structure for BCKDHA. This is the case for 2bfd_A.
By looking at our results and the fact that this hit can not be used we only have structures with an identity lower than 40%. Since there are just structures available from this region we decided to take two structures out of it. One with a 39% identity and one with 18% identity so that there is still a variation in the identities.
In the following we worked with 1qs0_A (39%) and with 2o1x_A (18%).

Modeller

MODELLER is used for homology or comparative modelling of protein three-dimensional structures. It calculates a model containing all non-hydrogen atoms. There are also many other tasks provided by MODELLER like de novo modelling of loops in protein structures, optimization of various models of protein structure with respect to a flexibly defined objective function, multiple alignment of protein sequences and/or structures, clustering, searching of sequence databases, comparison of protein structures, etc.[1]

A tutorial is provided on [2] and on [3]

To run modeller with more than one template we use the targets (the percentage values indicate the sequence similarity to the target):

  • 1dtw:A 95%
  • 2bfe:A 94%
  • 2bfb:A 99%
  • 2bfd:A 99%
  • 1gpu:A 22%
  • 2o1x:A 18%
  • 2r8o:A 33%

Protocol Modeller
Protocol Modeller

SWISS-MODEL

SWISS-MODEL

SWISS-MODEL server page


To find protein structure homology models SWISS-MODEL can be used. SWISS-MODEL is a fully automated protein structure homology-modeling server and is accessible via the ExPASy web server, or from the program DeepView (Swiss Pdb-Viewer).
It provides three different modelling modes:

  • Automated Mode
  • Alignment Mode
  • Project Mode

The Automated Mode uses fully automated modelling and can therefore be only used when the template is very similar to the target.<ref>http://swissmodel.expasy.org/?pid=smd03&uid=&token=</ref>
As an Input for the automated mode, only an amino acid sequence (raw or FASTA format) or the Uniprot AC of the target is required. Optional a template PDB id can be given. Swissmodel automatically selects templates from a Blast run which are suitable due to their E-values if no template is given. The Alignment Mode has to be used for the structures with a low identity. Since we only have hits in the region < 40% we used this tool.
Protocol Swissmodel
Protocol Swissmodel


Results

Global Model Quality Estimation

The following analysis show the global quality of the results of SWISS-MODEL and it also compares the models with the two different structures 1qs0 and 2o1x. For these analysis the QMEAN4 global scores and the local scores are used.

QMEAN4 global scores

1qs0
Comparison of the model with non-redundant set of PDB structures; the red x stands for the Z-score of this model
Density plot for QMEAN scores of the reference set where the red line stands for the QMEAN score of this model
Z-score of the individual components of QMEAN
Scoring function term Raw score Z-score
C_beta interaction energy -153.75 0.05
All-atom pairwise energy -9712.58 -0.49
Solvation energy -29.76 -0.69
Torsion angle energy -32.75 -3.30
QMEAN4 score 0.568 -3.28
2o1x
Comparison of the model with non-redundant set of PDB structures; the red x stands for the Z-score of this model
Density plot for QMEAN scores of the reference set where the red line stands for the QMEAN score of this model
Z-score of the individual components of QMEAN
Scoring function term Raw score Z-score
C_beta interaction energy 69.70 -4.21
All-atom pairwise energy 328.76 -4.29
Solvation energy 30.38 -6.23
Torsion angle energy 48.91 -7.04
QMEAN4 score 0.181 -9.89


Local scores

1qs0
Coloring of the 1qs0 modell by residue error
Estimated per-residue in accuracies along the sequence for the 1qs0 model
2o1x
Coloring of the 2o1x modell by residue error
Estimated per-residue in accuracies along the sequence for the 2o1x model
Local Model Quality Estimation: Anolea / QMEAN
1qs0 2o1x
Local Model Quality Estimation with Anolea and QMEAN for the 1qs0 model
Local Model Quality Estimation with Anolea and QMEAN for the 2o1x model

iTasser

2bfd_A
2bfd_A


Prediction for 2bfd

Seq    SSLDDKPQFPGASAEFIDKLEFIQPNVISGIPIYRVMDRQGQIINPSEDPHLPKEKVLKLYKSMTLLNTMDRILYESQRQGRISFYMTNYGEEGTHVGSA 
Pred   ccccccccccccccccccccccccccccccccSSSSSccccccccccccccccHHHHHHHHHHHHHHHHHHHHHHHHHHcccccccccccccHHHHHHHH
Conf   9867789999988665555664786666789768888999988884236898999999999999999999999999996798467658877389999999

Seq    AALDNTDLVFGQYREAGVLMYRDYPLELFMAQCYGNISDLGKGRQMPVHYGCKERHFVTISSPLATQIPQAVGAAYAAKRANANRVVICYFGEGAASEGD
Pred   HHcccccSSScccHHHHHHHHccccHHHHHHHHHccccccccccccccccccccccccccccHHHccHcHHHHHHHHHHHcccccSSSSSSccccccccc
Conf   9769989775570357899837998999999983777788989998673426212872246336336308999999999709998899994577444210

Seq    AHAGFNFAATLECPIIFFCRNNGYAISTPTSEQYRGDGIAARGPGYGIMSIRVDGNDVFAVYNATKEARRRAVAENQPFLIEAMTYRIGHHSTSDDSSAY
Pred   HHHHHHHHHHHcccSSSSSScccSSccccHHHHHccccHHHHcHcccccSSSSccccHHHHHHHHHHHHHHHHcccccSSSSSSSSSccccccccccccc
Conf   9999999999679979999559821467788772698789843106988689769479999999999999998189988999998750686788998667

Seq    RSVDEVNYWDKQDHPISRLRHYLLSQGWWDEEQEKAWRKQSRRKVMEAFEQAERKPKPNPNLLFSDVYQEMPAQLRKQQESLARHLQTYGEHYPLDHFDK
Pred   ccHHHHHHHHHcccHHHHHHHHHHHcccccHHHHHHHHHHHHHHHHHHHHHHHHcccccHHHHHHHHHccccHHHHHHHHHHHHHHHHccccccHHHHcc
Conf   8999999998639869999999998799999999999999999999999999858998999999675318998899999999999996733188555249 

Prediction for 2r8o

Seq    SSLDDKPQFPGASAEFIDKLEFIQPNVISGIPIYRVMDRQGQIINPSEDPHLPKEKVLKLYKSMTLLNTMDRILYESQRQGRISFYMTNYGEEGTHVGSA
Pred   CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCSSSSSCCCCCCCCCCCCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCCCCCCHHHHHHHH
Conf   9867789999988665555664786666789768888999988884236898999999999999999999999999996798467658877389999999

Seq    AALDNTDLVFGQYREAGVLMYRDYPLELFMAQCYGNISDLGKGRQMPVHYGCKERHFVTISSPLATQIPQAVGAAYAAKRANANRVVICYFGEGAASEGD
Pred   HHCCCCCSSSCCCHHHHHHHHCCCCHHHHHHHHHCCCCCCCCCCCCCCCCCCCCCCCCCCCCHHHCCHCHHHHHHHHHHHCCCCCSSSSSSCCCCCCCCC
Conf   9769989775570357899837998999999983777788989998673426212872246336336308999999999709998899994577444210

Seq    AHAGFNFAATLECPIIFFCRNNGYAISTPTSEQYRGDGIAARGPGYGIMSIRVDGNDVFAVYNATKEARRRAVAENQPFLIEAMTYRIGHHSTSDDSSAY
Pred   HHHHHHHHHHHCCCSSSSSSCCCSSCCCCHHHHHCCCCHHHHCHCCCCCSSSSCCCCHHHHHHHHHHHHHHHHCCCCCSSSSSSSSSCCCCCCCCCCCCC
Conf   9999999999679979999559821467788772698789843106988689769479999999999999998189988999998750686788998667

Seq    RSVDEVNYWDKQDHPISRLRHYLLSQGWWDEEQEKAWRKQSRRKVMEAFEQAERKPKPNPNLLFSDVYQEMPAQLRKQQESLARHLQTYGEHYPLDHFDK
Pred   CCHHHHHHHHHCCCHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHHCCCCCHHHHHHHHHCCCCHHHHHHHHHHHHHHHHCCCCCCHHHHCC
Conf   8999999998639869999999998799999999999999999999999999858998999999675318998899999999999996733188555249


Secondary structure elements are shown as H for Alpha helix,S for Beta sheet and c for Coil

Additionally iTasser predicts several different models and presents the top five. To predict these models it uses a lot of templates. iTasser searches the templates itself and also evaluates which one is the best.


3DJigsaw

3DJigsaw is a server which builds protein models based on already predicted models for a specific target. It recombines the models and optimizes them.

We startet Jigsaw for different categories of sequence identity. The first category used models created by modeller, Swissmodel and iTasser for the 2bfd template. The second Jigsaw run recombined models for a template with low sequence identity (2r8o).


high sequence-identity category: The following models were chosen to build a recombined model with 3DJigsaw:

  • modeller model for template 2bfd
  • modeller model for multiple templates
  • swissmodel model for template 2bfd
  • iTasser model 1 for template 2bfd
  • iTasser model 3 for template 2bfd

As the predicted models have quite bad TM-scores (around 0.3), another 3DJigsaw run was startet using the five iTasser models for 2bfd as input. The first run was not evaluated further as the new results are expected to be better. The following models were chosen to build a better recombined model with 3DJigsaw:

  • iTasser model 1 for template 2bfd
  • iTasser model 2 for template 2bfd
  • iTasser model 3 for template 2bfd
  • iTasser model 4 for template 2bfd
  • iTasser model 5 for template 2bfd

low sequence-identity category: The following models were chosen to build recombined models with 3DJigsaw (inferred from models created with templates with low sequence identity):

  • iTasser model 1 for template 2r8o
  • iTasser model 2 for template 2r8o
  • iTasser model 3 for template 2r8o
  • iTasser model 4 for template 2r8o
  • iTasser model 5 for template 2r8o

These models were chosen because of their high TM-score.


Prediction for the high-sequence identity-category

AA:   SSLDDKPQFPGASAEFIDKLEFIQPNVISGIPIYRVMDRQGQIINPSEDPHLPKEKVLKL
Pred: CCCCCCCCCCCCCHHHHHHHCCCCHHHCCCCCEEEEECCCCCCCCCCCCCCCCHHHHHHH
Conf: 987768999799968982200028344168867999989985867653479988999999

AA:   YKSMTLLNTMDRILYESQRQGRISFYMTNYGEEGTHVGSAAALDNTDLVFGQYREAGVLM
Pred: HHHHHHHHHHHHHHHHHHHCCCEEEEECCCCHHHHHHHHHHHCCCCCEEEEECCCHHHHH
Conf: 999999999999999999879869982125848999999973586879997303389999

AA:   YRDYPLELFMAQCYGNISDLGKGRQMPVHYGCKERHFVTISSPLATQIPQAVGAAYAAKR
Pred: HCCCCHHHHHHHHHCCCCCCCCCCCCCCCCCCCCCCEECCCCHHHHHHHHHHHHHHHHHH
Conf: 879998999999707788888999872123446778104620467559999999999996

AA:   ANANRVVICYFGEGAASEGDAHAGFNFAATLECPIIFFCRNNGYAISTPTSEQYRGDGIA
Pred: CCCCCEEEEEECCHHHHCCHHHHHHHHHHHCCCCEEEEEECCCCCCCCCCCCCCCHHHHH
Conf: 599818999986427738849999999986399889999888811246765456806899

AA:   ARGPGYGIMSIRVDGNDVFAVYNATKEARRRAVAENQPFLIEAMTYRIGHHSTSDDSSAY
Pred: HHHHHCCCCEEEECCCCHHHHHHHHHHHHHHHHHCCCCEEEEEEECCCCCCCCCCCCCCC
Conf: 999863995999977189999999999999998539989999953378787899996628

AA:   RSVDEVNYWDKQDHPISRLRHYLLSQGWWDEEQEKAWRKQSRRKVMEAFEQAERKPKPNP
Pred: CCHHHHHHHHHCCCHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHHCCCCCH
Conf: 998999998853997999999999878999899999999999999999999971688898

AA:   NLLFSDVYQEMPAQLRKQQESLARHLQTYGEHYPLDHFDK
Pred: HHHHHHHHHHCCHHHHHHHHHHHHHHHHCCCCCCHHHHCC
Conf: 9999989774898999999999999987775578658709

Prediction for the low-sequence identity-category

AA:   SSLDDKPQFPGASAEFIDKLEFIQPNVISGIPIYRVMDRQGQIINPSEDPHLPKEKVLKL
Pred: CCCCCCCCCCCCCHHHHHHHCCCCHHHCCCCCEEEEECCCCCCCCCCCCCCCCHHHHHHH
Conf: 987768999799968982200028344168867999989985867653479988999999

AA:   YKSMTLLNTMDRILYESQRQGRISFYMTNYGEEGTHVGSAAALDNTDLVFGQYREAGVLM
Pred: HHHHHHHHHHHHHHHHHHHCCCEEEEECCCCHHHHHHHHHHHCCCCCEEEEECCCHHHHH
Conf: 999999999999999999879869982125848999999973586879997303389999

AA:   YRDYPLELFMAQCYGNISDLGKGRQMPVHYGCKERHFVTISSPLATQIPQAVGAAYAAKR
Pred: HCCCCHHHHHHHHHCCCCCCCCCCCCCCCCCCCCCCEECCCCHHHHHHHHHHHHHHHHHH
Conf: 879998999999707788888999872123446778104620467559999999999996

AA:   ANANRVVICYFGEGAASEGDAHAGFNFAATLECPIIFFCRNNGYAISTPTSEQYRGDGIA
Pred: CCCCCEEEEEECCHHHHCCHHHHHHHHHHHCCCCEEEEEECCCCCCCCCCCCCCCHHHHH
Conf: 599818999986427738849999999986399889999888811246765456806899

AA:   ARGPGYGIMSIRVDGNDVFAVYNATKEARRRAVAENQPFLIEAMTYRIGHHSTSDDSSAY
Pred: HHHHHCCCCEEEECCCCHHHHHHHHHHHHHHHHHCCCCEEEEEEECCCCCCCCCCCCCCC
Conf: 999863995999977189999999999999998539989999953378787899996628 

AA:   RSVDEVNYWDKQDHPISRLRHYLLSQGWWDEEQEKAWRKQSRRKVMEAFEQAERKPKPNP
Pred: CCHHHHHHHHHCCCHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHHCCCCCH
Conf: 998999998853997999999999878999899999999999999999999971688898

AA:   NLLFSDVYQEMPAQLRKQQESLARHLQTYGEHYPLDHFDK
Pred: HHHHHHHHHHCCHHHHHHHHHHHHHHHHCCCCCCHHHHCC
Conf: 9999989774898999999999999987775578658709

2.Evaluation of models

General

A detailed description of how the created models were evaluated can be found in the Evaluation Protocol. The following section presents only the modelling and evaluation results.

Two interesting score when comparing two structures for their structural similarity are the RMSD and the TM-Score. These are two measures which are usually used to measure the accuracy of modelling a structure when the native structure is known.

The RMSD is the average distance of all residue pairs in two structures. The C-alpha RMSD is the average distance between aligned alpha-carbons. The smaller the RMSD value, the better the predicted structure. A local error (e.g. misorientation of the tail) will result in a high RMSD value, although the global structure is correct.

As the RMSD is sensitive to the local error, the TM-Score was proposed. The TM-Score weights close matches stronger than distant matches and therefore the local error problem is overcome. A TM-Score <0.5 indicates a model with random structural similarity, wherease 0.5 < TM-score < 1.00 means the two compared structures are in about the same fold and therefore the predicted model has a correct topology.

Modeller

Numeric evaluation

template molpdf DOPE score GA341 score
2R8O 11049.43 -7610.51 0.00000
2BFD 2247.36 -41979.05 1.00000
1DTW, 1GPU, 2BFB, 2BFD, 2BFE, 2O1X, 2R8O 13873.63 -43399.59 1.00000


The DOPE (Discrete Optimized Protein Energy) score is calculated to assess homology models. The lower the value of the DOPE score the better the . This can be also seen in our three models. The first one (2r8o) which has the worst sequence identity has a quite high DOPE score. The model where 2bfd was the template has a very low score which is reasonable since 2bfd had a very high sequence identity. It is interesting that the model which is build with 7 templates has a higher score than the one which is only build with 1bfd. This can be explained by the influence of the templates which have a low sequence identity with 1u5b.

GA341 is calculated to decide wether the result is a good model or not. A model which is quite good has a score near one. When a model has a score lower than 0.6 it is a bad model. This is also reflected by our results. The model with 2r8o as template is not a good model since the sewuence identity was low and also the DOPE score is quite high so it has a GA341 score of 0. This shows that it is a really bad model. The other two models have a GA341 score of one which shows that they are good models.

Comparison to experimental structure

experimental structure model with template RMSD (DaliLite) RMSD (sap) TM-score Superposition
1U5B_A 2BFD_A 1.1 0.442 0.3526
Superimposed structures of 1U5B and the modeller model with template 2bfd
1U5B_A 2R8O_A no value 95.095 0.1749
Superimposed structures of 1U5B and the modeller model with template 2r8o
1U5B_A 1DTW_A, 2BFE_A, 2BFB_A, 2BFD_A, 1GPU_A, 2O1X_A, 2R8O_A 1.4 0.396 0.3596
Superimposed structures of 1U5B and the modeller model with more than one template


C-alpha RMSD is a measure of the average deviation in distance between aligned alpha-carbons. The higher this distance value the worse is the model. The first model using 2r8o as template has no C-alpha RMSD since the programm we used could find enough significant similarities because the structures are to dissimilar. The model build with 2bfd has a C-alpha RMSD score of 1.1. This is a very good score. It is interesting that again the model for 7 template proteins does not have a better score (1.4), although some templates with very high sequence similarity were included. This shows that the templates with low sequence similarity have too much influence on the final model. The model with 2bfd is the best prediction by modeller for our target.


all atom RMSD

position 2bfd 2r8o multi
161 0.478 - 0.238
166 0.186 - 0.146
167 0.184 - 0.149

It was not possible for pymol to calculate the RMSD value for the second model because it was not possible to create a matching alignment. "multi" includes the models with 1DTW_A, 2BFE_A, 2BFB_A, 2BFD_A, 1GPU_A, 2O1X_A, 2R8O_A as template sequences.

improved alignment

The model which was build with 2r8o was so bad that it was not possible for DaliLite to predict a C-alpha RMSD. So we had to improve it. For this improvement we load the alignment of 1u5b and 2r8o in Jalview <ref>http://www.jalview.org/download.html</ref> to compare the two sequences. To find more equal residues in both sequences we deleted some gaps and checked the Consensus-line to find the amino acids which are in both sequences. With this handmade alignment we repeated the MODELLER-run. To evaluate the resulting model we calculated the C-alpha RMSD and the TMscore.

template C-alpha score TMscore Superposition
2r8o 3.1 0.1740
Superimposed structures of 1U5B and the modeller model with the improved alignment for template 2r8o

As we can see the improvement of the alignment was successful since the model has a much better C-alpha score. In comparison to the C-alpha scores of the other modeller results, this model with the smallest sequence identity still performs worst. The TM-score also gets a little bit smaller compared to the unimproved alignment, indicating that the overall model did not improve.

Swissmodel

Numeric evaluation

QMEAN4 global scores

QMEANscore4

2bfd_A 2r8o_A
0.67 0.203


QMEANscore4 is calculated to compare whole models. The score ranges between 0 and 1. The higher the value the better is the quality of the model. By comparing the scores of the model with 2bfd as target and 2r8o as target it iat obvious that the first one os the better one since it has a much higher QMEANscore4.


QMEAN Z-Score

2bfd_A 2r8o_A
-1.604 -9.522
Z-Score plot1 2bfd_A
Z-Score plot1 2r8o_A
Z-Score plot2 2bfd_A
Z-Score plot2 2r8o_A


The QMEAN Z-Score represents the absolute quality of a model. Models with a low quality have a strongly negative QMEAN Z-scores. The 2bfd-model has a less negative score than the 2r8o-model which schos again that this model has a better quality.



Score components

2bfd_A 2r8o_A
score components 2bfd_A
score components 2r8o_A


Local scores

2bfd_A 2r8o_A
Coloring by residue error 2bfd_A
Coloring by residue error 2r8o_A
Residue error plot 2bfd_A
Residue error plot 2r8o_A


With the coloring by residue error the inaccuracy of each residue is esitmated . The results are visualised using a color gradient where blue means that assured region and red means that this region is unreliable. In the model of 2bfd there are many blue alpha helices which means that they are right and only a few red coils. Since blue is the dominant color this shows that the model is mainly right. In contrast the other model has a lot of red and orange alpha helices and coils and nearly no blue region. This reflects the bad quality of this model.

The residue error plot shows the predicted error (y-axis) per residue (x-axis). The highest error score of the 2bfd-model is 12 and the average is about 3 whereas the highest peak score of the 2r8o-model is 15 and the average is about 5. Again it can be seen that the 2bfd-model is the better one.


Global scores: QMEAN4:

2bfd_A 2r8o_A
Scoring function term Raw score Z-score Raw score Z-score
C_beta interaction energy -162.66 0.54 74.97 -4.18
All-atom pairwise energy -10811.93 0.35 2113.21 -5.03
Solvation energy -27.04 -1.02 26.87 -5.92
Torsion angle energy -75.78 -1.45 36.84 -6.47
QMEAN4 score 0.670 -1.60 0.203 -9.52


Local Model Quality Estimation

2bfd_A 2r8o_A
Local Model Quality Estimation 2bfd_A
Local Model Quality Estimation 2r8o_A


For the local model quality estimation we chose the ANOLEA potential. This program performs energy calculations on a protein chain. On the y-axis the energyof each amino acid is represented. Negative energy values (in green) represent favourable energy environment whereas positive values (in red) unfavourable energy environment for a given amino acid. The result of the comparison of this estimation between the 2bfd-model and the 2r8o-model is quite clear since nearly the whole left plot is green and nearly the whole right plot is red. These two plots show that the 2bfd-model is much better than the other one.

Comparison to experimental structure

experimental structure model with template RMSD (DaliLite) RMSD (sap) TMscore Superposition
1U5B_A 2BFD_A 1.1 0.288 0.1640
Superposition of the Swissmodel model using template 2bfd and target 1U5B
1U5B_A 2R8O_A 3.1 2.110 0.1639
Superposition of the Swissmodel model using template 2r8o and target 1U5B

C-alpha RMSD is a measure of the average deviation in distance between aligned alpha-carbons. The higher this distance value the worse is the model. The 2bfd-model has a score of 1.1 and the 2r8o-model has a score of 3.1. This comparison shows clearly that the first model is mcuh better than the second one.

all atom RMSD

position 2bfd 2r8o
161 0.165 1.075
166 0.126 2.585
167 0.127 2.043


improved alignment

experimental structure model with template C-alpha RMSD TMscore Superposition
1U5B_A 2R8O_A 0.1592
Superposition of the Swissmodel model using the improved alignment for template 2bfd and target 1U5B

iTasser

Numeric evaluation

C-score

2bfd
model1 model2 model3 model4 model5
1.999 -3.781 -4.970 -4.970 -3.781

The C-score is a measure for the quality of predicted models by I-TASSER. C-score ranges between [-5,2], where a C-score of higher value signifies a model with a high confidence.

Comparison to experimental structure

2bfd 2r8o
No TMscore RMSD (DaliLite) RMSD (sap) Superposition TMscore RMSD (DaliLite) RMSD (sap) Superposition
1 0.9709 0.49 0.312
iTasser model 1 for template 2bfd superimposed on target 1U5B
0.5190 3.4 3.377
iTasser model 1 for template 2r8o superimposed on target 1U5B
2 0.8609 1.44 0.354
iTasser model 2 for template 2bfd superimposed on target 1U5B
0.4979 3.2 3.935
iTasser model 2 for template 2r8o superimposed on target 1U5B
3 0.8597 1.43 0.478
iTasser model 3 for template 2bfd superimposed on target 1U5B
0.4871 3.0 3.476
iTasser model 3 for template 2r8o superimposed on target 1U5B
4 0.8549 1.71 0.493
iTasser model 4 for template 2bfd superimposed on target 1U5B
0.5354 4.8 2.449
iTasser model 4 for template 2r8o superimposed on target 1U5B
5 0.8251 1.73 0.348
iTasser model 5 for template 2bfd superimposed on target 1U5B
0.5107 6.0 2.540
iTasser model 5 for template 2r8o superimposed on target 1U5B


To calculate the RMSD of the 6A radius of the catalytic center we had to find the catalytic center first. There are three catalytic center on the positions 161, 166 and 167. We calculated the RMSD for all of them.


2bfd 2r8o
model 161 166 167 161 166 167
1 0.269 0.251 0.191 5.940 1.070 0.952
2 0.349 0.348 0.271 0.676 1.141 1.142
3 0.480 0.467 0.330 1.527 1.252 1.310
4 0.440 0.507 0.430 1.748 1.224 1.074
5 0.299 0.291 0.269 1.315 1.053 1.180


All of these models are very good which is shown by the table since they have all a high TMscore and a low C-alpha RMSD score. But this is clear because they are the top 5 hits of iTasser. Perhaps the first model is a bit better than the other 4. This can be expected since the Scores are a bit better than of the other 4 models.

3DJigsaw

Numeric evaluation

energy plot
energy plot
high sequence identity low sequence identity
No Energy Coverage Ramachandran Plot Energy Coverage Ramachandran Plot
1 -504.14 0.98
Ramachandran Plot for model 1 predicted by 3DJigsaw
-442.89 1.0
Ramachandran Plot for model 1 predicted by 3DJigsaw
2 -503.04 0.98
Ramachandran Plot for model 1 predicted by 3DJigsaw
-442.26 1.0
Ramachandran Plot for model 1 predicted by 3DJigsaw
3 -502.83 0.98
Ramachandran Plot for model 1 predicted by 3DJigsaw
-441.89 1.0
Ramachandran Plot for model 1 predicted by 3DJigsaw
4 -502.16 0.98
Ramachandran Plot for model 1 predicted by 3DJigsaw
-441.76 1.0
Ramachandran Plot for model 1 predicted by 3DJigsaw
5 -501.32 0.98
Ramachandran Plot for model 1 predicted by 3DJigsaw
-441.62 1.0
Ramachandran Plot for model 1 predicted by 3DJigsaw

Comparison

high sequence-identity category low sequence-identity category:
Model RMSD (DaliLite) RMSD (sap) TM-score Superposition RMSD (DaliLite) RMSD (sap) TM-score Superposition
1 0.6 0.347 0.9887
3DJigsaw recombined model 1 from models with high sequence identity superimposed on target 1U5B
3.3 3.862 0.5031
3DJigsaw recombined model 1 from models with low sequence identity superimposed on target 1U5B
2 0.6 0.347 0.9887
3DJigsaw recombined model 2 from models with high sequence identity superimposed on target 1U5B
3.9 3.910 0.5028
3DJigsaw recombined model 2 from models with low sequence identity superimposed on target 1U5B
3 1.3 0.439 0.9712
3DJigsaw recombined model 3 from models with high sequence identity superimposed on target 1U5B
3.8 3.932 0.5029
3DJigsaw recombined model 3 from models with low sequence identity superimposed on target 1U5B
4 1.5 0.998 0.9629
3DJigsaw recombined model 4 from models with high sequence identity superimposed on target 1U5B
3.3 3.902 0.4982
3DJigsaw recombined model 4 from models with low sequence identity superimposed on target 1U5B
5 1.6 0.993 0.9617
3DJigsaw recombined model 5 from models with high sequence identity superimposed on target 1U5B
3.8 3.968 0.5031
3DJigsaw recombined model 5 from models with low sequence identity superimposed on target 1U5B

As expected the 3DJigsaw prediction based on models for a template with high sequence identity to our target is very good. The RMSD values are very low and the TM-scores are all close to 1.0. The predicted models for a template with high sequence identity are therefore good models which could be used to assign a structure to our target.

The models created by 3DJigsaw based on the iTasser models for the template with little sequence identity are also an improvement compared to the iTasser models which were used as input.

So in our case 3DJigsaw used the given information and improved the previously predicted programs.

Comparison of the methods

Numerical Evaluation

The following tables again list the RMSD and TM-score values, which were computed before, to provide an overview of the performance of the different methods.

modeller

2BFD_A 2R8O_A Multi
C-alpha RMSD TMscore C-alpha RMSD TMscore C-alpha RMSD TMscore
1.1 0.3526 3.1 0.1749 1.4 0.3596

Swissmodel

2BFD_A 2R8O_A
C-alpha RMSD TMscore C-alpha RMSD TMscore
1.1 0.1640 3.1 0.1639

iTasser

2bfd 2r8o
model1 model2 model3 model4 model5 model1 model2 model3 model4 model5
RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore
0.49 0.9709 1.44 0.8609 1.43 0.8597 1.71 0.8549 1.73 0.8251 3.4 0.5190 3.2 0.4979 3.0 0.4871 4.8 0.5354 6.0 0.5107

3DJigsaw

models for template 2bfd models for template 2r8o
model1 model2 model3 model4 model5 model1 model2 model3 model4 model5
RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore RMSD TMscore
0.347 0.9887 0.347 0.9887 0.439 0.9712 0.998 0.9629 0.993 0.9617 3.862 0.5031 3.910 0.5028 3.932 0.5029 3.902 4.982 3.968 0.5031

Discussion

To compare the predicted models and the real crystallized structure of our template different scores (RMSD, TM-score) were calculated. Based on these scores iTasser computed the best ab initio models for our template. Especially the TM-score is much higher for all of the iTasser models compared to the modeller and Swissmodel predictions.

The similarity of the template is a limiting factor for the model prediction. In our case, the best model for a sequence with only 33% sequence similarity to the target had an RMSD value of 3.4 and an TM-score of 0.5190 (iTasser model1).

References

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