Difference between revisions of "Gaucher Disease: Task 05 - Homology Modelling"

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| <!--[[File:.png|center|thumb|300px| Visualization of the reference target structure 1OGS (green) and the Modeller model created from high sequence identity 2XWD and low sequence identity 2WNW (purple).]]--> || <!--[[File:.png|center|thumb|300px| Visualization of the reference target structure 2V3E_B (limegreen) and the Modeller model created from high sequence identity 2XWD and low sequence identity 2WNW (purple).]]-->
 
| <!--[[File:.png|center|thumb|300px| Visualization of the reference target structure 1OGS (green) and the Modeller model created from high sequence identity 2XWD and low sequence identity 2WNW (purple).]]--> || <!--[[File:.png|center|thumb|300px| Visualization of the reference target structure 2V3E_B (limegreen) and the Modeller model created from high sequence identity 2XWD and low sequence identity 2WNW (purple).]]-->
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<center><small>'''<caption>''' Modeller results of the modeling with multiple templates and comparison with two reference template structures. The "salign" method was used (alignment using 2D information).</caption></small></center>
 
<center><small>'''<caption>''' Modeller results of the modeling with multiple templates and comparison with two reference template structures. The "salign" method was used (alignment using 2D information).</caption></small></center>

Revision as of 15:02, 2 September 2013

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This page is still under construction.

Calculation of models

Lab journal

We created two single target models of our protein sequence, P04062: one with a high sequence identity target, 2XWD_A, and one with a low sequence identity target, 2WNW_A, with each one of the three tools: Modeller, Swiss-Model and iTasser.

For Modeller we additionally executed multiple target modeling mode. In the multiple target modeling mode, Modeller first aligns the user selected templates, then adds the target to the MSA, which is finally used for modeling. We tried the following template combinations:

  • close homologues (> 60% sequence identity): all four (3KE0_A, 2XWD_A, 2WKL_A and 2NSX_A)
  • distant homologues (< 30% sequence identity): all three (2WNW_A, 1VFF_A and 3II1_A)
  • close and distant homologues: 2XWD_A and 2WNW_A

Evaluation of models

Scores given by the modelling programs

TODO:

  • Check the numeric evaluation of your models (scores given by the modelling programs)

Pymol visualization and RMSD calculation

Modeller

TODO: figtable single_templates

<figtable id="multiple_templates">

High sequence identity
Templates 3KE0, 2XWD, 2WKL
Reference targets 1OGS 2V3E_B
C_alpha RMSD (# superimposed residues) blub blub
RMSD of common residues (# common residues)
TM-score
GDT-TS-score
DOPE score
Pymol visualization
Low sequence identity
Templates 2WNW, 1VFF, 3II1
Reference targets 1OGS 2V3E_B
C_alpha RMSD (# superimposed residues)
RMSD of common residues (# common residues)
TM-score
GDT-TS-score
DOPE score
Pymol visualization
Mixed sequence identity
Templates 2XWD, 2WNW
Reference targets 1OGS 2V3E_B
C_alpha RMSD (# superimposed residues)
RMSD of common residues (# common residues)
TM-score
GDT-TS-score
DOPE score
Pymol visualization
Modeller results of the modeling with multiple templates and comparison with two reference template structures. The "salign" method was used (alignment using 2D information).

</figtable>

Swiss-Model

iTasser

iTasser scores are explained on the results page:

C-score is a confidence score for estimating the quality of predicted models by I-TASSER. It is calculated based on the significance of threading template alignments and the convergence parameters of the structure assembly simulations. C-score is typically in the range of [-5,2], where a C-score of higher value signifies a model with a high confidence and vice-versa.

TM-score and RMSD are known standards for measuring structural similarity between two structures which are usually used to measure the accuracy of structure modeling when the native structure is known. In case where the native structure is not known, it becomes necessary to predict the quality of the modeling prediction, i.e. what is the distance between the predicted model and the native structures? To answer this question, we tried predicted the TM-score and RMSD of the predicted models relative the native structures based on the C-score.

TM-score is a recently proposed scale for measuring the structural similarity between two structures (see Zhang and Skolnick, Scoring function for automated assessment of protein structure template quality, Proteins, 2004 57: 702-710). The purpose of proposing TM-score is to solve the problem of RMSD which is sensitive to the local error. Because RMSD is an average distance of all residue pairs in two structures, a local error (e.g. a misorientation of the tail) will araise a big RMSD value although the global topology is correct. In TM-score, however, the small distance is weighted stronger than the big distance which makes the score insensitive to the local modeling error. A TM-score >0.5 indicates a model of correct topology and a TM-score<0.17 means a random similarity. These cutoff does not depends on the protein length.

C_alpha RMSD summary

<figtable id="models_RMSD">

Program Templates
High PIDE (2XWD) low PIDE (2WNW) high PIDE (4 temp.) low PIDE (3 temp.) mixed PIDE (2XWD & 2WNW)
Modeller 0.284 (408) 1.523 (337) 0.272 (439) 20.935 (471) 0.328 (418)
Swiss-Model 0.305 (407) 1.105 (368) - - -
iTasser 0.692 (461) 1.232 (431) - - -
Pymol C_alpha RMSD of alignments between 1OGS_A and the models of P06042 created with the different programs and templates. The number of atoms considered in the calculation of the RMSD is given in brackets.

</figtable>

TODO:

  • Compare the models to the experimental structure (Select one apo and one complex structure if there are several experimental structures, document your choice of reference)
    • Look at your models! -> Pymol, 1OGS
    • Calculate the GDT scores of the models.
    • Calculate the C_alpha RMSD of the models (use /mnt/project/pracstrucfunc13/bin/sap). Hint: You can force pymol to only calculate the RMSD using all C-alpha atoms by calculating the RMSD between two selections: [1] -> Pymol, 1OGS
    • Extra diligence task: define a radius of 6 Angstrom around the catalytic centre / binding site and calculate the all atom RMSD in that region

Discussion

TODO:

  • Discuss your results (You do not need to calculate correlation coefficients, a qualitative estimation is enough.):
    • How do the RMSD and GDT correlate? Is one score more helpful in finding meaningful models?
    • Do you see any correlation between the quality scores provided by the modelling tools and the RMSD/GDT?
    • Is any method systematically better at predicting the structure?
    • Does this depend on the similarity of the template?
    • Can you imagine any other kind of information that might improve the models?
    • For Modeller: How does including more templates change the model quality?

Sources

Modeller:

1) N. Eswar, M. A. Marti-Renom, B. Webb, M. S. Madhusudhan, D. Eramian, M. Shen, U. Pieper, A. Sali. Comparative Protein Structure Modeling With MODELLER. Current Protocols in Bioinformatics, John Wiley & Sons, Inc., Supplement 15, 5.6.1-5.6.30, 2006.

2) M.A. Marti-Renom, A. Stuart, A. Fiser, R. Sánchez, F. Melo, A. Sali. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29, 291-325, 2000.

3) A. Sali & T.L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

4) A. Fiser, R.K. Do, & A. Sali. Modeling of loops in protein structures, Protein Science 9. 1753-1773, 2000.

Swiss-Model:

1) Arnold K., Bordoli L., Kopp J., and Schwede T. (2006). The SWISS-MODEL Workspace: A web-based environment for protein structure homology modeling. Bioinformatics, 22,195-201.

2) Schwede T, Kopp J, Guex N, and Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Research 31: 3381-3385.

3) Guex, N. and Peitsch, M. C. (1997) SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 18: 2714-2723.

iTasser:

1) Ambrish Roy, Alper Kucukural, Yang Zhang. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols, vol 5, 725-738 (2010).

2) Yang Zhang. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 9:40 (2008).

3) Ambrish Roy, Jianyi Yang, Yang Zhang. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Research, vol 40, W471-W477 (2012).