Gaucher Disease: Task 05 - Homology Modelling
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This page is still under construction.
Contents
Calculation of models
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
In the following we present the results of the created models. We compare the models to two reference structures - 1OGS_A and 2V3E_B - by superimposing and calculating C_alpha RMSD by Pymol as well as all atom RMSD, GDT-TS-score and TM-scoreby TMscore tool. In addition, we compare main scores given by the modelling programs.
Modeller
<figtable id="single_templates">
| High sequence identity | ||||
|---|---|---|---|---|
| Template | 2XWD_A | |||
| Alignment method | malign | salign | ||
| DOPE score | -64821.74609 | -64821.746094 | ||
| GA341 score | 1.00000 | 1.00000 | ||
| Reference targets | 1OGS_A | 2V3E_B | 1OGS_A | 2V3E_B |
| C_alpha RMSD (# atoms pairs) | 0.284 (408) | 0.194 (444) | 0.284 (408) | 0.194 (444) |
| RMSD (of 497 common residues) | 23.987 | 24.032 | 23.987 | 24.032 |
| TM-score | 0.2160 | 0.2167 | 0.2160 | 0.2167 |
| GDT-TS-score (%) | 0.0664 | 0.0669 | 0.0664 | 0.0669 |
| Pymol visualization | ||||
| Low sequence identity | ||||
| Template | 2WNW_A | |||
| Alignment method | malign | salign | ||
| DOPE score | -57316.70703 | -53548.45703 | ||
| GA341 score | 1.00000 | 0.99999 | ||
| Reference targets | 1OGS_A | 2V3E_B | 1OGS_A | 2V3E_B |
| C_alpha RMSD (# atoms pairs) | 1.217 (340) | 1.116 (336) | 1.523 (337) | 1.523 (337) |
| RMSD (of 497 common residues) | 22.979 | 22.980 | 22.972 | 22.949 |
| TM-score | 0.2301 | 0.2307 | 0.2257 | 0.2249 |
| GDT-TS-score (%) | 0.0659 | 0.0664 | 0.0634 | 0.0649 |
| Pymol visualization | ||||
</figtable>
<figtable id="multiple_templates">
| High sequence identity | ||
|---|---|---|
| Templates | 3KE0_A, 2XWD_A, 2WKL_A, 2NSX_A | |
| DOPE score (%) | NA | |
| GA341 score | NA | |
| Reference targets | 1OGS_A | 2V3E_B |
| C_alpha RMSD (# atoms pairs) | 0.272 (439) | 0.337 (413) |
| RMSD (of 497 common residues) | 24.370 | 24.402 |
| TM-score | 0.2173 | 0.2175 |
| GDT-TS-score (%) | 0.0664 | 0.0674 |
| Pymol visualization | ||
| Low sequence identity | ||
| Templates | 2WNW_A, 1VFF_A, 3II1_A | |
| DOPE score (%) | NA | |
| GA341 score | NA | |
| Reference targets | 1OGS_A | 2V3E_B |
| C_alpha RMSD (# atoms pairs) | 20.935 (471) | 21.012 (473) |
| RMSD (of 497 common residues) | 22.759 | 22.875 |
| TM-score | 0.1931 | 0.1924 |
| GDT-TS-score | 0.0508 | 0.0523 |
| Pymol visualization | ||
| Mixed sequence identity | ||
| Templates | 2XWD_A, 2WNW_A | |
| DOPE score (%) | ||
| GA341 score | ||
| Reference targets | 1OGS_A | 2V3E_B |
| C_alpha RMSD (# atoms pairs) | 0.328 (418) | |
| RMSD (of 497 common residues) | ||
| TM-score | ||
| GDT-TS-score | ||
| Pymol visualization | ||
</figtable>
- Pymol alignment of models of P04062 computed by Modeller (lime) with the original structure 1OGS, chain A (purple). For the templates, chains A were used.
Model using templates with high sequence identity: 3KE0, 2XWD, 2WKL and 2NSX and salign alignment method. RMSD=0.272 (439 C_alpha atoms pairs).
Model using templates with low sequence identity: 2WNW, 1VFF and 3II1 and salign alignment method. RMSD=20.935 (471 C_alpha atoms pairs).
Model using templates with mixed sequence identity: high 2XWD and low 2WNW and salign alignment method. RMSD=0.328 (418 C_alpha atoms pairs).
Swiss-Model
- Pymol alignment of models of P04062 computed by Swiss-Model (lime) with the original structure 1OGS, chain A (purple). For the templates, chains A were used.
Model using template 2XWD. RMSD=0.305 (407 C_alpha atoms pairs).
Model using template 2WNW. RMSD=1.105 (368 C_alpha atoms pairs).
iTasser
- Pymol alignment of models of P04062 computed by iTasser (lime) with the original structure 1OGS, chain A (purple). For the templates, chains A were used.
The best model (Model1) using template 2XWD. C-score=-0.33. Estimated accuracy: 0.67±0.13 (TM-score), 8.2±4.5Å (RMSD). Pymol: RMSD=0.692 (461 C_alpha atom pairs).
The best model (Model1) using template 2WNW. C-score=-0.40. Estimated accuracy: 0.66±0.13 (TM-score), 8.4±4.5Å (RMSD). Pymol: RMSD=1.232 (431 C_alpha atom pairs).
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
In the following table C_alpha RMSD computed by Pymol with 1OGS_A as reference are summarized in the following table.
<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) | |||
</figtable>
TODO:
- Select one apo and one complex structure if there are several experimental structures -> document your choice of reference
- 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).
